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Modeling and Forecasting Volatility – How Reliable are modern day approaches?

Anirudh Mehta and Kunal Kanishka

MPRA Paper from University Library of Munich, Germany

Abstract: This study explores the volatility models and evaluates the quality of one-step ahead forecasts of volatility constructed by (1) GARCH, (2) TGARCH, (3) Risk metrics and (4) Historical volatility. Volatility forecasts suggest that TGARCH performs relatively best in term of MSPE, followed by GARCH, Risk metrics and historical volatility. In terms of VaR, we test for correct unconditional coverage and index- Dependence of violations using Likelihood Ratio tests. The tests suggest that VaR forecasts at 90 % and 95% have desirable properties. Regarding 99% VaR forecasts, We find significant evidence that suggests none of the models can reliably predict at this confidence level.

Keywords: Asset pricing; Volatility Forecasting; GARCH; T-GARCH; Risk metrics; LR ratio; VaR (search for similar items in EconPapers)
JEL-codes: C10 C12 C15 C19 C51 C53 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for, nep-ore and nep-rmg
Date: 2014-11-08
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