Sucarrat, Genaro (2020): Identification of Volatility Proxies as Expectations of Squared Financial Return.

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Abstract
Volatility proxies like Realised Volatility (RV) are extensively used to assess the forecasts of squared financial return produced by Autoregressive Conditional Heteroscedasticity (ARCH) models. But are volatility proxies identified as expectations of the squared return? If not, then the results of these comparisons can be misleading, even if the proxy is unbiased. Here, a tripartite distinction between strong, semistrong and weak identification of a volatility proxy as an expectation of squared return is introduced. The definition implies that semistrong and weak identification can be studied and corrected for via a multiplicative transformation. Wellknown tests can be used to check for identification and bias, and Monte Carlo simulations show they are wellsized and powerful  even in fairly small samples. As an illustration, twelve volatility proxies used in three seminal studies are revisited. Half of the proxies do not satisfy either semistrong or weak identification, but their corrected transformations do. Correcting for identification does not always reduce the bias of the proxy, so there is a tradeoff between the choice of correction and the resulting bias.
Item Type:  MPRA Paper 

Original Title:  Identification of Volatility Proxies as Expectations of Squared Financial Return 
English Title:  Identification of Volatility Proxies as Expectations of Squared Financial Return 
Language:  English 
Keywords:  GARCH models, financial timeseries econometrics, volatility forecasting, Realised Volatility 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C18  Methodological Issues: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C58  Financial Econometrics 
Item ID:  101953 
Depositing User:  Dr. Genaro Sucarrat 
Date Deposited:  22 Jul 2020 07:11 
Last Modified:  22 Jul 2020 07:11 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/101953 