NONPARAMETRIC FILTERING OF THE REALIZED SPOT VOLATILITY: A KERNEL-BASED APPROACH

A kernel weighted version of the standard realised integrated volatility es- timator is proposed. By different choices of the kernel and bandwidth, the measure allows us to focus on specific characteristics of the volatility process. In particular, as the bandwidth vanishes, an estimator of the realised spot volatility is obtained. We denote this the filtered spot volatility. We show con- sistency and asymptotic normality of the kernel smoothed realised volatility and the filtered spot volatility. The choice of bandwidth is discussed and data- driven selection methods proposed. A simulation study examines the finite sample properties of the estimators.

[1]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[2]  E. Barucci,et al.  On measuring volatility of diffusion processes with high frequency data , 2002 .

[3]  N. Shephard,et al.  Power and bipower variation with stochastic volatility and jumps , 2003 .

[4]  R. Renò NONPARAMETRIC ESTIMATION OF THE DIFFUSION COEFFICIENT OF STOCHASTIC VOLATILITY MODELS , 2008, Econometric Theory.

[5]  Dimitris Bertsimas,et al.  When is Time Continuous? , 1998 .

[6]  Michael W. Brandt,et al.  Range-Based Estimation of Stochastic Volatility Models , 2001 .

[7]  T. Bollerslev,et al.  ANSWERING THE SKEPTICS: YES, STANDARD VOLATILITY MODELS DO PROVIDE ACCURATE FORECASTS* , 1998 .

[8]  Lan Zhang,et al.  Inference for volatility-type objects and implications for hedging , 2008 .

[9]  S. Heston A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options , 1993 .

[10]  Jianqing Fan,et al.  Variable Bandwidth and Local Linear Regression Smoothers , 1992 .

[11]  R. Renò Nonparametric estimation of stochastic volatility models , 2006 .

[12]  Uniform Convergence Rates of Kernel Estimators with Heterogenous, Dependent Data , 2008 .

[13]  B. Werker,et al.  Closing the GARCH gap: Continuous time GARCH modeling , 1996 .

[14]  C. Chu,et al.  Kernel-Type Estimators of Jump Points and Values of a Regression Function , 1993 .

[15]  Jianqing Fan,et al.  Aggregation of Nonparametric Estimators for Volatility Matrix , 2007, math/0701107.

[16]  N. Shephard,et al.  Econometric analysis of realized volatility and its use in estimating stochastic volatility models , 2002 .

[17]  Peter C. B. Phillips,et al.  Fully Nonparametric Estimation of Scalar Diffusion Models , 2001 .

[18]  Tim Bollersleva,et al.  Estimating stochastic volatility di#usion using conditional moments of integrated volatility , 2002 .

[19]  N. Shephard,et al.  Markov chain Monte Carlo methods for stochastic volatility models , 2002 .

[20]  C. S. Jones The dynamics of stochastic volatility: evidence from underlying and options markets , 2003 .

[21]  N. Shephard,et al.  Realised power variation and stochastic volatility models , 2003 .

[22]  Giuseppe Cavaliere Stochastic Volatility: Selected Readings , 2006 .

[23]  T. Bollerslev,et al.  Intraday periodicity and volatility persistence in financial markets , 1997 .

[24]  Constance Van Eeden,et al.  Mean integrated squared error of kernel estimators when the density and its derivative are not necessarily continuous , 1985 .

[25]  D. Florens-zmirou On estimating the diffusion coefficient from discrete observations , 1993, Journal of Applied Probability.

[26]  P. Hall Large Sample Optimality of Least Squares Cross-Validation in Density Estimation , 1983 .

[27]  Alan G. White,et al.  The Pricing of Options on Assets with Stochastic Volatilities , 1987 .

[28]  T. Bollerslev,et al.  Estimating Stochastic Volatility Diffusion Using Conditional Moments of Integrated Volatility , 2001 .

[29]  O. Scaillet,et al.  CONSISTENCY OF ASYMMETRIC KERNEL DENSITY ESTIMATORS AND SMOOTHED HISTOGRAMS WITH APPLICATION TO INCOME DATA , 2005, Econometric Theory.

[30]  Daren B. H. Cline,et al.  Kernel Estimation of Densities with Discontinuities or Discontinuous Derivatives , 1991 .

[31]  Rohana J. Karunamuni,et al.  On kernel density estimation near endpoints , 1998 .

[32]  Songxi Chen BETA KERNEL SMOOTHERS FOR REGRESSION CURVES , 2000 .

[33]  Neil Shephard,et al.  Power variation and stochastic volatility: a review and some new results , 2004, Journal of Applied Probability.

[34]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[35]  Bert van Es Asymptotics for Least Squares Cross-Validation Bandwidths in Nonsmooth Cases , 1992 .

[36]  A. Lunde,et al.  Wavelet Estimation of Integrated Volatility , 2003 .

[37]  N. Shephard,et al.  LIMIT THEOREMS FOR BIPOWER VARIATION IN FINANCIAL ECONOMETRICS , 2005, Econometric Theory.

[38]  Irène Gijbels,et al.  Understanding exponential smoothing via kernel regression , 1999 .

[39]  M. Yor,et al.  Continuous martingales and Brownian motion , 1990 .

[40]  T. Björk Arbitrage Theory in Continuous Time , 2019 .

[41]  Dean P. Foster,et al.  Continuous Record Asymptotics for Rolling Sample Variance Estimators , 1994 .

[42]  T. Andersen,et al.  Estimating continuous-time stochastic volatility models of the short-term interest rate , 1997 .

[43]  Mark Broadie,et al.  Exact Simulation of Stochastic Volatility and Other Affine Jump Diffusion Processes , 2006, Oper. Res..

[44]  P. Protter Stochastic integration and differential equations , 1990 .

[45]  Peihua Qiu,et al.  Jump-Preserving Regression and Smoothing using Local Linear Fitting: A Compromise , 2007 .

[46]  R. C. Merton,et al.  On Estimating the Expected Return on the Market: An Exploratory Investigation , 1980 .

[47]  D. Talay Numerical solution of stochastic differential equations , 1994 .

[48]  V. A. Epanechnikov Non-Parametric Estimation of a Multivariate Probability Density , 1969 .

[49]  Hans-Georg Müller,et al.  Smooth optimum kernel estimators near endpoints , 1991 .

[50]  F. Diebold,et al.  The Distribution of Realized Exchange Rate Volatility , 2000 .

[51]  E. Stein,et al.  Stock Price Distributions with Stochastic Volatility: An Analytic Approach , 1991 .

[52]  Estimation of Local Smoothness Coefficients for Continuous Time Processes , 2002 .

[53]  N. Shephard Stochastic Volatility: Selected Readings , 2005 .

[54]  N. Shephard,et al.  Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics , 2004 .

[55]  Neil Shephard,et al.  Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise , 2008 .

[56]  Thomas Mikosch,et al.  Stock Market Risk-Return Inference. An Unconditional, Non-Parametric Approach. , 2003 .

[57]  N. Shephard,et al.  Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise , 2006 .

[58]  N. Shephard,et al.  Power variation & stochastic volatility: a review and some new results , 2003 .

[59]  W. Härdle,et al.  How Far are Automatically Chosen Regression Smoothing Parameters from their Optimum , 1988 .

[60]  N. Shephard,et al.  Power Variation and Time Change , 2006 .

[61]  Francis X. Diebold,et al.  Modeling and Forecasting Realized Volatility , 2001 .

[62]  P. Mykland,et al.  ANOVA for diffusions and Itô processes , 2006, math/0611274.