Log-Volatility Enhanced GARCH Models for Single Asset Returns

• Autorzy: Tomasz Skoczylas
• Języki publikacji: EN

Abstrakty

EN

This paper presents an alternative approach to modelling and forecasting single asset return volatility. A new, flexible framework is proposed, one which may be considered a development of single-equation GARCH-type models. In this approach an additional equation is added, which binds logarithms of conditional volatility and observed volatility, as measured by the Garman-Klass variance estimator. It enables more information to be retrieved from data. Proposed models are compared with benchmark GARCH and range-based GARCH (RGARCH) models in terms of prediction accuracy. All models are estimated with the maximum likelihood method, using time series of EUR/PLN, EUR/USD, EUR/GBP spot rates quotations as well as WIG20, Dow Jones industrial and DAX indexes. Results are encouraging, especially for foreasting Value-at-Risk. Log-volatility enhanced models achieved lesser rates of VaR exception, as well as lower coverage test statistics, without being more conservative than their single-equation counterparts, as their forecast error measures are to some degree similar.(original abstract)

Słowa kluczowe

EN

Value at Risk Analysis; GARCH model; Variability; Forecasting

PL

Analiza wartości zagrożonej; Model GARCH; Zmienność; Prognozowanie

• Czasopismo: Bank i Kredyt
• Rocznik: 2015
• Numer: nr 5
• Strony: 411--431

Twórcy

autor

Tomasz Skoczylas

• University of Warsaw

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