Zhiwu Chen

Informational Content of Option Volume Prior to Takeovers

This paper examines the information embedded in both the stock and option markets prior to takeover announcements. During normal periods, buyer-seller initiated stock volume imbalances are significant predictors of next-day stock returns and option volume imbalances are uninformative. However, prior to takeover announcements, call volume imbalances are strongly positively related to next-day stock returns. Cross-sectional analysis shows that those takeover targets with the largest pre-announcement call-imbalance increases experience the highest announcement-day returns. The largest increase in buyer-initiated trading activity is in short-term out-of-the-money calls that subsequently experience the largest returns. Collectively, these findings are consistent with the hypothesis that, in the presence of pending extreme informational events, the options market plays an important role in price discovery.

Baby Boom, Population Aging, and Capital Markets

This article tests how demographic changes affect capital markets. The life-cycle investment hypothesis states that at an early stage an investor allocates more wealth in housing and then switches to financial assets at a later stage. Consequently, the stock market should rise but the housing market should decline with the average age, a prediction supported in the post-1945 period. The second hypothesis that an investors risk aversion increases with age is tested by estimating the resulting Euler equation and supported in the post-1945 period. A rise in average age is found to predict a rise in risk premiums. Copyright 1994 by University of Chicago Press.

Pricing and hedging long-term options

Recent empirical studies find that once an option pricing model has incorporated stochastic volatility, allowing interest rates to be stochastic does not improve pricing or hedging any further while adding random jumps to the modeling framework only helps the pricing of extremely short-term options but not the hedging performance. Given that only options of relatively short terms are used in existing studies, this paper addresses two related questions: Do long-term options contain different information than short-term options? If so, can long-term options better differentiate among alternative models? Our inquiry starts by first demonstrating analytically that differences among alternative models usually do not surface when applied to short term options, but do so when applied to long-term contracts. For instance, within a wide parameter range, the Arrow-Debreu state price densities implicit in different stochastic-volatility models coincide almost everywhere at the short horizon, but diverge at the long horizon. Using regular options (of less than a year to expiration) and LEAPS, both written on the S&P 500 index, we find that short- and long-term contracts indeed contain different information and impose distinct hurdles on any candidate option pricing model. While the data suggest that it is not as important to model stochastic interest rates or random jumps (beyond stochastic volatility) for pricing LEAPS, incorporating stochastic interest rates can nonetheless enhance hedging performance in certain cases involving long-term contracts.

Option Pricing and Hedging Performance Under Stochastic Volatility and Stochastic Interest Rates

Recent studies have extended the Black–Scholes model to incorporate either stochastic interest rates or stochastic volatility. But, there is not yet any comprehensive empirical study demonstrating whether and by how much each generalized feature will improve option pricing and hedging performance. This paper fills this gap by first developing an implementable option model in closed-form that admits both stochastic volatility and stochastic interest rates and that is parsimonious in the number of parameters. The model includes many known ones as special cases. Based on the model, both delta-neutral and single-instrument minimum-variance hedging strategies are derived analytically. Using S&P 500 option prices, we then compare the pricing and hedging performance of this model with that of three existing ones that respectively allow for (i) constant volatility and constant interest rates (the Black–Scholes), (ii) constant volatility but stochastic interest rates, and (iii) stochastic volatility but constant interest rates. Overall, incorporating stochastic volatility and stochastic interest rates produces the best performance in pricing and hedging, with the remaining pricing and hedging errors no longer systematically related to contract features. The second performer in the horse-race is the stochastic volatility model, followed by the stochastic interest rates model and then by the Black–Scholes.

Production-based asset pricing in Japan☆

Abstract This paper develops and tests, using data from Japan, a discrete-time production-based asset pricing model. We derive an Euler equation that jointly characterizes the firms optimal investment policy and investment returns with a stochastic discount factor. When firms can choose between financial investments on the stock market and real investments in production processes, stock market returns must be linked to real investment returns. Hence, stock price behavior should be related to the ultimate sources of business cycle risk such as technology shocks and adjustment costs, as production investments are. Based on the stock market and investment data from Japan and using the generalized method of moments, we find supportive evidence for the production-based asset pricing model. Specifically, Japanese stock returns and investment returns are forecastible from the same set of information variables, and a single latent variable model representation for investment returns and stock market returns is not rejected. However, forecasts of future real activity are more accurate using stock market data than using investment returns.

Cash Flow Risk, Discounting Risk, and the Equity Premium Puzzle

Abstract This article investigates the impact of cash flow risk and discounting risk on the aggregate equity premium. Our approach is based on the idea that consumption is hard to measure empirically, so if we substitute out an empirically difficult-to-estimate marginal utility by a pricing kernel of observables, we can evaluate the empirical performance of an equilibrium asset pricing model in a different way. Once the pricing-kernel process is specified, we can endogenously solve for the equity premium, the price of the market portfolio, and the term structure of interest rates within the same underlying equilibrium. Embedded in the closed-form solution are compensations for cash flow risk and discounting risk. With the solution for the risk premium explicitly given, we then calibrate the model to evaluate its empirical performance. This approach allows us to avoid the impact of the unobservable consumption or market portfolio on inferences regarding the models performance. Our illustrative model is based on the assumption that aggregate dividend equals a fixed fraction of aggregate earnings plus noise, and the expected aggregate earnings growth follows a mean-reverting stochastic process. Moreover, the economy-wide pricing kernel is chosen to be consistent with (1) a constant market price of aggregate risk and (2) a mean-reverting interest rate process with constant volatility. Estimation results show that the framework can mimic the observed market equity premium.