This paper uses a disaggregated approach to study the volatility of common stocks at the market, industry, and firm levels. Over the period 1962–1997 there has been a noticeable increase in firm-level volatility relative to market volatility. Accordingly, correlations among individual stocks and the explanatory power of the market model for a typical stock have declined, whereas the number of stocks needed to achieve a given level of diversification has increased. All the volatility measures move together countercyclically and help to predict GDP growth. Market volatility tends to lead the other volatility series. Factors that may be responsible for these findings are suggested.

Using high-frequency data on deutschemark and yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only model-free, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of long-memory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation.

Numerous studies report that standard volatilitymodelshavelow explanatorypower, leading some researchers to question whether these models haveeconomic value.We examine thisquestion by using conditional mean-variance analysis to assess the value of volatility timing to short-horizon investors. We find that the volatilitytiming strategies outperform the unconditionally efficient static portfolios that have the same targetexpected return and volatility. This finding is robust to estimation risk and transaction costs. January 19, 2000 *Fleming and Ostdiek are at Rice University and Kirbyis at the AustralianGraduate School of Management. This paper was completed while Kirbywas at the UniversityofTexas at Dallas. We have benefitted from manyhelpful comments byRen'e Stulz (the editor), an anonymous referee, Amir Barnea, Jay Coughenour, Rob Engle, Wayne Ferson, John Graham,David Hsieh, David Ikenberry, Ravi Jagannathan, Andrew Karolyi, Uri Loewenstein, Jack Mosevich, Ehud Ronn, Paul Seguin, ...

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We propose extensions of the continuous record asymptotic analysis for rolling sample variance estimators developed by Foster and Nelson (1996) for estimating the quadratic variation of asset returns, which is also referred to as integrated or realized volatility. The new approach treats integrated volatility as a continuous time stochastic process sampled at high frequencies and suggests rolling sample estimators which share many features with spot volatility estimators.

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Ito processes are the most common form of continuous semimartingales, and include diffusion processes. The paper is concerned with the nonparametric regression relationship between two such Ito processes. We are interested in the quadratic variation (integrated volatility) of the residual in this regression, over a unit of time (such as a day). A main conceptual finding is that this quadratic variation can be estimated almost as if the residual process were observed, the difference being that there is also a bias which is of the same asymptotic order as the mixed normal error term. The proposed methodology, “ANOVA for diffusions and Ito processes”, can be used to measure the statistical quality of a parametric model, and, nonparametrically, the appropriateness of a one-regressor model in general. On the other hand, it also helps quantify and characterize the trading (hedging) error in the case of financial applications.

The econometric literature of high frequency data often relies on moment estimators which are derived from assuming local constancy of volatility and related quantities. We here study this local-constancy approximation as a general approach to estimation in such data. We show that the technique yields asymptotic properties (consistency, normality) that are correct subject to an ex post adjustment involving asymptotic likelihood ratios. These adjustments are given. Several examples of estimation are provided: powers of volatility, leverage effect, integrated betas, bipower, and covariance under asynchronous observation. The first order approximations in this study can be over the period of one observation, or over blocks of successive observations. The advantage of blocking is a gain in transparency in defining and analyzing estimators. The theory relies heavily on the interplay between stable convergence and measure change, and on asymptotic expansions for martingales.

We present an econometric procedure for calibrating no-arbitrage term structure models in a way that is time-consistent and robust to measurement errors. Typical no-arbitrage models are time-inconsistent because their parameters are assumed constant for pricing purposes despite the fact that the parameters change whenever the model is recalibrated. No-arbitrage models are also sensitive to measurement errors because they fit exactly each potentially contaminated bond price in the cross-section. We overcome both problems by evaluating bond prices using the joint dynamics of the factors and calibrated parameters and by locally averaging out the measurement errors. Our empirical application illustrates the trade-off between fitting as well as possible and overfitting the cross-section of bond prices due to measurement errors. After optimizing this trade-off, our approach fits almost exactly the cross-section of bond prices at each date and produces out-of-sample forecast errors that beat a random walk benchmark and are comparable to the results in the affine term structure literature. We find that non-linearities in the pricing kernel are important, lending support to quadratic term structure models.