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Parametric and Nonparametric Volatility Measurement Author info | Abstract | Publisher info | Download info | Related research | Statistics Torben G. Andersen
Tim Bollerslev
Francis X. Diebold
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Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the ex-post sample-path return variability over a fixed time interval, (ii) the ex-ante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discrete-time ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete- and continuous-time stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recently-popularized realized volatility measures for (non-trivial) fixed-length time intervals.
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Paper provided by Wharton School Center for Financial Institutions, University of Pennsylvania in its series Center for Financial Institutions Working Papers with number
02-27.
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Date of creation: Jul 2002Date of revision:
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"A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation ,"
Journal of Financial Economics ,
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"Fractionally integrated generalized autoregressive conditional heteroskedasticity ,"
Journal of Econometrics ,
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Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001.
"Modeling and Forecasting Realized Volatility ,"
NBER Working Papers
8160, National Bureau of Economic Research, Inc.
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Other versions:
Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001.
"Modeling and Forecasting Realized Volatility ,"
Center for Financial Institutions Working Papers
01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
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Xiaohong Chen & Lars P. Hansen & Marine Carrasco, 2009.
"Nonlinearity and Temporal Dependence ,"
CIRANO Working Papers
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Journal of Business & Economic Statistics ,
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Other versions: Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001.
"The distribution of realized stock return volatility ,"
Journal of Financial Economics ,
Elsevier, vol. 61(1), pages 43-76, July.
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Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000.
"Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian ,"
NBER Working Papers
7488, National Bureau of Economic Research, Inc.
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