Conference Agenda

We study whether and how monetary policymakers may have contributed to inflate asset price bubbles and in general what are the potentially complex, non-linear linkages between short-term policy rates and the size and expected durations of equity bubbles. In particular, we extend empirical models of periodically collapsing, rational bubbles to test whether and to what extent the long cycle of rates at the zero lower bound and of quantitative easing policies may have increased the probability of bubbles inflating and persisting, with special emphasis on the US stock market. We find that the linkages between S&P returns and rate-based indicators of monetary policies contain evidence of recurring regimes that can be characterised as one of a persisting vs. one of a collapsing bubble. Moreover, the probabilities of financial markets transitioning from a bubble to a state of (partial) collapse turns out to depend on both the initial, relative size of the bubble and on monetary policy indicators. This implies that an easier (tighter) monetary policy will inflate (deflate) a bubble through a simple, regression-style effect, but also yield a non-linear, “concave” effect by which sufficiently low (high) rates are enough for a bubble to inflate (deflate) with high probability. Besides fitting the data, the resulting, parsimonious, regime switching models provide an accurate and economically valuable predictive performance, even when transaction costs are taken into account.

This paper examines how central bank solvency affects the transmission of global financial stress to domestic stock markets. We develop a novel solvency index using principal component analysis, incorporating profitability, capital endowment, and risk exposure. Using data from 46 countries from 2001 to 2023, asset pricing tests reveal that higher central bank solvency mitigates financial stress transmission, while weaker solvency leads to higher excess returns as risk compensation. Panel regressions identify transmission channels, including central bank independence, transparency, and financial system size, showing that lower solvency restricts the lender of last resort function, amplifying financial stress in vulnerable economies.

This study investigates the predictive power of weekend cryptocurrency returns on Monday stocks returns.Using a Bayesian regression framework and Kalman filter, we assess how cryptocurrency weekend returns, volatility, and volume affect Monday’s stock returns. Our findings reveal that negative weekend cryptocurrency returns and volatility predict the stock market performance on Mondays, an effect that emerged after the LUNA crash in mid-2022. These results have practical implications for short-selling strategies, portfolio risk management, and policymakers, highlighting the growing interconnection between cryptocurrency and traditional markets.