Chardin Wese Simen

Variance Risk Premia in Commodity Markets

We use a large panel of commodity option prices to study the market price of variance risk. We construct synthetic variance swaps and find significantly negative variance risk premia in nearly all commodity markets. An equally-weighted portfolio of short commodity variance swaps earns an annualized Sharpe Ratio of around 40%. We document increasing comovements across bonds, commodities and equity variance swap returns, suggesting that the variance swap markets are increasingly integrated. Finally, we show that commodity variance risk premia are distinct from price risk premia, indicating that variance risk is unspanned by commodity futures.

The Term Structure of Systematic and Idiosyncratic Risk

We study the term structure of variance (total risk), systematic, and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little indication of a time-varying term premium. Turning the focus to individual stocks, we cannot reject the expectations hypothesis for systematic variance, but we strongly reject it for idiosyncratic variance. Our results are robust to jumps and potential statistical biases.

International Tail Risk and World Fear

We examine the pricing of tail risk in international stock markets. Studying all MSCI Developed and Emerging Markets countries, we find that the tail risk of these countries is highly integrated. We find that both local and our newly computed global tail risk strongly predict global equity index excess returns. These results hold both in-sample and out-of-sample. Sorting countries into portfolios by their tail risk generates sizable excess returns across various holding periods. Finally, we find that global tail risk is linked to international economic activity.

Predicting the Equity Market with Option-Implied Variables

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.

The Risk Premium of Gold

This paper examines the properties of the gold risk premium. We estimate a parsimonious model for the gold risk premium and uncover important time variations in the dynamics of the risk premium. We also estimate the risk premia of the stock and bond markets and investigate their co-movements. The results show that the co-movements of expected gold returns with expected returns of stocks and bonds are positive, while co-movements of realized returns are zero or negative on average. This results holds not only during normal market periods, but also in times of market stress. Furthermore, we find no significant co-movement of expected and realized returns of gold with inflation.

Estimating Beta: Forecast Adjustments and the Impact of Stock Characteristics for a Broad Cross-Section

Researchers and practitioners face many choices when estimating an assets sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as a shrinkage toward the industry average yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors. Finally, we document a robust link between stock characteristics and beta predictability.Researchers and practitioners face many choices when estimating an assets sensitivities toward risk factors, i.e., betas. We study the effect of different data sampling frequencies, forecast adjustments, and model combinations for beta estimation. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as a shrinkage toward the industry average yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors.

What Makes the Market Jump

Using intraday transaction prices and a non-parametric jump test, we show that jumps in the S&P 500 and VIX are low-probability, high-impact events. Extant research investigating the causes of jumps primarily focuses on scheduled macro-announcements. However, we find that unscheduled news, which has so far received little attention, triggers twice as many jumps and accounts for a larger proportion of the jump variation than scheduled news. Intriguingly, we show that close to 50% of jumps are not explained by fundamental news, revealing the presence of “excess jumps” in financial markets.