We use fractionally-integrated time-series models to investigate the joint dynamics of equity trading volume and volatility. Bollerslev and Jubinski (1999) show that volume and volatility have a similar degree of fractional integration, and they argue that this evidence supports a long-run view of the mixture-of-distributions hypothesis. We examine this issue using more precise volatility estimates obtained using high-frequency returns (i.e., realized volatilities). Our results indicate that volume and volatility both display long memory, but we can reject the hypothesis that the two series share a common order of fractional integration for a fifth of the firms in our sample. Moreover, we find a strong correlation between the innovations to volume and volatility, which suggests that trading volume can be used to obtain more precise estimates of daily volatility for cases in which high-frequency returns are unavailable.

Recent work suggests that intradaily returns can be used to construct estimates of daily return volatility that are more precise than those constructed using daily returns. We measure the economic value of this “realized” volatility approach in the context of investment decisions. Our results indicate that the value of switching from daily to intradaily returns to estimate the conditional covariance matrix can be substantial. We estimate that a risk-averse investor would be willing to pay 50 to 200 basis points per year to capture the observed gains in portfolio performance. Moreover, these gains are robust to transaction costs, estimation risk regarding expected returns, and the performance measurement horizon.

We investigate the nature of volatility linkages in the stock, bond, and money markets. We develop a simple model of speculative trading that predicts strong volatility linkages in these markets due to common information, which simultaneously affects expectations across markets, and information spillover caused by cross-market hedging. To measure these linkages, we estimate a stochastic volatility representation of our trading model using GMM. The results indicate that our specification explains many of the observed characteristics of the data, and that the volatility linkages between the three markets are indeed strong. Moreover, we find that the linkages have become stronger since the 1987 stock market crash.

Wildcard options are embedded in many derivative contracts. They arise when the settlement price of the contract is established before the time at which the wildcard option holder must declare his intention to make or accept delivery and the exercise of the wildcard option closes out the underlying asset position. This paper provides a simple method for valuing wildcard options and illustrates the technique by valuing the sequence of wildcard options embedded in the S&P 100 index option contract. The results show that wildcard options can account for an economically significant fraction of S&P 100 index option value.

ABSTRACT We find that trading‐ versus nontrading‐period variance ratios in weather‐sensitive markets are lower than those in the equity market and higher than those in the currency market. The variance ratios are also substantially lower during periods of the year when prices are most sensitive to the weather. Moreover, the comovement of returns and volatilities for related commodities is stronger during the weather‐sensitive season, largely due to stronger comovement during nontrading periods. These results are consistent with a strong link between prices and public information flow and cannot be explained by pricing errors or changes in trading activity.

ABSTRACT Numerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional meanm‐variance analysis to assess the value of volatility timing to short‐horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs.

Derman and Kani (1994), Dupire (1994), and Rubinstein (1994) hypothesize that asset return volatility is a deterministic function of asset price and time, and develop a deterministic volatility function (DVF) option valuation model that has the potential of fitting the observed cross section of option prices exactly. Using S&P 500 options from June 1988 through December 1993, we examine the predictive and hedging performance of the DVF option valuation model and find it is no better than an ad hoc procedure that merely smooths Black–Scholes (1973) implied volatilities across exercise prices and times to expiration.

ABSTRACT Wildcard options are embedded in many derivative contracts. They arise when the settlement price of the contract is established before the time at which the wildcard option holder must declare his intention to make or accept delivery and the exercise of the wildcard option closes out the underlying asset position. This paper provides a simple method for valuing wildcard options and illustrates the technique by valuing the sequence of wildcard options embedded in the S&P 100 index (OEX) option contract. The results show that wildcard options can account for an economically significant fraction of OEX option value.

Wildcard options are embedded in many derivative contracts. They arise when the settlement price of the contract is established before the time at which the wildcard option holder must declare his intention to make or accept delivery and the exercise of the wildcard option closes out the underlying asset position. This paper provides a simple method for valuing wildcard options and illustrates the technique by valuing the sequence of wildcard options embedded in the S&P 100 index (OEX) option contract. The results show that wildcard options can account for an economically significant fraction of OEX option value.

Derman and Kani (1994), Dupire (1994), and Rubinstein (1994) hypothesize that asset return volatility is a deterministic function of asset price and time, and develop a deterministic volatility function (DVF) option valuation model that has the potential of fitting the observed cross section of option prices exactly. Using S&P 500 options from June 1988 through December 1993, we examine the predictive and hedging performance of the DVF option valuation model and find it is no better than an ad hoc procedure that merely smooths Black–Scholes (1973) implied volatilities across exercise prices and times to expiration.