Evaluating Volatility and Correlation Forecasts.

This paper considers the problem of evaluation and comparison of univariate and multivariate volatility forecasts, with explicit attention paid to the fact that in such applications the object of interest is unobservable, even ex post. Thus the evaluation and comparison of volatility forecasts must...

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Main Authors: Patton, A, Sheppard, K
Format: Working paper
Language:English
Published: Oxford-Man Institute of Quantitative Finance 2007
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author Patton, A
Sheppard, K
author_facet Patton, A
Sheppard, K
author_sort Patton, A
collection OXFORD
description This paper considers the problem of evaluation and comparison of univariate and multivariate volatility forecasts, with explicit attention paid to the fact that in such applications the object of interest is unobservable, even ex post. Thus the evaluation and comparison of volatility forecasts must rely on direct or indirect methods of overcoming this difficulty. Direct methods use a “volatility proxy”, i.e. some observable variable that is related to the latent variable of interest. We will assume the existence of an unbiased volatility proxy, such as daily squared returns for the daily conditional variance of returns. Indirect methods of overcoming the latent nature of the variable of interest include comparing forecasts via mean-variance portfolio decisions or comparisons based on portfolio “tracking error”.
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spelling oxford-uuid:474e796d-5656-4e6b-94d3-b10125785fc52022-03-26T15:19:29ZEvaluating Volatility and Correlation Forecasts.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:474e796d-5656-4e6b-94d3-b10125785fc5EnglishDepartment of Economics - ePrintsOxford-Man Institute of Quantitative Finance2007Patton, ASheppard, KThis paper considers the problem of evaluation and comparison of univariate and multivariate volatility forecasts, with explicit attention paid to the fact that in such applications the object of interest is unobservable, even ex post. Thus the evaluation and comparison of volatility forecasts must rely on direct or indirect methods of overcoming this difficulty. Direct methods use a “volatility proxy”, i.e. some observable variable that is related to the latent variable of interest. We will assume the existence of an unbiased volatility proxy, such as daily squared returns for the daily conditional variance of returns. Indirect methods of overcoming the latent nature of the variable of interest include comparing forecasts via mean-variance portfolio decisions or comparisons based on portfolio “tracking error”.
spellingShingle Patton, A
Sheppard, K
Evaluating Volatility and Correlation Forecasts.
title Evaluating Volatility and Correlation Forecasts.
title_full Evaluating Volatility and Correlation Forecasts.
title_fullStr Evaluating Volatility and Correlation Forecasts.
title_full_unstemmed Evaluating Volatility and Correlation Forecasts.
title_short Evaluating Volatility and Correlation Forecasts.
title_sort evaluating volatility and correlation forecasts
work_keys_str_mv AT pattona evaluatingvolatilityandcorrelationforecasts
AT sheppardk evaluatingvolatilityandcorrelationforecasts