We investigate whether: (i) co-skewness and co-kurtosis are significant factors in modeling hedge fund (HF) returns, (ii) HF return volatility displays clusters, asymmetry and shock persistence, (iii) volatility clusters of HF styles drive volatility clusters of one another, major asset classes, and major banking organizations, (iv) HF return and volatility patterns changed after the financial crises of 1998 and 2007–2009. A higher-moment EGARCH model and monthly data over January 1993–April 2014 period on 13 HF styles are employed. Out-of-sample forecasts are generated over the period of May 2014–April 2016. Results show: (i) most of the co-skewness and co-kurtosis coefficients are statistically significant, strongly supporting the higher-moment return generating models; (ii) there is strong evidence in favor of EGARCH specification, volatility clustering, asymmetry, and shock persistence; (iii) there were distinct effects on the returns and volatilities of HFs during the 1998 Russian bond crisis, Long-Term Capital Management crisis, and the 2007–2009 financial crisis; and (iv) shocks to volatility clusters of a HF style do spillover to other HF styles, major banking firms, and key asset classes. Our findings have major implications for regulators, investors, HF managers and hedging strategists.

We propose a new credit variable called probability of aggregate loan trend deviations (pltd) that forecasts crash risk in U.S. bank stock returns. Compared to more classic measures of credit, the main innovation in pltd is that it incorporates a large amount of macroeconomic information in the form of money and interest rate variables known to drive the aggregate loan supply. Positive changes in pltd increase the level of systemic risk in the economy and also trigger lower bank returns and cash flows up to horizons of one year. To disentangle the effect our credit variable has on future bank returns we break down the total variance into individual components and estimate series for news of discount-rates and cash flows. We find that pltd only leads the cash-flow news component without significant effects on the discount-rate news. Finally, we show that pltd is not only useful for predicting the mean of future bank stock returns in-sample and out-of-sample but is also an important driver of the tail of the return distribution proxied by CATFIN.

The objective of this paper is to employ the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the effect of interest rate and its volatility on the bank stock return generation process. This framework discards the restrictive assumptions of linearity, independence, and constant conditional variance in modeling bank stock returns. The model presented here allows for shifts in the volatility equation in response to the changes in monetary policy regime in 1979 and 1982 to be estimated. ARCH, GARCH, and volatility feed back effects are found to be significant. Interest rate and interest rate volatility are found to directly impact the first and the second moments of the bank stock returns distribution, respectively. The latter also affects the risk premia indirectly. The degree of persistence in shocks is substantial for all the three bank portfolios and sensitive to the nature of the bank portfolio and the prevailing monetary policy regime.

We examine the risk and return linkages across US commercial banks, securities firms, and life insurance companies during the 1991–2001 period. After controlling for changes in the broader stock market, interest rates, and foreign currency values, we find that return and risk interdependencies across these financial firms are significant and size-varying; larger institutions display stronger volatility transmission linkages, while smaller ones exhibit more prominent return-related linkages. The tighter link in risk among large financial institutions (FIs) suggests stronger convergence, employment of common models of risk measurement and risk management, and more intense inter-industry competition, particularly between large banks and large securities firms, compared to smaller institutions. Lack of risk spillover among smaller FIs confirms the intuition that they typically assume more localized and idiosyncratic risk. The co-movement of stock returns among smaller FIs has been helped by the effects of locally based factors, such as economic conditions and state regulations, on all such institutions, and a less diversified product set. Differences in spillover patterns between large and smaller institutions have implications on investment choices and mergers and acquisitions in the industry. Introduction of the Gramm-Leach-Bliley Act (1999) has had dissimilar effects on the riskiness of large versus smaller life insurance and securities firms, and an insignificant effect on commercial banks.