Information Uncertainty, Volatility Term Structure and Index Option Returns

In this paper, we explore the relation between information uncertainty and S&P 500 index option returns. Since underlying state variable affecting economy is unobservable, investors have to obtain their own estimations based on available information. During such procedure, it is inevitable that their results are contaminated by various kinds of noise signals. Therefore, investors cannot be 100% confident about the their estimations. We model such phenomena through incorporating investors' learning behavior into an equilibrium stochastic volatility model. In the model, we introduce noise signals as a stochastic process independent with economic fundamentals. Such information uncertainty is able to generate time-varying volatility for stock returns, even when volatility of economic fundamental is constant. As a source of risk, for investors with recursive preference, it is priced and is able to explain variance premium and cross-section index option returns. In order to test the model implication, empirically, we construct several proxies for information uncertainty. Consistent with model intuition, we show that information uncertainty as a systematic risk factor is able to explain variance premium term structure and has better performance to explain cross-section index option returns than traditional symmetric risk factors such as volatility and jump