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首頁> 外文期刊>Electronic Journal of Applied Statistical Analysis >Volatility estimation using support vector machine: Applications to major foreign exchange rates
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Volatility estimation using support vector machine: Applications to major foreign exchange rates

機(jī)譯：使用支持向量機(jī)的波動率估計(jì)：在主要匯率中的應(yīng)用

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摘要

In finance, volatility is fundamentally important because it is associated with the risk. A growing body of literature shows that risks associated with volatility are priced in stock, option, bond, and foreign exchange markets. Therefore, an accurate measurement and estimation of the volatility is critical in financial markets. The generalized autoregressive conditional heteroskedasticity (GARCH) has been one of the most popular volatility models and the model is usually estimated from the maximum likelihood estimation (MLE) method. In this paper, we attempt to improve the MLE-based GARCH forecast using the support vector machine (SVM). We also compare with two asymmetric volatility models: exponential GARCH (E-GARCH) and Glosten-Jagannathan-Runkle GARCH (GJR-GARCH). We carry out the analysis through simulations and real datasets. The results show that the GARCH- and SVM-based volatility model provides better predictive potential than the existing volatility models.
機(jī)譯：在金融中，波動性從根本上很重要，因?yàn)樗c風(fēng)險(xiǎn)相關(guān)。越來越多的文獻(xiàn)表明，與波動相關(guān)的風(fēng)險(xiǎn)是在股票，期權(quán)，債券和外匯市場中定價(jià)的。因此，對波動率的準(zhǔn)確測量和估計(jì)在金融市場中至關(guān)重要。廣義自回歸條件異方差（GARCH）是最流行的波動率模型之一，通常通過最大似然估計(jì)（MLE）方法估計(jì)該模型。在本文中，我們嘗試使用支持向量機(jī)（SVM）改進(jìn)基于MLE的GARCH預(yù)測。我們還與兩個(gè)非對稱波動率模型進(jìn)行了比較：指數(shù)GARCH（E-GARCH）和Glosten-Jagannathan-Runkle GARCH（GJR-GARCH）。我們通過模擬和真實(shí)數(shù)據(jù)集進(jìn)行分析。結(jié)果表明，與現(xiàn)有的波動率模型相比，基于GARCH和SVM的波動率模型提供了更好的預(yù)測潛力。

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