We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

This paper analyzes the implications of autoregressive betas in single factor models for the statistical properties of stock returns. It is demonstrated that this assumption alone is sufficient to account for the most important stylized facts of stock returns, namely conditional...

Stationary Increment Tempered Fractional Lévy Processes (TFLP) introduced by Boniece, Didier and Sabzikar (2020) are applied to financial data. They are used to model the stochastic drift rate of a mean reverting equation. The new processes are called OU processes with a TFLP drift rate....

In this paper, we investigate the dynamic response of stock market volatility to changes in monetary policy. Using a vector autoregressive model, our findings reveal a significant and asymmetric response of stock returns and volatility to monetary policy shocks. Although the increase in the...

We elaborate economic explanations for the time-varying risk of month, quarter and year base load electricity forward contracts traded on the Nord Pool Energy Exchange from January 2006 to March 2010. Daily risk quantities are generated by decomposing realized volatility in its continuous and...

We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and...

Accurately forecasting volatility is key in many financial applications. In this study, I suggest that individuals gather information online before implementing their trading decisions. In periods of higher investor concern, online information seeking intensifies. By analysing Google search data...

This paper replicates and extends the Amihud (2002) study that links liquidity to asset pricing. Using the current version of the CRSP dataset, we obtain essentially the same results that Amihud presents. The same methods applied to more recent data show a much weaker relation between liquidity...

This paper investigates a variety of features exhibited by the amplitude of stock returns. Some of these "stylized facts" have already attracted a great deal of attention from researchers, while some others have been documented only recently. - Horizontal dependence of volatility: Volatility is...

This paper examines long memory volatility in the cross-section of stock returns. We show that long memory volatility is widespread in the U.S. and that the degree of memory can be related to firm characteristics such as market capitalization, book-to-market ratio, prior performance and price...