Why risk controls as I mentioned you guys have already hopefully read the case studies, and then you've got an activity on those Because of that we have to put risk measures in place energy commodity trading in all its forms basically the SEC and the CFTC came in at one point and said that Publicly traded energy companies will in fact have to institute some type of risk control program We'll talk about the types of controls and then last we're gonna talk about some recommendations if you were to sit down with a company and Make some recommendations for a risk program for them I've got a list of things that you might you know want to be able to say to them or recommend to them And we talked about these case studies already. I added a fourth one down there. You can see his latest 2006 There was another trading company Amaranth and they lost six billion dollars trading nine next futures again, none of oversight Common issues throughout all of these. I think hopefully you've picked up on that in the case studies They're really the single or multiple rogue traders and we say rogue traders We're talking about people who did things on their own. They made decisions on their own They were dealing in risky derivatives. These are not people dealing simply in The underlying derivatives in some case. We know they were doing option swaps Exotic options and some other things a little or no accountability. This is the big one, okay? There's there's really no line of accountability where there's oversight In several of these cases in fact in the three case studies that you had there was a total control over the paper trail as we saw in the Case study with the Gleason he actually controlled all of the accounting So he could controlled the execution through settlement and then had his phony account set up And this is one of the issues this next point the lack of understanding and recognition by the Executives of financial derivative trading and the risks involved To this day, I still believe there are executives over companies where Energy commodity trading exists financial derivatives are used where the executives truly Don't understand the risks that the company is taking on with the various forms of Transactions even if they're presented with a daily report from your risk control group. I don't know that they fully Understand and can interpret those reports properly So some risk measures these are standard risk measures one of the most common ones and one that Hopefully is most well-known is mark to market. That's the value of the portfolio at the close of the day Based on the settlement prices now in fact sim the simulator If you you know watched your position every day if you had open positions The simulator value those based on the prices at the end of the day And so that is the mark to market you are taking all of your open positions because you have not yet Close them and it's marking them against the settlement prices for the markets closing that day and putting a value on it Then we have value at risk. And this is a much more complicated risk measure. I don't expect you to Understand it in its entirety But it's what's known as the theoretical maximum loss on a total book for a given period of time at a given Confidence level at the fine holding period at expected market conditions now. I realize that's quite a mouthful There's a single number that comes out What happens is there is a first step in calculating value at risk the mark to market Calculation is run on the entire book. So you'll have a mark to market number and then What'll happen is that will get compared to historical prices, but then also within the value at risk system and again, this is a Calculation that's done by software. It's not a hand calculation There will be a Monte Carlo simulator and a Monte Carlo simulator is really a random number generator so in essence the Monte Carlo simulator will come up with literally thousands of potential price scenarios and Those will get compared against The actual mark to market values for particular day that the value at risk is run And so this comparative analysis Comes up with a single number and that single number represents again a theoretical Maximum one-day loss on the book as it exists now the parameters because this is a form of statistics It's a statistical analysis. The parameters are that basically the result is saying okay The maximum loss on the book as it exists today Comparing the mark to market to these prices that have been generated by Monte Carlo simulator The company could lose as much as ten million dollars the confidence the statistical confidence in this case on this var Calculation is 98% and Then there also has to be what's known as the holding period in other words The var calculation is done at the end of the day on the book as it currently exists with the mark to market as it was calculated for that day However in the var calculation There has to be an assumption of how many days you could hold those positions open so you'll have The the single dollar value which represents the maximum loss a level of confidence in 98 percent You know usually would be the one to use because then you've only got 2% outliers on the other end But you might have you know one day two day three day four day five day holding period That's up to the company to determine But the holding period that's chosen also represents there should represent a the Reality when it would come to liquidating the position So in other words it would be unrealistic to have a single holding day Period because you can't liquidate your entire book within one day To do that would then adversely impact the prices in the marketplace on that day Which in turn would adversely impact the mark to market at the end of that day Other risk measures profit and loss now once you've calculated the mark to market Okay, remember you're going to have unrealized gains or losses and so at the end of that day the profit or loss total It's going to be the mark to market value and what you normally do is it becomes a cumulative number each day as you go through the month So the mark to market gain or loss on day one is added to the mark to market day loss on Mark to market daily loss on day two and so on so you have this running total and then you also need to Figure out the volumetric position. You want to know from a contractual standpoint. What is your exposure? So this is the total of all the derivative contracts that you have out there that are straight-up contracts Maybe they're futures. Maybe they're swaps, but then also, you know, we touch briefly on the options delta effect In other words, you know Getting back to the options if an option writer Okay, let's say writes a put or writes a call They immediately have some exposure which is quantified in the number of contracts that they might have to buy themselves or sell Okay, in order to fulfill the obligations under the options contracts if executed So this has to be quantified. There's a certain number of contracts that are represented when the Delta calculation on the options is run So for the true volumetric position of a particular book It's all the open derivative contract volumes. In other words again things like swaps forwards futures and then What the options delta calculation ends up being in terms of contracts? You have to add all of those together. Now, you know the volumetric position for the book itself In terms of energy commodity trading, obviously, we know in April 1990 natural gas contract was launched in 1983 the crude oil contract was launched We know to the provider price transparency and market liquidity You are now able to hedge your price risk, but it also added some more instruments for speculative trading It led to the proliferation of various financial derivatives as we know options such as puts and calls more exotic options And then swaps will Henry look alike swing swaps and basis swaps Now the Securities Exchange Commission and the commodities futures trading Commission and mandated that publicly traded energy companies Have to implement a risk control program Effective with their fiscal year for 2001. Okay, so what happened here is? They had to report their market market value under their earnings So in essence at the end of every day, they had to go in and calculate the mark the market value of their open positions Well, the federal government then said that represents revenue You've either got on raw unrealized gains or unrealized losses and you have to report those in earnings Okay Well for and Ron that basically gave them the license to steal Why because what Enron did was set up various shell companies paper companies and then they would calculate the market to market earnings every day on those little companies and Basically those would show gains and the more earnings that the all these companies were making In in terms of in total showed up by Enron's books. And so this was leading to a higher share price So the state now also the other problem happened was the traders now have a large stake in mark the market They want to manipulate the prices set these forward curves forward prices that we know are in the marketplace Well, they were setting them for Certain price categories that for which they were reporting to the publications and others So you can see they were starting to try to influence the cash marketplace the cash publications Which you know as direct market manipulations then another thing they would do is roll positions forward and backwards to gain mark the market value So they had positions that could be Liquidated and they could draw cash in and then turn around and put those same positions back on they would do this again We're talking about fluffing books up so that the books really didn't Were not true reflection of actual earnings or cash positions of the companies In a post-Enron world In essence a little more than a year after Enron collapsed what had been the top five energy trading companies in the United States were gone Wall Street, you know became very leery of energy trading companies. You'll find more companies today that are named energy service companies And Wall Street analysts when they want to look at a company now They're gonna look at the book size and it was total volumetric Open positions and then the mark to market related to that. They don't really put much confidence in Value at risk. They're not as interested in that because again, as I mentioned earlier, it's sort of theoretical There are you know also more and more companies Adopted fast 133 hedge accounting and what this did was allow them to shrink their speculative book I know the words positions are not open if you can tie them to a physical transaction And then of course there was the adoption of Sarbanes oxley Sarbanes oxley is an extremely invasive and intensive Procedures and recording of pretty much every, you know, every single transaction even down to the keystrokes in some cases Now here's where I mentioned Recommendations if a company doesn't have a risk policy in place and they have to implement one to me first and foremost the executive training They need to understand What energy commodity derivatives are and the various types and the types of risk exposures that are out there trading them They have to establish risk risk policies and procedures and within the policies and procedures It has to be number one a statement of the purpose of the hedging activity You know, what is it that you have that exposes you to? price and market risk Therefore, you know, you state why you're going to hedge Also, then you start to establish risk measures and limits. What's the daily maximum mark to market? You know loss that you're going to allow the trading company to have what's the maximum var You need oversight there needs to be a risk control desk And you need have set positions within that desk and the responsibilities for each one of them There needs to be a risk oversight committee. This is usually comprised of an executive panel Trading policies have to have violation penalties in them In other words, you know, there has to be a situation where if a trader violates it There is a penalty that they're very much aware of it's going to happen which can include termination specific procedures things like deal sheets Daily checkouts and those types of things and as I mentioned, you know, adopt phas 133 heads accounting Educate both internal and external auditors. It's kind of an odd thing But from time to time you find auditors You don't really understand financial derivatives either and yet they come in to audit the books of companies that have financial derivatives On their books and then of course Sarbanes oxley you have no choice but to implement Sarbanes oxley Even as as I mentioned as complicated procedurally as it is


A few videos ago in our lecture series, we discussed about negating logical statements. That is, if you put a not in front of a statement, how does that change the meaning of it? Now, as we've developed our Boolean arithmetic on logical statements, we've done a lot more since than as we've now developed it, it's important that we talk about some more complicated statements that get negated. For example, if our statement has the form of a conjunction or a disjunction, that is the words and or used, how does negation affect those things? This actually leads to what's commonly referred to as the De Morgan laws. The De Morgan laws tell us that for any logical statement, P and Q, if you take not P and Q, this is logically equivalent to not P or not Q. And similarly, if you take the compound statement, not P or Q, this is logically equivalent to not P and not Q. One can prove that these statements are logically equivalent by, among other methods, by drawing a truth table and seeing that these two statements have the exact same truth values for every possible combination of the primitives P and Q. And I will leave that as an exercise to the viewer here to verify that these two statements are in fact logically equivalent using our method of truth tables. The important thing to note about the De Morgan laws is that when you negate an and statement, it becomes an or. And if you negate an or statement, it becomes an and. The following shorthand that you can think of is not and is actually an or. I mean, this symbol is actually nonsensical, but the idea is, if you think of this as like a light switch or this light switch is on, this light switch is off. The negation is a toggle. It's turning the light from on to off. And likewise, if your light is off, not turns it on. It toggles between and and or. So if you look at some examples of this, let's take the statement P, which is the numbers X and Y are both odd. Hopefully we can recognize that this is a conjunction. We have two statements in play here. X is odd, Y is odd. And so P is then their conjunction. We put them together via and. So P is X is odd and Y is odd. That's the logical form of this statement.  But since you're taking not of an and, that by the De Morgan laws means you're gonna negate each of the statements, but then the and turns into an or. So not X is odd and Y is odd is the same thing as saying X is not odd or Y is not odd. Notice here that with the original statement, we're suggesting that both X and Y are odd. But when you negate it, only one of the two, only one of X and Y has to be odd. They could both be odd, right? That is a possibility with an or. But when you negate it, if you're like this statement is false because X is even, that's what makes that statement false. If I say X and Y are both odd and you're like, uh, X is even, even if Y was odd, that makes the statement false. And this is why essentially you change from an and to an or here. And means for all as opposed to or, which means at least this is something that we should hopefully be piecing together. When we talk about conjunctions, there's this relationship between conjunctions and the universal quantifier because an and statement is true when all primitives are true, all right? Conversely though, when you look at an or statement has this connection to existential quantifiers. I wrote that the wrong way. The existential quantifier, right? A backwards either. The conjunction is correlated with the existential because a or statement is true if at least one of the primitive statements is true. And so when you negate the and, it switches to an or. And this is also gonna correlate with what we'll see with quantifiers at the end of this video here. So the appropriate negation of the statement X and Y are both odd would be something like, oh, either X or Y is even. Now remember, for integers, you're either even or odd. So if you're not odd, you are even. So I can get away with saying even instead of not odd. Even and not odd are logically equivalent to each other. So the negation is X is X or Y is even. And turns to or when you negate it. And another example here. If you take this time that the statement Q, which is you can solve it by factoring or with the quadratic formula. This might be something I say to my college algebra students if they have to solve a quadratic equation. Oh, you can solve it by factoring or you can solve it by the quadratic formula. This is a disjunction. You can solve it by factoring or you can solve it by the quadratic formula. Those are both acceptable statements there. And therefore, if we wanted to negate our statement Q, well, you put a not in front of the compound statement. Since it is a conjunction, the De Morgan laws tell us that not our original statement Q then becomes you cannot solve it by factoring and you cannot solve it by the quadratic formula. Or if you say that in more standard English, you would say something like, you can neither solve it by factoring nor the quadratic formula. And proper grammar here, if you have a neither, it's followed by a nor. Just so you know in English, it means not or, but as excuse me, not or, but as we now are talking about, not or actually means and. Our English language has this logical principle already built into it. Not or means and. Because if I tell you, you can solve it using one method or the other and that's a false statement, that means neither method can be used to solve the equation. Maybe if you're trying to solve a cubic equation or an exponential equation, I could say something like, oh, I might be like, oh, you can solve it by factoring the quadratic formula. And you're like, no, no, you can't factor it won't work. Quadratic formula won't work. That's a false statement. They have to both be false in that situation to be the negation. Now we've talked about conjunctions and disjunctions. How do you deal with conditionals? Well, honestly to deal with conditionals, it's easiest just to recognize that a conditional statement can always be rewritten as a disjunction. We proved previously that P implies Q is logically equivalent to not P or Q. Because remember, if the premise is false, then the statement is vacuously true. That would be not P. But if the conclusion is true, then the conditional is also true because it's trivially true. So if the conclusion is true or if the premise is false, the conditional is true. These two statements are logically equivalent. Now, as a conditional can always be rewritten as a disjunction, you can actually use the De Morgan laws to negate a conditional just by thinking of it as its disjunctive equivalent. So consider the following statement. P equals the statement. If A is odd, then A square is odd. This is a true statement. But that's beside the point. Let's still consider it's negation. Before we do that, let's make sure we understand the form that's in play here, okay? The statement P is odd, then A square is odd. This is a conditional statement. The premise is A is odd. The conclusion is A square is odd. And so we have that implication. We can rewrite this conditional as a disjunction where we negate the premise or we take the conclusion. So not A is odd or A square is odd. And if you wanted to, you could rewrite that as A is even or A square is odd. That would be the negation of those things, okay? Now, excuse me, that would be the equivalent form if we write it as a disjunction. Now for the negation, if this is our statement P, the negation would then be not, not A is even or A square is odd. By the De Morgan laws, you would then as you distribute this not, the or becomes an and, and then everyone gets a not. Well, not A squared is odd is just A squared is odd. But be aware that if you have a double not, that actually would make it true again. It's like that light switch. If you flip the light switch twice, it's your back on again. So not not, what we often refer to in English as a double negative, actually is a positive statement again. So the negation of our original conditional would be A is odd and A squared is, A is odd and A squared is not odd. Or you might say something like A is odd, but A squared is not. We mentioned before that but often means and, when there's a contrast in play, A is odd and A squared is not odd, but it's the contrast, which is why we use the word but as opposed to and. And this actually makes sense because the only way that a conditional statement is false is that the premise is true and the conclusion is false. And so if you want to think of it that way, that's exactly what we're doing via the De Morgan laws. The negation would be, oh, the premise is true, the conclusion is false. Now, as I mentioned earlier, we mentioned how one can negate compounded logical statements using the De Morgan laws, they help us take care of ands and ors. We also talked about with conditionals because you can rewrite a conditional as an or statement. We could also do by conditionals because a by conditional statement P is equivalent to Q. That is always logically equivalent to P implies Q and we'll write the other way around Q implies P. And so a by conditional statement is just two conditional statements with a conjunction. We know how negation affects conjunctions and we just talked about how you can negate conditional. So we could do by conditionals as well in theory. The last thing I want to talk about is negating these statements is how do you deal with quantifiers? I made this mention earlier that conjunctions are somehow correlated to universal quantifiers and how disjunctions are correlated with existential quantifiers. If you think of like and is true when all primitives are true and or is true when at least one primitive true. That's the connection you want there. And so basically for the same reasons by the same De Morgan laws, when you negate a quantifier, it's gonna switch just like conjunction switch to de junction and or switch to ands. We have that the universal quantifier is gonna switch to a existential quantifier and existentials will switch to universals when you take negations. The phrase not all is logically equivalent to the phrase at least one is not. And likewise, the phrase not some is equivalent to the phrase none are. And so we kind of get the following notation here. If you negate a universal quantifier, it then switches to the existential quantifier and the negation then moves to the side. So not all means there's at least one not. And similarly, if I were to say something like not there does not exist at least one would mean that for all not happens. And let's put some real words into this to make a little bit more sense out of it. If we take the statement P to be all customers we'll get a free dessert. I really wanna go to that restaurant. That'd be nice. But if the statement all customers will get a free dessert all here this is our universal quantifier for all all all customers will get a free dessert. So if we negate that that would say not all customers will get a free dessert that's equivalent to saying that some customers will not get a dessert. Cause if I say that not all customers will get a dessert there is still the possibility that some could get it. Maybe, maybe not. Maybe no one will get one but we don't know that. What we can say is if not everyone gets one then at least someone didn't get one. That is the proper negation of the quantifier there. Let's do one with an existential. Let's take the statement Q to be that some computers have a two year warranty. So that's our existential quantifier there. Some computers there's no guarantee that all computers have a warranty but some of the computers we're selling at the store do have a two year warranty. And as such if we were to negate this there should be a negation symbol there. If we take not Q then this would say that it is not true that some computers have a two year warranty. That is the proper negation. It's kind of awkward in language but that is the proper negation. One that would be easier to say and would be logically equivalent is that no computers have a two year warranty. When we say no computers we mean for all computers not warranty is what's happening there. So the negation of some computers would be some computers have a warranty would be that no computers have a two year warranty. So you have to be very careful. Not all means some not and not some means all not. The quantifiers toggle when you negate just like the connectives and or toggle when you negate them. If you can keep track of that you'll then be able to handle negations of any logical statements.


Hey guys, welcome back to my channel. I am looking crazy right now. I wasn't going to film but I decided to So I am getting ready to go to an event in Atlanta. I don't think it's like a party or anything I think it's just like a little gathering that the ladies have him. I Did a photo shoot with her before for my website and she was really really nice So she sent out some invites to some of her clients and I was one of them So I'm getting ready now My mirror is here. You guys are here. So let's just chat Look at my skin. It's like, I don't know if my skin is like in the best condition The older I get the more I'm just noticing like little things I used to have like Super perfect skin But that could just be I haven't been to get a face shoot in a while Um So that could be it but anyway, I'm just gonna get ready and just chat with you guys About what's been going on. So you guys know I am in the middle of finishing up my living room space, right and Trying to finish up my living room space and I don't know if I told you guys. No, I didn't so I did tell you guys that I had another Whitehall table firm Restoration hardware coming because the second one was also damaged like the first one. So But the second one had like a really small Issue like it was really tiny, but you could still see it. So they bought the replacement yesterday There was actually no The replacement came yesterday and I'll be honest guys It My experience with restoration hardware Hasn't been the best but I'll say this my mirror that had the issue with the With the piece on the back being crooked They actually gave me Some money back on that one. So that was great And then I told her if the mirror This is white if the if the table is messed up just as much as this one or More I would keep this one and have them come repair and they'll just give me back some money So when the other table came yesterday guys, I don't know it was just like You haven't got something Sat with it for a while So now you're already kind of used to that design. It's wood. So you guys know it can look Different ways When you order the next one, right? The next one kind of looked like it's two things that I didn't like about it one Actually, it was three. It looked a little bit lighter. The third thing is It looked like it Was a repaired table. I don't know if that makes sense. So I can see like the top layer of it And it was giving me shine Okay, and then the prints on it which can vary of course I didn't really like it and then the guy the guy that uh One of the workers was like You know, hey, it looks like his English was not he said it wasn't great, but I understood him He was like it looks like like hands cat hands and I was like oh pause it did it just look like I don't know I didn't like it. So and then it looked like there was some like I don't know like some screeching. I don't I don't even know how to say it, but It just wasn't like the one that I had that had this the slight issue So I told them that I would keep I would keep this one and then they're gonna give me Some money off of it because I'm just like come on now now I get it It could have been a new designer and it's no shade to the designer because the table is really really nice right, but I think When it came to packaging it They probably could have done a little better I don't know maybe because I don't take no I did see I saw it the second time it was sitting in Like styrofoam, but I think they could have done a better job in my opinion for it to be all right, you know The stuff is not cheap and It should be lasting me for a really really long time. So now I'm thinking When I do decide to move This thing's gonna have to be packaged up to them. I mean it might have to be The last thing in the truck the first thing off the truck if that makes sense So I'm already gonna just like prepare myself for that situation when that you know comes around when I move But all in all I'm gonna keep it they're gonna give me around I think $500 back or Whatever is cheaper at the Outlet price, right?  You don't have money to waste so but luckily that they have Their workers are extremely nice and professional you know, so Yeah, so now I'm just waiting for a console table that I purchased from Creighton Barrel and I said I wasn't gonna get anything that was gonna take a month But I did it was pretty much a close as in color to my table I was gonna get a gray table and just do something a little bit different When I put the color gray in that table came up But when I looked at the people's reviews, it was not giving gray. It was giving like a Funny brown, which is kind of like how my My table is They're not gonna be side-by-side. So it's not a set. So I'm not worried about that and Yeah, so pretty much everything that's coming along the next thing I have to get is my My pictures my curtains are simple but my pictures over my dining table I need to Find I think I want like I Don't know. I feel like I want women like Diana Ross Like, you know women may be fully sure for sure. I don't know. I think I want black and white photos of women on my wall I think if I did abstract It's going to be too harsh. I don't know yet. I'm still but I really I want really large Photos really large photos. I'm looking this way. Sorry guys. I need to look this way like really large photos so What I'm gonna do is I'm going to just do some research and try to find the best one that I can find Let's see.  That's a lot That's a lot to me. I really don't like I'll be honest with y'all. I don't really like Foundation like that. I don't know like I remember going to school with girls and like the sixth grade and They was like Make up down. Okay Like this is gonna drag me insane They were like lipstick I don't even know if anybody was wearing foundation back then those girls But they damn sure had on a full face of makeup Full face of makeup Hold this over here a full face of makeup and then years later After junior high school mind you this was sixth grade these girls after junior high school I saw a couple of girls and They skin didn't look great and I kind of at that age I was just like maybe it was because they wore makeup so early on You know messed up your skin skins not breathing and stuff. I don't know that was my thought But it made them look a lot older when we got older, you know And they weren't even that old So Yes, I don't really like to put it. Maybe I'll get someone to do my makeup again Because I know for sure I'm not like putting on a lot. I know this for sure So All right, let's get into this concealer. I typically use You guys know if you guys don't know I use the NARS cafe deep one I think it's yeah deep one and Then I don't even put a lot of this. I am like I guess you could call like a no makeup makeup It's not really a lot and I need to open up my other one because this one is like On oh it's almost almost done For all the makeup gurus Listen, it works for me.  I Actually like it So I'll just keep like some Tissue of something here and Then what time is it I will take a monday Which is lighter and I would just like Chatter, but I'm trying to tell you And That's it. I cannot wait to go shopping guys. I haven't shown you guys any fashion content in a while I've been so focused on My house just you know redecorating and I just took a lot of time To be honest the next thing is my office my office which I Have stuff the issue is I'm trying to get rid of a sofa It's in really great condition, but I'm just trying to tone down the super white in that room. So yeah But let me tell you what I was doing. I was actually taking my finger and I was like going like this And I'll be honest. I was liking it when I did my finger and sometimes you can just say Now don't try this at home if this is If this is not This is not right, but I remember when I was in New York Eighth grade prom Okay My aunt paid no that my aunt my dad my dad paid for Me to get my makeup professionally done in my eighth grade prom for my eighth grade prom and I remember going to a guy in Harlem who was gay and He's like he used his fingers and I was like, oh, he was like your fingers is the key I would never forget that your fingers is the key and I was like, oh So sometimes when I rub it in I'm just like I'm just using my fingers All right, so I'm just gonna tap this on just a little bit and put it here Yeah, I Don't put a lot guys. I promise you All right, so then I'm going to but yeah, he did a really good job with my My makeup using his using his hands his fingers All right, so let's take out I have this palette This is the supernatural glow three thirty five zero three five oh and then I also Used the Mac which this Mac powder is like almost on its way out the door and I just put it I Put a little bit over My skin. This is mineralized up Just a little bit And to be honest, I don't even think he'd be doing anything to my skin and Notice how I bite that off super fast because I don't really like that powder because it's so It's so light like it's white and I used to have which I'm gonna buy I Used to have which one is it? The darker one the translucent medium deep But no, I ran out. So I'm just trying to get it back together and now I'm gonna take my Morphe leader Bronzer, I Just take it you guys seen that for Clone and then I move this over I may have to fix this. This is so far down. Oh Well, it's not bad Yeah, and then I'm gonna take some of this eyeshadow. I'm gonna take the color It's called darker in how bizarre and then remarkable I've been using those quite a bit Because it's not that dark I don't even think I use the right brushes guys to be quite on this I'm telling you for all you makeup girls put a sister. Oh, I didn't even put any primer on my eye No primer But to be quite honest, I don't even have an issue where it's like Creasing or anything like that. I do have a primer I brought primer. I mean a Shadow primer. I just don't use it. Yeah And I don't know maybe I'm afraid to put a lot of makeup on Afraid that I'm gonna like break out or something Because I just I Don't that's a lot. I look like a lot Yeah, it could be a fear Because nobody got time to be breaking out at almost 43 Okay, I'm actually gonna leave it like that.  I'm gonna try to vlog when I get there I just don't know how it's gonna be inside. It's not that big. I mean, it's a decent size It's a decent size And let's take some blush Nars this is savage This thing is beat to the ground guys Beat to the ground and then let me spray Chab run out of sam powder. I think I'm gonna try Urban decays because I heard that one is like really really good So I think I'm gonna try urban decays because this Mac one It'll be hidden So urban decay I'm gonna buy some new stuff and show you guys Listen, like I said, I got to get back into the swing of things because I was really trying to focus on my home I don't know how are you like that? Like are you when you're working on something you kind of like Pump the brakes on other stuff. I do Cuz they'd be a lot going on. I'm like, I need to do this this and that All right, so I'm gonna use So listen my eyeliner liquid liner I just use Maybelline. I was using the the elf one, but this one has like more of like a stiff Tip Let me see if I can get this on. Oh shoot. I'm not no good brew. Okay Let's put it over here. I gotta hurry up and get out of here because I gotta drop always to the city Oh, oh, I see my nose has We see my nose my eyes water Weather's not so great either. So and then I use my Dior show. I love this I'm gonna try to stop putting castor oil on my eyes Cuz I heard that it makes your lashes grow I'm gonna try it up and you guys know, I don't wear lashes We're not not often like it's very very Far apart when I do with them and that is because I can't put them things on quick enough I need to add some more line into the top It looks a little faded. You can see I'm gonna fix that. Let's add a little more. I think this side is too dark Whatever Actually, I didn't put it on the bottom. All right I'll show you guys when I'm dressed. I have no idea what I'm wearing But I want it to be like a little chill Because it's not that nice outside. Well, listen, I Think you guys for joining Mean today And all of the makeup that I use It's between Nars most of it is Nars and Then more morphing And What else Morphe and Mac and what's this? Laura Morse mercier. Yeah, most of it is Mac actually This is chestnut and then I usually go in is that chestnut Yeah, and then I usually go in with my Spirit This is spirit, which is like a natural And I'll just go in the center. That's it That is it I don't do anything extra but I am gonna go get dressed so I will catch you I will not get dressed and show you mine My look for today and I'm gonna really try to vlog inside.  So I'll catch you guys in a minute. Let me get dressed and then I'll come back With my final look. All right


Rwy'n cael ei iechyd rydyn ni, yn gwirionedd nifer i fynd i ddweud, a rwy'n rwy'n dod i'w ddweud i yw iddydd ymdian. ydw i ddweud chi'n cael eu cyfrifredeb Fyglwedd. Mae'r Fyglwedd Ardorladd fel Oesri, y mae eisiau gyda'r cyfrifredeb fydd yn gael arall yn y rhaid i gyd. Dwi'n cael e Womeniadau, yn fwy gwaith ymlaen, yn y pwyfin i'r cyflwyno i'r mateis cyfrifredeb. Onw. Onw'n weithio. Rhyw i'w ddweud a'n mynd i'w wneud yw'r bod yn ddechrau'n tŷu'r tŷu i fynd a'n dweud. Mae David a'r gyfnodd yw'n rŵn i'w ymddechrau yn dweud yn gweithio. Mae'n ddweud a'n ddweud i'n ddweud i'w ddweud i'w ddweudio'n ddweud ymddangos. Ond oed yn ddau'r fflawedau, rydw i'n byw'n bod yn dweud i'w ddweud ymddangos o'r ddweud. Ond rydyn ni'n gwybod, maelodden ni'n gallwn gweld unrhyw gyntafol i chi, neu oddo i ddweud yn gweithio newydd. On iddyn nhw, y gallwn arwain, dau 10 ysgol, rydyn ni'n gweithio'r reu'r hyffordd. Rydyn ni'n gweithio'r reu'r hyffordd sydd yn han i'n ddiwedd. Rydyn ni'n cael ei wneud ar mwylo i'r fodfyn Cymraethol a hynny'n gweithio'r fodfyn cyfnodol ateb ar ei wneud boi ddim yn ddweud ydym yn gweithio'r ddweud. Rydyn ni'n gwneud i'n ddweud i'r cyfnodd, rydyn ni'n mynd i'n ddweud y cyflwyntau. Rydyn ni'n gweithio, ynghylch yn ymddi, rydyn ni'n ddweud i'n ddweud i'r ddyfnodd yng nghymoedd o'r hollwch, wychwydd Lyfyn Hwyl yn boblnewisio fel y dysgu yn y dyma sy'n gallu cyfweld. Maen nhw'n ôl i'ch bod Folden Davies a'r awrf y project ond yn y fawr o'r gwahod, yn y bapers yw'r byw penderfyniad o fath yn deithun o'i bydd y gallwn gwithio y dyma maen nhw, y gweithiau ar gyfer gweld yn eich gwelwyd. Yna chi'n cael ei gwael wneud ond mae'r gwelwyd awr Jegol yw yn eich gweld. Felly, mae'r ffordd yn bryd ifnol yn unrhyw fyddi, tentrell o Melanovic yw fyddi yn ymgyrch basediaff y gallwn felly gydag y dyfodol. Felly, yn ein dwylliant, ryw'n edrych o ffrind i fơ o'r ysgolion sydd hi'n symud bod ydych yn gweithio lle mae'n ymhyfyr, oherwydd yr ysgolion yn ymgyrch. Ond, mae'n gweithio chi'n gwybod rwy'n edrych, oherwydd yr ysgolion yn fwy ar iechyd, ac mae hi'n fyddych sy'n adlengwys ar y cyflodiadau. And it's turned out to be, as I'll explain now, I think rather better than we had envisag'd. But ten years ago we started a project which was with Jim Davies, Ed Wolff, who's very well known in the US wealth field. Susanna Sanström, who was working with us here as a research assistant. Felly, fel am yw'r ysgolwydd, ydym yn cerddodd ystod, a'r ffordd o'r hollwch chi ar hyn yn ysgolwyddu, ac rydyn ni'n dod o'r ffordd am y byddai, ac mae'n ddod o'r rhaid o'r rhaid o'r hollwch chi ar y byddai. Rydyn ni'n dod o'r hollwch chi ar y byddai, ac mae'r hollwch chi ar y byddai. ac y ddatganiadio ar hunain. O'r cyfnod o'r ffordd yn ffordd. Gwyddoedd yw'r llwag yma. Rwy'n ffordd iawn, ac mae'n gweithio'r ddifŵr oedd cyffredinol. Mae'r llwg arall, oherwydd mae'n gweithio'r ddifthoedd, rydyn ni wedi eu cydnog yma, ond mae'n gweithio'r ddifthoedd. Yn y rhan o'r ffordd, and as Tony just said I expect you might even be able or if you can. I don't know if there's any copies to buy here but if you do have a copy you can get the editor to sign that since he's here as well. But it's an interesting book and still lots of interesting material in there. serious trouble. When I was, I have to say, some of my work on wealth has led me to think about some advice that I might give to people when they're working in their income and wealth field. One is, I think, first of all, to be skeptical, and that is, I'm strax all the time how people just get a data set and just process it, and discuss the results without really thinking about all the problems with the data. Ie ddaeth y survey, yn ei ddweud o ddysgu ddater, yn ei ddysgu ddater a i'w ddod oedd ganddysgu'n ddater. Mae'r cyfnod wedi bod yn ddau'n ganddysgu'n ddater, byddai'n ganddysgu'n ddater, byddai'n ganddysgu'n ddater, byddai'n ddater sydd yn ei ddater i'r llwyffydd yn ffordd fawr. Mae'n cyfrifio'n gyfrifio'n cyfrifio'n cyfrifio ydw i ddater, byddai'n ddater i chi ddau. just test it. But I think there's a lot of other data which people do do just rather process it rather unthinkingly and you do have to, I think, not trust any data in this field unless you really have confidence in it. I would give that advice because I think that you can benefit a lot from taking that advice. The second thing I've learnt by doing this wealth is to try to be resourceful or inventive, and that is, you know, try to use data which is not the standard data and try and put things together and try to get an overall perspective from combining data from different sources and processing it in interesting ways. We've done this in the wealth field in a couple of ways. First of all, usually you get data when it's lagging behind several years. We actually use, we are just now processing data for the middle of this year. We are about to publish wealth estimates for the middle of 2014. How do we do that? Well, because we use a lot of changes year to year in wealth holdings are driven by what's happening to stock market prices. We're working for Credit Suisse so they give us the data on stock market indices to the end of June. We have house price indices, which we use as a proxy for real assets. We have exchange rates quite easily available. So we use all this and we get, I think, a reasonable estimate of middle 2014. If you wait for all the data to come available, we're talking about two or three years lag. Of course, things are changing quite rapidly in the world and it's much better to have up-to-date data. So that's the way in which we can use some interesting available data in useful ways to get up-to-date estimates. The other thing that we've been doing is to use data on top wealth holders. There's actually quite a lot of that available. Every year it's more and more available. The Forbes data, which is the best known on billionaires, that goes back now, I think in the 26 or 27 years of billionaire data. The UK has a rich list, which they publish every year. They've done 25 years now. There's lots of data for individual countries now with that sort of information. There's problems about it, which I will also mention, but in terms of using that data, I think we're using it extensively and I'll try and persuade you that it's, I think, a very valuable source of data. But also in the income field, I think we have now, as Francois was talking today, and the PSC database, there's lots of data on top wealth income holders in various countries, the top 1%. A lot of the data which you get in processing survey data is not going to really capture those top incomes. Can you somehow combine the two together to get a more realistic estimate of income inequality? And I would say yes, and I'm going to at least show you how we do it for wealth so that you can perhaps do it also for income. And that's one of our objectives as well. The last point here is to be humble and that is, I think in all of this field, there's a lot of uncertainties here. And anyone who simply starts to talk about the third decimal point of the genicoefficient and so on, I just wonder, you know, I think you're giving a misleading impression. I think we have to just sort of say, you know, I think this seems to be the sort of trend, you know, if you've got enough data here, put points for enough data years, you can have a little bit more confidence. But I think we're talking about here of quite large confidence intervals and I think everyone should at least, I say, be humble in terms of what conclusions you draw. Today I'm just going to say I'm going to talk about the methodology we were using for our global wealth distribution side. I'm going to talk about the various problems that we've encountered. I'm going to actually end up offering a sort of alternative to the Lorraine's Curve as a method for capturing and analysing inequality data. And if I'm probably, I won't have time to talk very much about the income side, but if there's some questions I'll handle that as well. This is briefly, then. This is our strategy for doing global wealth, for constructing a sort of global wealth estimates. It's first of all we try to estimate the average level of wealth in different countries. How do we do this? Well, some countries have good balance sheet data. Some other countries have some survey data. Where this is available, we use it. There's a growing number of countries, so we're now up. I forget exactly, but I think we're talking about 40-odd countries that would have some sort of data, certainly on the financial side, fewer countries with non-financial data. We also, we then extend this to other countries by using regression methods, because we think that there's a relationship between income and wealth, and so we can sort of estimate what we think the asset holdings are in various countries that way. Other countries where we just can't do it at all, we like to have a complete picture of the world, so for some other countries we just sort of impute a number, which is the sort of regional average. These tend to be quite small countries, or countries like North Korea, Myanmar, Sudan, these sort of countries which you can't really do much about, but rather than just miss them out, some other people that talk about global income inequality, they just miss these countries out, so we just give an imputed value. Then we look at the distribution within each country. We do that. Some countries have wealth distribution data, but when we started it was really quite small, I think less than 20. Now it's up to around about 30, I'm sort of ballpark figures, so we thought, well, what we can do here is if we haven't got the wealth distribution, we do have income distribution figures, because we've got the WID data, and maybe there is a sort of systematic relationship between wealth inequality and income inequality, wealth inequality is always higher, and there is a sort of pattern. So we would estimate the Lorenz curve for the wealth distribution from the income using the 2030 countries where we do have both wealth and income data. So this was our strategy, and then we would add the, again, impute values to countries that didn't have any income distribution data either. I should also have gone back and said, for the mean for the, as I said before, for the recent years we use, we update the figures using recent data on what's been happening to the market capitalisation in different countries and house price indices and exchange rates. We then, I should also mention that in order, one of the problems, I won't mention problems, we have a little, we then use the distribution and the level we generate a synthetic sample for each country which exactly matches, which is consistent with the evidence that we have, consistent with the mean, consistent with the distribution, we generate a sample, and then we throw this all together. This sample is actually now one point, it's a sample of 1.3 million, so it's, you know, it's, it's roughly in proportion to the size of the populations of the countries. And then we process this and generate and discuss the results. What are the sort of problems? Well, some of them are problems which you have to tackle in the income side as well. You have problems with the definition of the income concept or the wealth concept. Again, I won't dwell on this, I've just mentioned, because it's not particularly interesting and it's rather predictable, I suppose. We also have problems with the unit of analysis. This is game one which anyone who's been working in this field, even if you're working within a country, you have a problem of what distribution are you talking about. For the wealth side, we've, we adopted the strategy that we're going to talk about adults, which is a bit unusual because people usually talk about households or about the distribution across persons. We took the view that really minors don't hold very much wealth and so we've, and it's not really interesting to look at the distribution across the world's population, it's more interesting to look at the distribution across world adults. And that actually seems to have been adopted by other people that work on wealth reports since we started that. There's issues also about exchange rates, whether we use current exchange rate or PPP, that type of thing, which we are progressively now just keeping with current exchange rates. More interesting questions that perhaps you won't have come across or the solutions we want to come across. One of the problems, if you look at the width, you've got a lot of, it's in a particular form, some countries you've got quintile data, sometimes it's DSAR data, you've got top 5%, bottom 5% missing bits and pieces. We've taken the view that we've put it all in a common framework, which is to look just to actually construct the Lorenzco of where you get the missing points. Then we have a nice little utility which we wrote when we were here at Ryder, which is on the Ryder website, which generates a sort of synthetic distribution to exactly match the distribution. The one that's on the Ryder website is the first version one of this. We've since been developed a more advanced one, which gives you sample with different weights. The reason why that's quite important in the wealth field is that we're now using one sample point for each 10,000 observations, that's for each country. But when you get to the top 10% in each country, we have one for every thousand, and when you get to the top 1%, we have one for every 100. This really gives us much more detail at the top end, which is really where all the driving interest is coming. So that's how we generate this 1.3 million observations around the world applying this rule. Here's an interesting question. I think we have come up today a little bit, particularly with France, so I think about the question about residency. It's not so important perhaps with the income side, although this question about people owning assets and keeping income or having income from overseas and whether they remitted and so on is an issue. In the wealth side it's really quite important because billionaires move around the world. And if you look at the Sunday time rich list for the UK, there's a lot of people that you wouldn't immediately think of being British. In fact, in the top three or four people are all, I think there's a couple of Russian billionaires in there. But this issue does crop up quite a lot and I think this is going to be another issue for the future. People do move around and certainly if they don't, some of their assets move around and it's not quite sure whether you're measuring them in the right sort of way. I don't have a particular solution to this but I think it is one which could be increasingly an issue for the future. The one which is really quite important and I'm going to spend almost time is the top tail adjustments. We think in the case of wealth distribution that the original data that we get, which is essentially from surveys, isn't accurate. Even in the best quality data is not and in most countries it's way off. So how do you somehow adjust the data to correct it for these missing people? There's two problems. One is that you miss people because in surveys there's a lack of response by people of the top end. This is well recognised. The second issue is that they tend to under-report it. It seems to be an endemic problem of surveys that to some extent you can try to improve on it but you're still going to be limited. The last issue here, I don't think this is completely exhaustive but there's questions about methods of presentation. Again, our experience of just sitting down and trying to present this data to various people. It's quite, you know, to think about how you present inequality data certainly in a way that is interesting to the general audience should we say. I think it is quite a challenge and I think just listening this morning there's various ways in which one does that quite successfully. I'm just going to mention too that we've used quite a lot. When we started doing with Credit Suisse, they kept talking about a pyramid, the wealth pyramid. Nobody had really thought about it but then I thought well we could do it as a wealth pyramid and that's exactly what we do now. This is the sort of pyramid that we produce. We group people into those with assets less than $10,000, 10 to 100, 100 to a million and over a million dollars and the areas here are reflecting the size in the world. So it's really quite an interesting way and I think one could do that for income side as well quite happily. The other thing which we do which doesn't seem to be used, I've never seen this used elsewhere but again it's something that we use first time in the wider book and I think it's really quite interesting is to look at the distribution of people at different points in the distribution in the world. So this is just looking at the desile points in the world and saying where do these people live. You could do this of course in individual countries and you could look at where people live within the country or various sorts of characteristics. It's a rather interesting way I think of rather than a table of doing a visual presentation. This is the latest version I've just taken from this year's report. China you can see there, I mean you look at this immediately you think there's the dominance of India and China in the world and China taking this really middle position. Ten years ago when we first started China was much more in the middle and you can just see it year by year this great blob moving towards the right. And I think it's really a sort of interesting again way of presenting the data. Now the real problem is here reflected in this diagram where we, this is just taking the data for China which is given from a survey and plotting it. What I've done here is to plot what you would normally do if you were looking at Pareto tails. In other words you plot the logarithm of the number of people with wealth above a certain level and you use the log of wealth. If it's a Pareto distribution you get a straight line, most times you're not going to get a straight line all the way down. But the notion that somehow as it towards the end it straightens out that would be the characteristic of the Pareto distribution. The problem as you can see here is that, oh here we are, there's a little blob there. Where's that blob from? That is the billionaire data. Back in the year 2000 China I think had one billionaire and there is the billionaire. And you can see if you were predicting that from the distribution you are not going to get any billionaires. In fact you would hardly get anyone with I would think even if you extend that you get, what do you get to? You get maybe 10 million if you're lucky. You wouldn't even get, the biggest wealth in China would be 10 million or something. Clearly they've got a billionaire. What's going on there? We think it's to do with all the problems that you have with survey data in the top tail. What do you do for that? Well we've taken the view that we'll assume that we have a Pareto tail. And we'll assume that the billionaire observation is correct. And that we will somehow straighten out the line and use that as our adjusted distribution. The problem with this is you're adding a huge amount of wealth in. So you then have to adjust the mean of the rest of the distribution down to correct it so that you've adjusted for this extra wealth that you're adding in. So you just reiterate and we would then, our adjusted distribution would be this sort of dotted line here. And this is what we didn't do this with the wider study here. We just used the unadjusted data. When we started doing it for Credit Suisse we have made these adjustments. It makes I think a huge difference to the inequality in these countries. I mean you get big jumps. We're talking about genies now. If you're used to thinking genie of 40 or 50 or 60 years high we'll just forget it. We're talking about up in the 80s and the global genies are close to 90 I think. So we are in a whole different world here. The problem is what happens here? How do you, this year we've been sitting and saying can we get a series of inequality, wealth inequality back to the year 2000? And the trouble now is saying okay, here is the data for China of 2005. If you keep your eye on that sblob as you, this is again, this would be the survey data. That would be the billionaire data. As we go on, we're up here, China in the year 2010, close to 100 billionaires. And here is 2013, it's 150 or something billionaires. Again, it's totally inconsistent with the survey data so we've got to do something about this or really give up our exercise. We get the same, and the question is how can, at least the initial thought might be, we just adjust this year by year. The problem there is we think that the billionaire data can be jumping around for, it's really quite, we're talking still about quite small samples. For other countries it can be, here's China with 100 or so, but other countries could only have four, five, six, ten billionaires. These are quite small samples, they can be jumping up and down each year if someone dies or splits their wealth. You can have quite erratic movements in billionaires. We could be relying too much on data which seems to be unreliable. I'm just going to show you the US data because the US is just so wonderful really. This is again the same figure, I'm afraid when I was printing this I realised there's a slight glitch somewhere in the US data and there's obviously some grouping somewhere because of the way that the data is being generated. So just forget about that, I'll sort that out at some point. The point to make here is here's the US figure. This is just their survey data, the survey of consumer finances and you can see actually the US has got something like 100. It's actually got more billionaires, 10 to the power to it, just 100 and this is for the year 2000. Even so we were able to generate our sample, I told you we were having one for every 100 people so when they get to billionaires in the hundreds we actually have a data point in our data set which is going to represent that. Here's the figure for 2013, again we've got sort of 400, I forget now, 400, 500 billionaires in the US. So our data point is, we've probably got a couple of data points in our data set. But again you see just how good it is, there's hardly any adjustment needed for the US. The US I think claims to just be about mis of top, about 2%, it only misses really the billionaires from their data set and it's roughly 2% of wealth. So the US data, they claim it doesn't mean much adjustment and in fact that's very strong evidence here. The US, the data seems to be way above much better than any other country possibly. There may be one or two other countries that do something that's pretty close. I think Canada, I don't know, maybe Italy, certainly not the UK, you wouldn't get anything like that. So the problem is, how do you combine these in different sort of years? This is my good idea I have to say now. Instead of here we're plotting the logarithm of the number of people, here we're plotting the logarithm of wealth. Suppose that we do here we plot the percentage, the logarithm of the percentage of the population and here not wealth but wealth relative to the mean wealth of the country, what multiple of mean wealth it is. The great advantage of that is you're adjusting for the population size, you're adjusting for the mean changes in mean wealth. So in terms of simple things it is, if we make that adjustment we're now, if all that's happening is there's a change in population or there's a change in mean, the actual curve doesn't change. It is a replication invariant and whatever it is, mean independent type, I forget the terms I've used in the past. So that allows us to say if inequality is not changing this distribution shouldn't change. But of course the mean changes so the billionaire point changes and what you then get is a fixed curve if there's no change in the distribution and you can plot all the billionaire points not just for a single year but for every year or indeed any rich list data on the same diagram. So here we have now, this is tracking the US figures, this is the billionaire data back to year 2000 all plotted on the same line and you can see roughly speaking here that the billionaire data as mean wealth goes up as it has been in the US the billionaire points are moving towards there's more billionaires because there's more people being pushed over the line. If inequality is not changing it'll just track up that curve. And indeed that is roughly what's been happening to the US. The evidence here is that there's a very little inequality change for the last, since the year 2000. Here's the one for Australia which I think is even nicer because Australia's had rather bigger increases in wealth partly because its exchange rates been appreciating. And here is the distribution curve adjusted for one year but we're keeping that distribution fixed. Now you can see you're pletting the logarithm of the percentage here so there is always two at the top here. 100% of the population is going to be the top end. Here is Australia and except for the first couple points up here in the first early 2000, 2001 you can look at the data, you're just tracking up, it's just moving straight up here which I'm interpreting as being almost no change in inequality year on year and simply moving up there. So this is a country in which wealth inequality has not been changing over time. But what's exciting me here is instead of relying on having billionaire data for one year and we just could be jumping around, here we're plotting them all and we can smooth out the data and actually get something which avoids some of the year on year variations. So I've got lots of, I've thought I would, for the Minister I've got a little diagram for everybody here now. This is Brazil again. Here the billionaires are going up but they're tracking up pretty well along the line indicating that wealth inequality, I mean I look in more detail in a minute but it doesn't seem to be that it's huge change in wealth inequality in Brazil over the time. Who else have we got? This is for Francois. Francois, well it actually looks like here where if you interpret this, these are the early years, we're moving in this direction which actually tends to, I'm interpreting this as actually if we started with the line here and the line gradually moves in this direction it means there's falling wealth inequalities. That's roughly what's happening. I've got here for, here's the Italian data again, not a huge, doesn't seem to be huge change. I'll at least, there's not as much obvious change as there would be from other countries. Mexico, this is for Nora. This is my Nora diagram for Mexico. Again, I don't quite, since you can't really tell which is the first year or the later years I'm not sure about the trend there but it's not quite, it's not so evident as it is for Australia where it sort of tracks up the curve. So, here's the UK but again all these points, interesting because we can plot them all on the same and we can start processing in the way. Now here is some interesting one. Here is China. What's happening here? Well, here is China in the year 2000, the line would be right down here as it goes, as we're going over time, it's moving up here and what's that mean? It means wealth inequalities going up big time in China. And this is looking at this data that's giving you a big signal that's happening. Here's the one, here's India again going up, trending upwards, here's the early years and we're trending up here. Here's Russia again the same sort of trend, perhaps not quite as strong as it is in China but again increasing inequality. So, let me just move back. What this allows you to do now is to look at, to take a sort of one line. I've taken for each year, I've taken the sort of average, what I've done is taken all of the Forbes data, combine that into a sort of single representative point, computed the curve based on that average and then looked at deviations each year from that line. So, you look now and you see, to what extent is it deviating, is it above the line or below the line and by how much? And then you can plot those against each year. The T here refers to the time since the year 2000. So, this is France and I have to say what is quite evident in this data is that there seems to be reduced inequality up to the financial crisis and then the crisis is changing and it's going up. In fact, I've just made that assumption and assumed that there's a split, there's a change in 2008. So, here we have France, so we're trending, here the deviations it was above the line but the line is going down and then the deviations are going up again. So, we've got reduced inequality for France going up again in the period since the year 2008. Here's Italy, again you look at the dots, there seems to be a pretty clear pattern there that Andrea would perhaps recognise. I don't know whether sometimes it's evident but I mean the French thing is because there's so much interest, the Piquet is generating this discussion in France, it may be, it's a groundswell of opinion thinking that inequality is going, wealth inequality is going up in France and this data would certainly tend to suggest that. So, that would be consistent and in Italy and indeed here's a Mexico, well not so much. I mean the Mexican figures are starting declining again up to the year 2008, a little bit of a jump, whatever. Here's the US, the US is quite interesting because it's been the main focus of all the discussion this year about what's happening to wealth inequality in the US. The SCF data is very close to our numbers here so we're not differing very much. In fact, the US data is probably good enough that you could take the individual year on year variations. Here we are, we seem to be, I forget now, according to the SCF it looks like I think 2010 is their latest figure and they said wealth inequality was higher than 2007 which is correct, I mean according to these numbers here. Beyond that, before that it was fairly flat, again this is exactly what we're generating here with our numbers but the indication here is we've generated the last three years and we think that the numbers are falling. So when the SCF come out with their next data, which will be for the year 2013, then according, if I were betting I would bet that actually it would be going down and that's going to confound quite a few people because they think that there's a trend going up. I'm about to, I'm getting close to finishing but here is China, again just all the time just unrelentless wealth inequality going up, particularly fast up to the financial crisis, not so fast now but still rising. India, where it's got flat, was rising up to 2008, now seems to be flat. Europe in general moving up fairly slowly but was going down so there's not much change really since the year 2000. Africa, one of the problems of course is that a lot of this billionaire data is there's not enough observations really to do individual countries, in those cases I've just done it, I've sort of put all the data together for Africa, used the billionaire data for Africa, adjusted the distribution and then given it back to the individual countries sort of separated it out. But there you are in Africa looks like since 2008 it is that inequalities increasing quite strongly. Canada's is one of the unusual cases where it seems to have gone down, that's a pattern quite common to lots of countries up to the year 2008 but it's still at least flat or perhaps even slightly decreasing since then. Australia again I told you that it was tracking up and that's exactly what's happening here, there's almost no trend at all in the Australian data. And that is actually the last slide, so I'm probably perfect in my timing. I don't know what generally I say, I think one of the implications here I think for if you're not interested in wealth inequality, clearly I think there's lots to be done here. And this is not just with the Forbes data because there's lots of countries which do have rich list data and you can use this and perhaps get longer series and do some interesting work. And this is really quite easy to do and so I could see quite a lot of interesting little studies for individual countries using these sort of techniques. For incomes I think one could do the same sort of thing, we do have data now on the top 1% incomes in lots of countries. We do have income distribution data, my guess is it would have the same problem that the top 1% is not, that you get from the top 1% database is not the numbers that you get from the survey type data. Can you match them together in the same sort of way and look at trends over time? I think that would really be interesting and really an important contribution. So I'm really quite excited about this, I think it opens up huge research possibilities and I have to say this is, there is not enough researchers certainly working in this field and I think particularly looking at trying to look across countries because one of the things we do try to do is get comparable data for different countries, otherwise when you're trying to compare different countries I think you're always rather dependent on the way that the variations in the individual countries can be bigger than the differences between countries. So this allows you I think to make much more of a sort of consistent comparable view of the world and how individual countries rank in their positions. Thank you very much. OK, thank you very much.


Hello, Chief Holbrook here with an important message and request. Just like I do every year, today I made my donation and commitment to the United Way. I encourage each of you to join me and do the same. No donation is too small. The United Way is a long-standing, trusted community partner and together we're making a difference. Last year, generous donors like yourself made a real impact in the lives of our friends and neighbors. Almost 1,900 students participated in quality in and out of school programs funded by the United Way and 88% of them improved grades, reading skills, attitude or advanced to the next grade. So I thank you in advance for your donation and choosing to live united. Stay safe and stay well.


Calvite and this is my colleague, Wendy Vortiz, with Dr. Duarte, I'm in bike youth leaders. We have a walk-in bike youth leader and we're actually, this is five of 11 and we are here to show you the work that they've been working on for the past five months. So this has been a program where we've had the opportunity to bring youth from across the state, so 16 to 23 who are active in their communities and have used this program to advance their skillset and their knowledge and also bring their voices to a larger audience, which we are here today to showcase to you their perspective in their community. So some of the things that they've been going through is curriculum to help them do an assessment of a particular place in their community that they'd like to see approved for walking and biking facilities and looking at it from their point of view and their perspective and their community's voice and so we're gonna be showing you just four of their projects, so not all the projects, but just four here today to give you a sense of their perspective, so with that, we'll get started. I'm Jean and I live in Westminster, California. So a little bit about me, I find out this program through my Westminster Youth Committee comprise of a couple teenagers from the area who just share their ideas, advice city council, what we think, how the city's doing and volunteer city events and at first when I got involved in the youth leaders program for walking and biking, I wasn't really into walking and biking, I was just kind of, I go places kind of thing, I don't really stop and think about it, but this program opened my eyes up a lot and I realized I didn't need to be affected by certain walking or biking incident in order to be impassionate about this movement and to be more involved and that I didn't realize I didn't need to come from a certain situation where I can share my ideas and open up to people like you. My name is Omar Vargas, I'm from East LA, East Los Angeles, California and I'm 23. I found out about this program through Los Angeles Walks when I was doing work with Visions World Los Angeles back in the summer, this summer around June and it was really an opportunity for me to really explore my interest in urbanism which is still pretty strong as of now and it's really, really made me open my eyes to my community and just understand it through a different perspective coming from the Midwest during my undergraduate years. Hi, my name is Zillian Gonzalez and I'm from Sacramento, California. I got involved with this program because of Tony Dang. I had been working with California Walks as a youth advocate throughout high school and so it was really great that now that I'm 19 and I'm built that I can still participate and still be learning a lot more from my peers. Thank you. Good morning, my name is Aaron Rees, I'm 16 and I'm from the city of Santa Ana and I got involved with this work as my youth organizer from YOS which is Youth for Active and Safe Communities and a little bit by myself. I don't bike but personally I am a ski porter but is Edgar here? No. I guess he's like, he's also here representing Saus which is a Santa Ana of the bike and I go along with them with my board. Yeah, that's basically what I do. Hey, my name is Andrea Garcia, I'm 18, I'm from Enmonte, California and I think with this program I didn't really know what I was getting myself into and just learning how to do a video voice and how much it could impact someone's life and also just meeting all the youth and our coordinators, we had a conversation last night which is so amazing how we're all so different but we all have something in common which is that we like advocacy and we're all so like-minded so I think just meeting each other was very special. My name is Kelly Ma and I am one of the...


Hey everybody, it's Hari Swaminathan from optiontiger.com. It's Tuesday, May 19th and we have swing trades here. The markets are somewhat flat rather but it's been in negative territory for a couple of hours now and just clawing back to the neutral but no big deal, there's nothing going on. It's kind of a flat market and so as you know yesterday booking made a huge move in anticipation that travel might be coming back must be because it made almost a 10% move. I think it was a 10% move that it made higher. So we're going to be focused today on some adjustments and also some closings. Here we can see Amazon is almost at full profit here. There is a little bit of premium on the call side. As you can see there is about 60 cents. So depending on how many of our contracts you have that's 60 cents and so if anybody wants, if you have any margin issues then we can close it. As you can see we are using about $30,000 in margin and plus I'm going to be needing some margin on the booking adjustment today and so I'm closing Amazon. I'm also closing Shopify. So as you can see now Shopify has come out of the the body of the condor here. Let's go take a look at Shopify and what I want to see. Yeah, it's coming out over here. So it's not worth it. I think if it would have stayed inside the body we could have waited for some time to get but it's not worth it anymore. There are decent profit $1,200 here and about $2,100 almost on Amazon. If you want to join the Swing Trading Signals Program you can go to this bit.ly link, http bit.ly slash 25 swing to get a 25% discount on the monthly plan. You can also come to the Option Tiger website at optiontiger.com and on the homepage itself you'll see the algorithm based swing trading signal service. If you click on learn more that will take you to the swing trading signals page and here you can get some more details on it and you can also sign up on a monthly plan for 25% off or an annual plan for 50% off the list price. Thank you.


Good morning, Berlin. Good morning, OpenStack Summit. It is great to be here. Do you guys hear that? Boom, boom, boom, boom, boom. I do. That's the heartbeat of the data center. That is what you guys have built. We at Intel feel really proud and excited about this community and its contributions to the stabilization and the growth of the data center. We have some other technologies that we'd like to talk to you about today as well. We are on familiar ground, though. The heartbeat of the data center, powered by OpenStack. We have so much strength in this community. You've worked so hard to enable deployable, stable, mature, secure software that enables private and hybrid cloud models to flourish across the ecosystem. That being said, we are in the reality of a software-defined infrastructure. OpenStack is at the center of what has become our world that we live in today. There are a lot of new technologies, though, that are coming into our ecosystem and are providing the next generation of innovation, and these are all built around OpenStack as well. As Amy mentioned earlier, there is significant opportunity for new use cases, and there's significant complexity associated with what has to be delivered for edge computing. Many people talk about latency as a major challenge towards edge deployment. That's not all, though. The amount of data that's being generated, the amount of data that needs to be analyzed, stored at the edge, becomes significant challenges when we start talking about deploying infrastructure at the edge. You've heard the use cases, right? Be it smart cities, be it distributed networks in the middle of skyscrapers, be it emergency response, you know, being from the west coast of the U.S., we've had a really painful and clear view of the criticality of infrastructure and need for real-time communications to preserve human life and make sure that the right people are notified at the right times. All of these things, be it exciting eSports applications or AR, VR, really require us to work together as a community and get to deployment fast, and that is where StarlingX comes in. You've just heard from the StarlingX guys, significant accomplishment starting an open-source project and within six months getting to a community release. The reason we emphasize that, the reason that folks have worked so hard, is we really want to put this configuration into your hands to be able to do and enable technology advocates to do what they do. You give them software assets, you give them infrastructure, and that is when the innovation really begins. You give them the platform, you give them the baseline infrastructure, and that is when the use cases are truly realized. And entrepreneurs and amazing technology advocates can figure out what they really can do when they actually have the deployment. StarlingX is designed to be scalable, so there's a couple different configurations designed to be very small at the gateway, all the way through a multi-server 100 node deployment. If you want to get that high availability, you need to have at least a dual server implementation, but it can scale all the way up to nine Ceph node clusters, so it can really go and reach to meet the complexity of the different use cases when you're talking about AR, VR across a huge stadium, etc. But this is not the only project that we're investing in. Project Cyborg is really important for us as well. This is part of the OpenStack Foundation projects and the OpenStack projects. It's integration with NOVA to enable FPGA, to enable accelerators to be viewed as devices with nested resource providers for FPGA, GPU, etc. We're really excited about enabling function as a service with the hardware acceleration and as well as devices as a service through Cyborg. This becomes really important in the future with the bare metal use cases, bare metal cloud that you've heard about so much over the last couple days as well. Moving up the stack a little bit, I'm excited to talk to you about NEMU. I don't know if you guys have heard about NEMU yet. QEMU is absolutely the de facto standard for open source hypervisors. It is stable, well known, it's accepted in the industry. It's also really large. And so what we have done with NEMU is to really try to focus what is needed for cloud hypervisors. We don't need a lot of legacy code. NEMU has a footprint that's about 70 plus percent smaller than QEMU. It is in the open source, we're working with the community and this was really developed in response to the major cloud service providers and the community saying we need something new. We need a new technology, we need a new solution. And so I'd invite you to check it out, take a look. We have some of the NEMU experts here at the OpenStack Summit and they will be in our booth so please talk to them about the effort in flight. Moving up the stack further, Kata. Man, Kata's gotten quite the reception here today. You heard about it from Deutsche Telecom. There continues to be very active development from Huawei and Google, Hyper, Intel as active contributors, but the folks who are using Kata, it's just designed to be a drag and drop replacement for your container runtime but leverages hardware virtualization to ensure your security. You can think of most of the major cloud service providers, both public and private hybrid cloud, are using Kata to deliver value to their customers and ensure that security. And so finally, I'm super excited for you guys to get to see these things in action. We have live demos of almost all these technologies in our booth. They'll be on rotation because we have so many demos. So please stop by a couple times, get to see the various technologies. You'll hear from the Kata team in just a few minutes. There's so much going on within the ecosystem and we need all of your help and support to really accelerate and drive innovation forward. Thank you very much.


All aboard, club called special, all aboard! It's in our seats to enjoy the March of Events and City Life section of our newspaper. This popular section appears in Hearst Sunday newspapers from coast to coast. It is written by such famous humorists as Will Rogers, Arthur Bugsbear, O.O. McIntyre, Milt Gross, Sam Hellman. Its cartoons are drawn by the brightest and most up-to-the-minute cartoonists of the day. The March of Events and City Life section offers you a million dollars worth of fun and probability. Here we go. The cartoon. Here's one of a man and woman sitting at a reading table in their home. The hour is late and the man seems to be dozing off. I said you dropped your paper.  Well, I'm too tired.   Oh, I see all the stories are having a bargain day tomorrow. I better get downtown early. And look at this. Did you read about that kangaroo with two heads? Henry, are you listening to me? Henry! What did you say? Oh, there you go, dozing off again. That's what you're doing. And right while I was talking to you. Now, do you think that's nice? Henry, you're doing it again. Do you hear me, Henry? Yeah, of course. Oh, what's the matter? Well, will you please wake up long enough to go to sleep? The story of an American who is traveling in Europe. It seems that this man went into a restaurant. And here's what happened. And what else, please? Well, let's see. Now, I'm not terribly hungry.  No, I don't feel much like soup today. I wish I could read your language better. This menu is all Greek to me. That, I fix it. I give you one of the American menus here. Oh, thanks. That's a lot better. Say, this is an American menu, but it's almost as bad as yours. All the dishes are named after movie stars. Yeah, yeah, movie stars, politicians and baseball players, and everyone that is famous. What's a Sally Rand salad? Sally Rand is your famous fan dancer, ain't it? Yes, that's right. Well, you see, the Sally Rand salad is served without dressing. I get it. But you know what I think I'd like to have? What is it, please? Just a plain ordinary Bismarck herring. Ah, that's too bad. The only herring we got is what we call the Huey Long Herring. Huey Long Herring? What is that like?  How about Club Carl's special next up, Arthur Bugs Bear? How about? Sunday newspapers have been excitedly following the goings-on of a mythical place known as Rufftown. According to its creator, Arthur Bugs Bear, it's a pretty tough little bird. The local heavyweight champion, whose name is What A Man, has been looking for a match and has gotten a line on a guy named Glut, who's the White Hope from the neighboring town of Red Ears. We find What A Man and his manager talking things over. But maybe he's tough. Oh, I ain't worried, nun. He's a pushover for me. Yeah, I ain't so sure. You didn't look so good in that last fight with Bobby Dale. Oh, I ain't worried. Gluttleby duck soup. Well, just the same. I ain't letting you fight nobody's like giving to one's over. I ain't figuring on losing any fights.  What are you beefing about? What do you know about it? Did you ever see the guy? Well, I ain't really seen him, but I got a picture of him. You have? Where is it? Right here. Here. Look at that. Gee, he's plenty big. Hey, I think you better lay off for him. Say, that's a funny pose he's got there, ain't it? Does he always pose with his arms straight out from his sides that way? Sure. And his legs spread out like that? Sure.  That's right. And his eyes closed? Yeah. Hey, that's a funny background in that picture. What is that background anyway? That's how I know he's a pushover. That's the floor and a ring. First Sunday newspapers, a family. The updacks easily deserve the title of the world's goofiest family. And we're suddenly bitten by the own your own home bug. Today we find cousin Pete, cousin Tinkham and cousin Ella considering the subject from all angles. I see no reason why he shouldn't do it. Neither do I. If cousin Tinkham is tired of enjoying his peace of mind, am I right, think old boy?  If I remember the statement correct, I merely referred to your peace of mind. I made no mention whatsoever of your mental equilibrium. In fact, I didn't even know you were suffering from it. You should see a doctor at once. Oh, pardon me, but I'm afraid you boys are getting off the track. We were discussing the purchase of a home and not a medical attention. So we were, and I apologize for my digressing. Let me see. Where were we? Personally, I was on the verge of calling up my real estate agent and telling him to send over the papers. That's right. I remember now, and I was on the verge of restraining you from your folly. And just why do you consider it folly for cousin Tinkham to own his own home? For the very simple reason, my dear cousin Ella, that cousin Tinkham has never been blessed with a ne'er-do-well son. I'm afraid that I don't folly you cousin people. What may I ask? Has the lack of a ne'er-do-well son got to do with the subject? Well, for instance, if you had a home of your own, you would also have a mortgage. Very probably. And if you had a mortgage, the sheriff might drop around one day and foreclose. Well, that's possible. Well then, according to the very best fiction on the subject, you need a ne'er-do-well son. What for? To return from Australia and lift the mortgage. All aboard. Next stop, Will Rogers, all aboard. This time, the subject of that very mysterious region above the earth known as the stratosphere. We hear an aviator trying to explain it to an interested woman. It surrounds the earth. You mean it's an entirely separate layer? That's right. You might say that the first layer, the one next to the earth, is like our underclothes. Oh, how interesting. And the stratosphere is a second layer like our outside clothing. Oh, I see what you mean. But I guess you couldn't explain it that way to everybody, could you? Oh, I think practically anyone would understand anything as simple as that. Oh, but how about a muted? Well, maybe you're right. Tell me, how high is the stratosphere? Oh, roughly speaking, it begins about eight miles up.  Well, I've never been that high myself, but it must be very strange. For one thing, you can't see the earth at all. Oh, my goodness. I guess that's why you never hear of a parachute jumper jumping from the stratosphere. No, you never do, but someday someone may have nerve enough to do it. But I don't see how they could. You know, even if they want to do it. Yeah, how do you mean? Well, you say you can't even see the earth from way, way up there. That's right. Then how would you know which way to jump? Oh, yeah. Club Carl's special next stop, Milk Growth. All aboard. This is his boss. In fact, whatever the boss does, Joe has to do. The other day, Joe heard him talking about a new necktie he bought, so he bought one on the sudden impulse. First time Joe passed a men's clothing shop, he stepped right in and headed for the necktie counter, and that's where we find him right now. Sorry. Certainly, sir.  Well, have you got one with polka dots? Right here, sir. How is this one? That's fine. Can I try it on? No, no trouble at all. Just remove the one that you're wearing now. All right, there. This is very simple to put on. Let me show you how it goes. It just snaps right on on your collar button. Just like that. How's that? Gosh, for goodness sakes. What's the matter? Well, it looks kind of funny. Oh, I see what you're doing. It's the shirt that you've got on. It's all wrong for that time. Here, try this shirt on. Try it on over your other one. All right. How's that? Now, look in the mirror. Gosh, for goodness sakes. What's the matter? Well, it still looks kind of funny. It does? Oh, I see what's the matter now. It's your hat. That hat is all wrong without shirt and tie. Oh, is that what it is? Yes, it's not that about it. Now, just let me have that baby of yours and put this straw hat on. All right, if you say so. What do you think of that? Well, if you don't mind, I still think it looks kind of funny. You all right? Oh, now I see what the whole trouble is. There's just one thing spoiling their whole effect. What's that? It's your face. What's that? The Club Car Special has come to the end of another trip on the air. While the journey has been brief, you will find a longer session of fun and nonsense by these same writers and others in the March of Events and City Life section of the Hearst Sunday Newspapers. Think of it, long articles each chuck full of laughs written by the world's leading humorists. No other newspaper presents such a list of names. O. O. McIntyre, Will Rogers, Bugs Bear, Milt Gross, Sam Hellman, and others. There are laugh-provoking cartoons too. Keep up with the best humor of the day just as millions of readers do each Sunday. The Club Car Special, a program built upon the articles of these foremost writers of comedy, will be ready to arrive at your home next week at this same time over the same station. Be sure to meet it and enjoy another 15 minutes of original comedy.


The loop dashboard is the landing page you see when you log in, and we've made several key improvements to it. You can change how your courses are displayed. You have three choices. Display your courses as pictures, as illustrated here. Display them as a list, or indeed display them by summary. We've also added a recently accessed block. This will display the three most recent courses you've accessed. Furthermore, we have a recently accessed item, and this will display the last item that you were in within one of your courses. We've brought back the ability to favor the course, and now it's possible to filter the modules just so you can see your favorites. Equally, you can hide modules. For examples, modules may appear on your dashboard because you are the coordinator, but don't actually teach on that module. Now you can hide us to help declutter your dashboard. The timeline block, which allows students to see all of their deadlines from across all of their programs, has been given a more moderate feel. Furthermore, students can now see their deadlines by the next seven days, 30 days, three months, and indeed six months. Returning to the dashboard, you will see that we've also added four shortcuts for each module on your dashboard to make life a little easier for you. For example, if you see the course here, Biology 101, each of the four icons bring you into specific features on this course. For example, one icon brings you to the course page with editing turned on, the second icon to get a list of participants on the course pages, the third one to do discussion forms. And once you're in the course, you can enter these pages in the usual way. These shortcuts through the icons are just for your convenience. Finally, there's a search bar up the top of your course page. And in previous years, you could only search for course titles, whereas now you can search for items within your courses. For example, if I search for marketing, it will come up with all of the content where marketing is mentioned. And it will tell you not only the actual content, but what course it's in. These are just a selection of the improvements that are due out for the academic year. And news on further development will be shared in coming weeks.