Long memory and the relation between implied and realized volatility

We argue that the conventional predictive regression between implied volatility (regressor) and realized volatility over the remaining life of the option (regressand) is likely to be a fractional cointegrating relation. Since cointegration is associated with long-run comovements, this finding modifies the usual interpretation of such regression as a study towards assessing option market efficiency (given a certain option pricing model) and/or short-term unbiasedness of implied volatility as a predictor for realized volatility, thereby rendering the conventional tests invalid. We use spectral methods and exploit the long memory in the data to design an econometric methodology which is robust to the various issues that the literature on the relation between implied and realized volatility has proposed as plausible explanations for an estimated slope coefficient less than one, implying biasedness, in the standard predictive regression (measurement errors and presence of an unobservable time-varying risk premium, for instance). Even though little can be said about market efficiency and/or short-term unbiasedness, which were the objects of the previous studies, our evidence in favor of a long-run one-to-one correspondence between implied and realized volatility series is rather strong. Simulation results confirm our findings.

Nonparametric Stochastic Volatility

Using recent advances in the nonparametric estimation of continuous-time processes under mild statistical assumptions as well as recent developments on nonparametric volatility estimation by virtue of market microstructure noise-contaminated high-frequency asset price data, we provide (i) a theory of spot variance estimation and (ii) functional methods for stochastic volatility modelling. Our methods allow for the joint evaluation of return and volatility dynamics with nonlinear drift and diffusion functions, nonlinear leverage effects, jumps in returns and volatility with possibly state-dependent jump intensities, as well as nonlinear risk-return trade-offs. Our identification approach and asymptotic results apply under weak recurrence assumptions and, hence, accommodate the persistence properties of variance in finite samples. Functional estimation of a generalized (i.e., nonlinear) version of the square-root stochastic variance model with jumps in both volatility and returns for the S&P500 index suggests the need for richer variance dynamics than in existing work. We find a linear specification for the variance's diffusive variance to be misspecified (and inferior to a more flexible CEV specification) even when allowing for jumps in the variance dynamics.Spot variance, stochastic volatility, jumps in returns, jumps in volatility, leverage effects, risk-return trade-offs, kernel methods, recurrence, market microstructure noise.

Microstructure noise, realized volatility, and optimal sampling

Recorded prices are known to diverge from their "efficient" values due to the presence of market microstructure contaminations. The microstructure noise creates a dichotomy in the model-free estimation of integrated volatility. While it is theoretically necessary to sum squared returns that are computed over very small intervals to better identify the underlying quadratic variation over a period, the summing of numerous contaminated return data entails substantial accumulation of noise. Using asymptotic arguments as in the extant theoretical literature on the subject, we argue that the realized volatility estimator diverges to infinity almost surely when noise plays a role. While realized volatility cannot be a consistent estimate of the quadratic variation of the log price process, we show that a standardized version of the realized volatility estimator can be employed to uncover the second moment of the (unobserved) noise process. More generally, we show that straightforward sample moments of the noisy return data provide consistent estimates of the moments of the noise process. Finally, we quantify the finite sample bias/variance trade-off that is induced by the accumulation of noisy observations and provide clear and easily implementable directions for optimally sampling contaminated high frequency return data for the purpose of volatility estimationMicrostructure noise, realized volatility

Accelerated Asymptotics for Diffusion Model Estimation

We propose a semiparametric estimation procedure for scalar homogeneous stochastic differential equations. We specify a parametric class for the underlying diffusion process and identify the parameters of interest by minimizing criteria given by the integrated squared difference between kernel estimates of drift and diffusion function and their parametric counterparts. The nonparametric estimates are simplified versions of those in Bandi and Phillips (1998). A complete asymptotic theory for the semiparametric estimates is developed. The limit theory relies on infill and long span asymptotics and the asymptotic distributions are shown to depend on the chronological local time of the underlying diffusion process. The estimation method and asymptotic results apply to both stationary and nonstationary processes. As is standard with semiparametric approaches in other contexts, faster convergence rates are attained than is possible in the fully functional case. From a purely technical point of view, this work merges two strands of the most recent econometrics literature, namely the estimation of nonlinear models of integrated time-series [Park and Phillips (1999, 2000)] and the functional identification of diffusions under minimal assumptions on the dynamics of the underlying process [Florens-Zmirou (1993), Jacod (1997), Bandi and Phillips (1998) and Bandi (1999)]. In effect, the 'minimum distance' type of estimation that is presented in this paper can be interpreted as extremum estimation for potentially nonstationary and nonlinear continuous-time models.

A Simple Approach to the Parametric Estimation of Potentially Nonstationary Diffusions

A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic differential equations. We specify a parametric class of diffusions and estimate the parameters of interest by minimizing criteria based on the integrated squared difference between kernel estimates of the drift and diffusion functions and their parametric counterparts. The procedure does not require simulations or approximations to the true transition density and has the simplicity of standard nonlinear least-squares methods in discrete-time. A complete asymptotic theory for the parametric estimates is developed. The limit theory relies on infill and long span asymptotics and is robust to deviations from stationarity, requiring only recurrence.Diffusion, Drift, Local time, Parametric estimation, Semimartingale, Stochastic differential equation

A Simple Approach to the Parametric Estimation of Potentially Nonstationary Diffusions

A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic differential equations. We specify a parametric class of diffusions and estimate the parameters of interest by minimizing criteria based on the integrated squared difference between kernel estimates of the drift and diffusion functions and their parametric counterparts. The procedure does not require simulations or approximations to the true transition density and has the simplicity of standard nonlinear least-squares methods in discrete-time. A complete asymptotic theory for the parametric estimates is developed. The limit theory relies on infill and long span asymptotics and is robust to deviations from stationarity, requiring only recurrence

Past Market Variance and Asset Prices

Recent work in asset pricing has focused on market-wide variance as a systematic factor and on firm-specific variance as idiosyncratic risk. We study an alternative channel through which the variability of financial market returns may help our understanding of cross-sectional price formation in financial markets. Invoking the countercyclical nature of market variance, we allow the (stochastic) discounting of future cash-flows to depend on the level of past market variance (pmv). Employing pmv as a conditioning variable in a classical consumption-CAPM framework, we derive economically meaningful conditional factor loadings and conditional risk premia. We show that scaling by pmv may also yield more effective pricing results than scaling by successful, alternative variables (such as the consumption-to-wealth ratio) precisely at frequencies at which their predictive ability for excess market returns should be (in theory) and is (empirically) maximal, i.e., business-cycle frequencies.Asset prices, financial markets,

Fully Nonparametric Estimation of Scalar Diffusion Models

We propose a functional estimation procedure for homogeneous stochastic differential equations based on a discrete sample of observations and with minimal requirements on the data generating process. We show how to identify the drift and diffusion function in situations where one or the other function is considered a nuisance parameter. The asymptotic behavior of the estimators is examined as the observation frequency increases and as the time span lengthens (that is, we implement both infill and long span asymptotics). We prove consistency and convergence to mixtures of normal laws, where the mixing variates depend on the chronological local time of the underlying process, that is the time spent by the process in the vicinity of a spatial point. The estimation method and asymptotic results apply to both stationary and nonstationary processes.Diffusion, Drift, Infill asymptotics, Kernel density, Local time, Long span asymptotics, Martingale, Nonparametric estimation, Semimartingale, Stochastic differential equation

On the functional estimation of multivariate diffusion processes

We propose a fully nonparametric estimation theory for the drift vector and the diffusion matrix of multivariate diffusion processes. The estimators are sample analogues to infinitesimal conditional expectations constructed as Nadaraya-Watson kernel averages. Minimal assumptions are imposed on the statistical properties of the multivariate system to obtain limiting results. Harris recurrence is all that we require to show strong consistency and asymptotic (mixed) normality of the functional estimates. Hence, the estimation method and asymptotic theory apply to both stationary and nonstationary multivariate diffusion processes of the recurrent type

On the functional estimation of multivariate diffusion processes

We propose a fully nonparametric estimation theory for the drift vector and the diffusion matrix of multivariate diffusion processes. The estimators are sample analogues to infinitesimal conditional expectations constructed as Nadaraya-Watson kernel averages. Minimal assumptions are imposed on the statistical properties of the multivariate system to obtain limiting results. Harris recurrence is all that we require to show strong consistency and asymptotic (mixed) normality of the functional estimates. Hence, the estimation method and asymptotic theory apply to both stationary and nonstationary multivariate diffusion processes of the recurrent type