So far, however, estimates of the term premium have remained compressed, suggesting that the recent rise in ten-year US Treasury yields reflects, by and large, a reappraisal of the future expected path of short-term interest rates. [16] The literature points to three potential explanations as to why the impact of QT might be more muted. One is that QT is missing the signalling component of QE. [17] That is, while balance sheet expansions signal a lower-for-longer interest rate policy, balance sheet reductions may provide little, if any, information about the future path of short-term interest rates. The second reason is that the unwinding of QE is typically more gradual than the build-up in assets. [18] The third explanation is that QT often happens in an environment of improved market functioning. Indeed, one reason why QE was so effective at the outbreak of the pandemic is that it quickly improved liquidity and reduced volatility. [19] However, isolating the effect of QT is inherently difficult. Many different forces drive long-term yields. For example, purchases by non-resident investors of US Treasury securities accelerated sharply last year, likely reflecting growing interest rate differentials and the rise in uncertainty on the back of Russia’s unjustified war against Ukraine and its people (Slide 9). Foreign investors alone absorbed nearly 60% of the net supply of US Treasuries in 2022. Increased foreign demand may have offset, at least in part, the impact of the higher actual and expected bond supply from QT.
These dynamics are captured in the simple specification  where  indexes time,  are iid normal shocks and  is the indicator  function taking value 1 when and value otherwise. As reputational concerns disappear, the term goes to zero, leaving simple autoregressive dynamics. But with reputational externalities, productivity shocks become amplified by the credit cycle: a high realisation of in period could push the macro state above the risky policy threshold, at which point the dynamics of begin also to shape the real economy. The distribution of is assumed to have a higher mean and variance than to simulate the effects of the credit boom. In particular, we constrain the distribution of relative to such that expected risk-adjusted fundamentals are constant over time. 10 So we have, in effect, a regime-switching model. When banks play safe strategies, fundamentals are relatively stable. When banks risk up, they create a temporary improvement in expected fundamentals at the cost of greater volatility. This dynamic specification has some interesting properties. First, even small shocks can have disproportionately large effects on fundamentals depending on the initial conditions. In particular, if fundamentals happen to be close to the switching threshold, a further positive shock will lead all firms to coordinate on the high risk-taking strategy. That drives up both the expected value of fundamentals and its volatility. This generates a form of path dependence. Second, credit cycles are endogenous in this framework. As firms coordinate on high risk strategies, this drives up the expected value of fundamentals.