The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real-time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.

The main goal of this paper is to study the cross-sectional pricing of market volatility. The paper proposes that the market return, diffusion volatility, and jump volatility are fundamental factors that change the investors’ investment opportunity set. Based on estimates of diffusion and jump volatility factors using an enriched dataset including S&P 500 index returns, index options, and VIX, the paper finds negative market prices for volatility factors in the cross-section of stock returns. The findings are consistent with risk-based interpretations of value and size premia and indicate that the value effect is mainly related to the persistent diffusion volatility factor, whereas the size effect is associated with both the diffusion volatility factor and the jump volatility factor. The paper also finds that the use of market index data alone may yield counter-intuitive results.

This paper investigates the risk-return trade-off by taking into account the model specification problem. Market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model implies Merton’s ICAPM in the absence of leverage effects, whereas the return–volatility relations are determined by interactions between risk premia and leverage effects in the presence of leverage effects. Empirically, I find a robust negative relationship between the expected excess return and the jump volatility and a robust negative relationship between the expected excess return and the unexpected diffusion volatility. The latter provides an indirect evidence of the positive relationship between the expected excess return and the diffusion volatility.

We develop a multivariate credit risk model that accounts for joint defaults of banks and allows us to disentangle how much of banks’ credit risk is systemic. We find that the United States and United Kingdom differ not only in the evolution of systemic risk but, in particular, in their banks’ systemic exposures. In both countries, however, systemic credit risk varies substantially, represents about half of total bank credit risk on average, and induces high risk premia. The results suggest that sovereign and bank systemic risk are particularly interlinked in the United Kingdom.

We find a positive relationship between individual stocks’ implied variance asymmetry, defined as the difference between upside and downside risk-neutral semivariances extracted from out-of-money options, and future stock returns. The high-minus-low hedge portfolio earns the excess return of 0.90% (0.67%) per month in equal-weighted (value-weighted) returns. We show that implied variance asymmetry provides a neat measure of risk-neutral skewness and outperforms the standard risk-neutral skewness in predicting the cross-section of future stock returns. Risk-based equilibrium asset pricing models can not explain such a positive relationship, which instead can be potentially explained by information asymmetry and informed trading.

Using model-free skewness measures that exploit the asymmetry in semivariances and option data from the over-the-counter currency market, we find that buying currencies with a high skewness risk premium (SRP) and selling currencies with a low SRP generates high returns and a Sharpe ratio. Asset pricing tests—which control for omitted variables and measurement errors—show that a SRP factor enters the currency pricing kernel and is central to the pricing of risks inherent in a broad currency cross section of 60 portfolio excess returns. These results imply that skewness risk is a strong and priced source of currency risk.

Published in Review of Financial Studies https://doi.org/10.1093/rfs/hhu078

We find a strong and robust positive relationship between individual downside variance premia (DVP)–the difference between risk-neutral and physical expected downside variances–and future corporate bond returns. The spread portfolio that longs the high DVP bond portfolio and shorts the low DVP bond portfolio earns a statistically significant excess return of 0.37% (0.42%) per month in value- (equal-)weighted returns. The alpha estimates from various factor models remain statistically significant and economically substantial. The predictive power of the downside variance premium is stronger in noninvestment-grade (long-maturity) corporate bonds than in investment-grade (short-maturity) bonds. We show that the downside variance premium positively relates to the likelihood of future default and cash flow uncertainty and negatively relates to future cash flows.