Using the approach of Ghysels, Santa-Clara, and Valkanov (2005), after correcting a coding error pointed out to us, we find that the Merton model holds over samples that exclude financial crises, in particular the Great Depression and/or the subprime mortgage financial crisis and the resulting Great Recession. We find that a simple flight to safety indicator separates the traditional risk-return relationship from financial crises which amount to fundamental changes in that relationship.
We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility.
The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data.
We examine whether the contribution of firm-level accounting earnings to the informativeness of the aggregate is tilted towards earnings with specific financial reporting characteristics. Specifically, we investigate whether considering the smoothness of firm-level earnings increases the informativeness of aggregate earnings for future real GDP, and if so, whether macroeconomic forecasters use this information efficiently. Using recently-developed mixed data sampling methods, we find that the aggregate is tilted towards firms with smoother earnings and that this composition of aggregate earnings outperforms traditional weighting schemes.
We evaluate the performance of two popular systemic risk measures, CoVaR and SRISK, during eight financial panics in the era before FDIC insurance. Bank stock price and balance sheet data were not readily available for this time period. We rectify this shortcoming by constructing a novel dataset for the New York banking system before 1933.
This paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York.