評估具厚尾分配風險之避險效果

Hedging and Value at Risk with a Heavy Tailed Distribution

本文探討金融資產報酬為非常態分配時，在最適避險策略的效果。我們首度採用極小化student-t分配之風險值及條件風險值來估算避險比率。以英國金融時報100指數、台灣加權股價指數、黃金與西德州原油為現貨及其對應之期貨為避險工具，探討極小化Student-t風險值與極小化Student-t條件風險值之避險效果，是否較極小化變異數、極小化Cornish-Fisher（Cornish and Fisher，1937）風險值、極小化Cornish-Fisher條件風險值模型等研究方法，提供較佳之避險績效。實證結果顯示，極小化Student-t風險值與極小化Student-t條件風險值避險法亦使避險組合分配產生高狹峰，但在風險值與條件風險值減少程度上優於其他方法。最後，利用回溯測試檢視各模型評估市場風險的能力。結果顯示只有極小化Student-t風險值法與極小化Student-t條件風險值通過檢驗。代表極小化Student-t風險值與極小化Student-t條件風險值法不但在避險績效上優於其他方法並且能精準地預測風險值。

This paper evaluates the non-normality effect of financial asset returns on the optimal hedge ratio. We adopt a new method of estimating the minimum value at risk and the minimum conditional value at risk hedge ratios based on the student-t distribution. Using spot and futures returns for the FTSE 100, Taiwan Weighted Stock Indices, Gold, and WTI Crude Oil, we examine whether the new approach works better than the minimum-variance hedging, minimum value at risk, and minimum conditional value at risk approaches based on the Cornish- Fisher expansion. Among the various models, the empirical results show that the minimum student-t value at risk and the minimum student-t conditional value at risk techniques present more efficient results. Moreover, through backtesting, the empirical results offer evidence that the new approaches accurately evaluate the market risk of hedging portfolios.