This paper examines the properties of the variance risk premium (VRP). We propose a flexible asset pricing model that captures co-jumps in prices and volatility, and self-exciting jump clustering. We estimate the model on equity returns and variance swap rates at different horizons. The total VRP is negative and has a downward-sloping term structure, while its jump component displays an upward-sloping term structure. The abrupt and persistent response of the short-term jump VRP to extreme events makes this specific premium a proxy for investors' fear of a market crash. Furthermore, the use of the VRP level and slope, and of its components, helps improve the short-run predictability of equity excess returns.

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We propose a direct and robust method for quantifying the variance risk premium on financial assets. We show that the risk-neutral expected value of return variance, also known as the variance swap rate, is well approximated by the value of a particular portfolio of options. We propose to use the difference between the realized variance and this synthetic variance swap rate to quantify the variance risk premium. Using a large options data set, we synthesize variance swap rates and investigate the historical behavior of variance risk premiums on five stock indexes and 35 individual stocks.

The equity variance risk premium is the expected compensation earned for selling variance risk in equity markets. The variance risk premium is positive and shows moderate persistence. High variance risk premiums coincide with the left tail of the consumption growth distribution shifting down. These facts, together with a positive, yet moderate, difference between the risk-neutral entropy and variance of the aggregate market return, refute the bulk of the extant consumption-based asset pricing models. We introduce a tractable habit model that does fit the data. In the model, the variance risk premium depends positively (negatively) on "bad" ("good") consumption growth uncertainty.

We study the dynamics and cross-sectional properties of the variance risk premia embedded in options on stocks and indices, approximated by the synthetic variance swap returns. Several important stylized facts and contributions arise. First, variance risk premia for indices are systematically larger (more negative) than for individual securities. Second, there are systematic cross-sectional differences in the price of variance in individual stocks. Linking variance swaps to firm size/book-to-market, and stock turnover characteristics, an investor gains access to several lucrative long-short strategies with Sharpe Ratios around 2.85. Third, principal component analysis reveals at most one important factor driving both stock and variance swap returns, which corresponds to the traditional market factor. For the remainder of the dynamics, the stock and its variance processes are nearly linearly independent. Fourth, we find the leverage effect through analysis of the relationship between the variance risk premium and stock to variance correlation. The systematic (market factor) part of the leverage effect provides additional evidence of the existence of one factor common to both variance swaps and stocks, but the contribution of the market risk premium to the total variance premium is very small. These findings stress the importance of using variance-based instruments in the portfolio of an investor.