Mitigating Tail-fatness, Lepto Kurtic and Skewness Problems in VaR Estimation via Markov Switching Settings-An Empirical Study on Major TAlEX Index Returns

藉由馬可夫轉換模型解決風險值計測過程高峰、厚尾與偏態問題－國內主要股市指數實證結果

林修葳(Hsiou-Wei Lin) ； 饒秀華(Hsiu-Hua Rau) ； 黎明淵(Ming-Yuan Li)

《中國財務學刊》 7卷3期 (1999/12) Pp. 61-94

https://doi.org/10.6545/JoFS.1999.7(3).3

摘要

本論文以馬可夫模型估算風險值，分析討論與實證應用或屬原創。風險值估算是財金學術與實務領域很重視課題，我們應用雙重狀態期望值與波動設定，採馬可夫過程控制狀態間切換，也藉國內主要股價指數報酬樣本，以貝氏混合分配與GARCH測試結果與馬可夫互對照。在各類模型降低報酬衝擊分配非常態問題效果比較，馬可夫表現最佳，貝氏混合分自己與GARCH則分呈過度與不足。就實務應用角度言，馬可夫優勢對5%臨界機率風險值不顯著，在1%典2.5%臨界機率情境則頗明顯。在學習窗期的選擇，為掌握殺傷力大但不常發生的特殊事件，描述報酬損失較利得需更長的歷史資料。

關鍵字

馬克夫轉換模型 ； 風險值

並列摘要

This paper serves as one of the first studies that estimate the value at risk (VaR) via a Markov-switching (MS) model. Specifically, we use a two-regime MS specification, a MS setting with two sets of regime mean and regime variance, on TAIEX as well as Taiwan's major industrial group stock index returns. We demonstrate that MS effectively correct non-normality problems and outshine both GARCH and the mixing normal models, with the former (latter) alternative being subject to over- (under-estimating) the persistence of stock return volatility (hereafter volatility). As for estimating the 5% VaR, MS appears to be equally effective as Bayesian mixing normal and GARCH. In contrast, MS significantly outperforms the two nonlinear alternatives for estimating VaR with 1% or 2.5% tail probabilities. Furthermore, as for the window of learning period on rare events, we find that one need to go much farther back to effectively depict the left as opposed to the right tail.

並列關鍵字

Markov-switching models ； value at risk