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- W2618859334 abstract "The accurate estimation and forecasting of volatility is of utmost importance for anyone whoparticipates in the financial market as it affects the whole financial system and, consequently,the whole economy. It has been a popular subject of research with no general conclusion as towhich model provides the most accurate forecasts. This thesis enters the ongoing debate byassessing and comparing the forecasting performance of popular volatility models. Moreover,the role of key parameters of volatility is evaluated in improving the forecast accuracy ofthe models. For these purposes a number of US and European stock indices is used. Themain contributions are four. First, I find that implied volatility can be per se forecastedand combining the information of implied volatility and GARCH models predict better thefuture volatility. Second, the GARCH class of models are superior to the stochastic volatilitymodels in forecasting the one-, five- and twenty two-days ahead volatility. Third, when therealised volatility is modelled and forecast directly using time series, I find that the HAR modelperforms better than the ARFIMA. Finally, I find that the leverage effect and implied volatilitysignificantly improve the fit and forecasting performance of all the models." @default.
- W2618859334 created "2017-06-05" @default.
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- W2618859334 date "2016-09-01" @default.
- W2618859334 modified "2023-09-27" @default.
- W2618859334 title "Essays on financial volatility forecasting" @default.
- W2618859334 hasPublicationYear "2016" @default.
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