We study whether prices of traded options contain information about future extreme market events. Our option-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and probability of the market crashes, beyond and above...

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...

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) emerges as a strong predictor of both excess returns and realized variance. This is true both in- and out-of-sample....

We consider the frequency and correlation of extreme return observations or “jumps” across equities, Treasury bonds, corporate bonds, currencies, commodities, and real estate. Understanding more about jumps is important to investors as diversification across asset classes is diminished if...

We study whether option-implied conditional expectation of market loss due to tail events, or tail loss measure, contains information about future returns, especially the negative ones. Our tail loss measure predicts future market returns, magnitude, and probability of the market crashes, beyond...

We propose a novel model-free approach to obtain the joint risk-neutral distribution among several assets that is consistent with market prices of options on these assets and their weighted index. In an empirical application, we use options on the S&P 500 index and its nine industry sectors. The...