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Medium‐term horizon volatility forecasting: A comparative study
Author(s) -
Hawkes Richard,
Date Paresh
Publication year - 2007
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.684
Subject(s) - volatility (finance) , forward volatility , econometrics , financial models with long tailed distributions and volatility clustering , implied volatility , stochastic volatility , autoregressive conditional heteroskedasticity , volatility risk premium , kalman filter , realized variance , volatility smile , economics , computer science , mathematics , statistics
In this paper, volatility is estimated and then forecast using unobserved components‐realized volatility (UC‐RV) models as well as constant volatility and GARCH models. With the objective of forecasting medium‐term horizon volatility, various prediction methods are employed: multi‐period prediction, variable sampling intervals and scaling. The optimality of these methods is compared in terms of their forecasting performance. To this end, several UC‐RV models are presented and then calibrated using the Kalman filter. Validation is based on the standard errors on the parameter estimates and a comparison with other models employed in the literature such as constant volatility and GARCH models. Although we have volatility forecasting for the computation of Value‐at‐Risk in mind the methodology presented has wider applications. This investigation into practical volatility forecasting complements the substantial body of work on realized volatility‐based modelling in business. Copyright © 2007 John Wiley & Sons, Ltd.