题名

匯率涉險值最佳GARCH模型評估-模型信心集的探討

并列篇名

A Comparison of GARCH-Family Models for Var Estimation In Taiwan Exchange Rate Market: Application of Model Confidence Set

DOI

10.6459/JCM.201803_15(1).0001

作者

梁晉綱(J. K. Liang)

关键词

GARCH ; 涉險值 ; 匯率 ; 波動率 ; 模型信心集 ; GARCH Models ; Value-at-Risk ; Exchange Rate ; Volatility ; Model Confidence Set

期刊名称

危機管理學刊

卷期/出版年月

15卷1期(2018 / 03 / 01)

页次

1 - 8

内容语文

繁體中文

中文摘要

本文應用Hansen et al. [1]所提出的MCS(model confidence set),替七國匯率(英鎊、日幣、港幣、新台幣、印尼盾、泰國銖)找出能夠估計出樣本外200天VaR (涉險值)最佳GARCH模型集。本文實證發現:針對不同的實證資料,會有不同的最佳MCS組合,同時不同的MCS,其組合內的各模型績效也各不相同,有的匯率以簡單的模型表現就很好,甚至優於複雜模型(有考慮偏態及槓桿效應),相反的,有的貨幣需要複雜模型,甚至包含波動率長短期效應才能預估準確,因此不同的模型適合不同的功能與不同的實證標的,所以模型信心集有其重要性,因其能結合預測能力(equal predictive ability)相當的模型,如此結合模型當優於單一模型的預估能力。

英文摘要

This paper compares the Value-at-Risk (VaR) forecasts delivered by 30 GARCH-family model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (2011). The MCS method is analogous to confidence interval of a parameter estimation in the sense that the MCS contains a set of statistical predictive accuracy (Equal Predictive Ability) models. Our empirical study suggests that some exchange rate's MCS contain more complicate models, such as model with skewness and leverage effect, however, other countries exchange rates can be predicted well using simplest GARCH model. It signals the importance of MCS method which permits to combine a set of superior models, and such superior set can improve the efficiency of predict accuracy than a single best model.

主题分类 社會科學 > 管理學
参考文献
  1. Bates, J.,Granger, C.(1969).The combination of forecasts.OR,20,451-468.
  2. Bernardi, M., Catania, L. and Petrella, L. (2014). “Are News Important to Predict Large Losses?” Working Paper, Arxiv Preprint
  3. Bollerslev, T.(1986).Generalized Autoregressive Conditional Heteroscedasticity.Journal of Econometrics,31(3),307-327.
  4. Clemen, R.(1989).Combining forecasts: A review and annotated bibliography.International Journal of Forecasting,5,559-583.
  5. Clemen, R.,Winkler, R.(1986).Combining economic forecasts.Journal of Business & Economic Statistics,39-46.
  6. Ding, Z.,Granger, C. W.,Engle, R. F.(1993).A long memory property of stock market returns and a new model.Journal of Empirical Finance,1(1),83-106.
  7. Engle, R.,Lee, G.(1999).A permanent and transitory component model of stock return volatility.Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W. J. Granger,New York:
  8. Glosten, L. R.,Jagannathan, R.,Runkle, D.(1993).On the relation between the expected value and the volatility of the nominal excess return on stocks.Journal of Finance,48(5),1779-1801.
  9. González-Rivera, G.,Lee, T.-H.,Mishra, S.(2004).Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood.International Journal of Forecasting,20(4),629-645.
  10. Hansen, P. R.,Lunde, A.,Nason, J. M.(2003).Choosing the best volatility models: The model confidence set approach.Oxford Bulletin of Economics and Statistics,65(s1),839-861.
  11. Hansen, P.,Lunde, A.,Nason, J.(2011).The model confidence set.Econometrica,79,453-497.
  12. Makridakis, S.,Winkler, R.(1983).Averages of forecasts: Some empirical results.Management Science,987-996.
  13. Nelson, D. B.(1991).Conditional heteroskedasticity in asset returns: A new approach.Econometrica,59,347-370.
  14. Stock, J.,Watson, M.(2004).Combination forecasts of output growth in a seven-country data set.Journal of Forecasting,23,405-430.
  15. Timmermann, A.(2006).Forecast combinations.Handbook of economic forecasting,1,135-196.