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題名 反向型ETF與波動型ETF之避險績效──應用Copula-GJR-GARCH模型
The hedging performance for inverse ETF and volatility ETF—applying the Copula-GJR-GARCH model
作者 林展源
Lin, Jhan-Yuan
貢獻者 林信助
林展源
Lin, Jhan-Yuan
關鍵詞 ETF
避險績效
Copula-GJR-GARCH模型
ETF
Hedging performance
Copula-GJR-GARCH model
日期 2019
上傳時間 5-Sep-2019 15:39:10 (UTC+8)
摘要 近年來，反向型ETF與波動型ETF成為相當熱門的避險與投機商品，然而以往的文獻卻很少將這兩種商品的避險績效互相比較。因此，本研究嘗試建構這兩種不同的避險組合，並利用動態Copula-GJR-GARCH模型，估計每個樣本點上的報酬變異與關聯結構參數，藉此求得更加精準的最適避險比率。本研究也以避險效率與避險效用來評估避險績效，旨在提供避險者可以從更客觀的角度分析前沿避險模型與傳統OLS模型的差異。最後經本研究實證結果顯示，所有動態Copula模型在避險效用上均勝於傳統OLS模型，而且波動型ETF不僅避險效用優於反向型ETF，也具有避險成本較低的優勢。
In recent years, inverse ETFs and volatility ETFs have become very popular instruments for the purpose of hedging and speculation. However, seldom did previous studies compare the hedging performance of those two instruments. Therefore, we attempt to construct two hedging portfolios with those two instruments, and employ the dynamic Copula-GJR-GARCH model to estimate the variation of returns and parameters of copula at each sample point, thereby obtaining the optimal hedging ratio more precisely. In order to analyze the difference between the frontier hedging model and the conventional OLS model from a relatively objective perspective, we evaluate the hedging performance of each portfolio by both the corresponding hedge effectiveness and the corresponding hedging utility. The empirical results show that all models embedded with a dynamic Copula function perform better than the conventional OLS model in terms of hedging utility, and the volatility ETF not only has greater hedging utility than the inverse ETF, but also has an advantage of lower hedging
cost.
參考文獻 Alexander, C., & Barbosa, A. (2008). Hedging index exchange traded funds. Journal of Banking & Finance, 32(2), 326-337.
Alexander, C., & Korovilas, D. (2012). Diversification of equity with vix futures: Personal views and skewness preference. Available at SSRN 2027580.
Baillie, R. T., & Myers, R. J. (1991). Bivariate Garch Estimation of the Optimal Commodity Futures Hedge. Journal of Applied Econometrics, 6(2), 109-124.
Bartram, S. M., Taylor, S. J., & Wang, Y.-H. (2007). The Euro and European financial market dependence. Journal of Banking & Finance, 31(5), 1461-1481.
Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model. The Review of Economics and Statistics, 72(3), 498.
Chakraborty, A., & Barkoulas, J. T. (1999). Dynamic futures hedging in currency markets. The European Journal of Finance, 5(4), 299-314.
Chang, K.-L. (2012). The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model. Economic Modelling, 29(6), 2298-2309.
Clayton, D. G. (1978). A Model for Association in Bivariate Life Tables and Its Application in Epidemiological Studies of Familial Tendency in Chronic Disease Incidence. Biometrika, 65(1), 141-151.
Curcio, R. J., Anderson, R. I., & Guirguis, H. (2015). On the Use of Leveraged-Inverse ETFs to Hedge Risk in Publicly Traded Mortgage Portfolios. The Journal of Index Investing, 6(3), 40-57.
Ederington, L. H. (1979). The Hedging Performance of the New Futures Markets. The Journal of Finance, 34(1), 157-170.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
Engle, R. F., & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. The Journal of Finance, 48(5), 1749-1778.
Fantazzini, D. (2008). Dynamic Copula Modelling for Value at Risk. Frontiers in Finance & Economics, 5(2), 1-38.
Figlewski, S. (1984). Hedging Performance and Basis Risk in Stock Index Futures. The Journal of Finance, 39(3), 657-669.
Ghorbel, A., & Trabelsi, A. (2009). Measure of financial risk using conditional extreme value copulas with EVT margins. Journal of Risk, 11(4), 51-85.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.
Gumbel, E. J. (1960). Bivariate Exponential Distributions. Journal of the American Statistical Association, 55(292), 698-707.
Hansen, B. E. (1994). Autoregressive Conditional Density Estimation. International Economic Review, 35(3), 705-730.
Hautsch, N., & Inkmann, J. (2003). Optimal hedging of the currency exchange risk exposure of dynamically balanced strategic asset allocations. Journal of Asset Management, 4(3), 173-198.
Hsu, C. C., Tseng, C. P., & Wang, Y. H. (2008). Dynamic hedging with futures: A copula‐based GARCH model. Journal of Futures Markets: Futures, Options, Other Derivative Products, 28(11), 1095-1116.
Joe, H., & Xu, J. J. (1996). The Estimation Method of Inference Functions for Margins for Multivariate Models. Technical Report 166, Department of Statistics, University of British Columbia.
Johnson, L. L. (1960). The Theory of Hedging and Speculation in Commodity Futures. The Review of Economic Studies, 27(3), 139-151.
Kroner, K. F., & Sultan, J. (1993). Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures. The Journal of Financial and Quantitative Analysis, 28(4), 535-551.
Lai, Y.-S. (2018). Dynamic hedging with futures: a copula-based GARCH model with high-frequency data. Review of Derivatives Research, 21(3), 307-329.
Lai, Y., & Tseng, J.-C. (2010). The role of Chinese stock market in global stock markets: A safe haven or a hedge? International Review of Economics & Finance, 19(2), 211-218.
Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54.
Lien, D. (2005). The use and abuse of the hedging effectiveness measure. International Review of Financial Analysis, 14(2), 277-282.
Lien, D., & Li, Y. (2006). Spot-futures spread, time-varying correlation, and hedging with currency futures. Journal of Futures Markets, 26(10), 1019-1038.
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Nelsen, R. B. (1999). An introduction to copulas: Springer-Verlag.
Pan, Z., & Sun, X. (2014). Hedging Strategy Using Copula and Nonparametric Methods: Evidence from China Securities Index Futures. International Journal of Economics and Financial Issues, 4(1), 107-121.
Park, T. H., & Switzer, L. N. (1995). Time-varying distributions and the optimal hedge ratios for stock index futures. Applied Financial Economics, 5(3), 131.
Patton, A. J. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47(2), 527-556.
Reboredo, J. C. (2013). Is gold a hedge or safe haven against oil price movements? Resources Policy, 38(2), 130-137.
Rob, W. J. v. d. G., Genest, C., & Werker, B. J. M. (2005). Bivariate option pricing using dynamic copula models. Insurance, Mathematics & Economics, 37(1), 101-114.
Rodriguez, J. C. (2007). Measuring financial contagion: A Copula approach. Journal of Empirical Finance, 14(3), 401-423.
Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de I’ Institut deStatistique de l’University de Paris(8), 229-231.
Stein, J. L. (1961). The Simultaneous Determination of Spot and Futures Prices. The American Economic Review, 51(5), 1012-1025.
描述 碩士
國立政治大學
國際經營與貿易學系
106351016
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106351016
資料類型 thesis
dc.contributor.advisor 林信助zh_TW
dc.contributor.author (Authors) 林展源zh_TW
dc.contributor.author (Authors) Lin, Jhan-Yuanen_US
dc.creator (作者) 林展源zh_TW
dc.creator (作者) Lin, Jhan-Yuanen_US
dc.date (日期) 2019en_US
dc.date.accessioned 5-Sep-2019 15:39:10 (UTC+8)-
dc.date.available 5-Sep-2019 15:39:10 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2019 15:39:10 (UTC+8)-
dc.identifier (Other Identifiers) G0106351016en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125500-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易學系zh_TW
dc.description (描述) 106351016zh_TW
dc.description.abstract (摘要) 近年來，反向型ETF與波動型ETF成為相當熱門的避險與投機商品，然而以往的文獻卻很少將這兩種商品的避險績效互相比較。因此，本研究嘗試建構這兩種不同的避險組合，並利用動態Copula-GJR-GARCH模型，估計每個樣本點上的報酬變異與關聯結構參數，藉此求得更加精準的最適避險比率。本研究也以避險效率與避險效用來評估避險績效，旨在提供避險者可以從更客觀的角度分析前沿避險模型與傳統OLS模型的差異。最後經本研究實證結果顯示，所有動態Copula模型在避險效用上均勝於傳統OLS模型，而且波動型ETF不僅避險效用優於反向型ETF，也具有避險成本較低的優勢。zh_TW
dc.description.abstract (摘要) In recent years, inverse ETFs and volatility ETFs have become very popular instruments for the purpose of hedging and speculation. However, seldom did previous studies compare the hedging performance of those two instruments. Therefore, we attempt to construct two hedging portfolios with those two instruments, and employ the dynamic Copula-GJR-GARCH model to estimate the variation of returns and parameters of copula at each sample point, thereby obtaining the optimal hedging ratio more precisely. In order to analyze the difference between the frontier hedging model and the conventional OLS model from a relatively objective perspective, we evaluate the hedging performance of each portfolio by both the corresponding hedge effectiveness and the corresponding hedging utility. The empirical results show that all models embedded with a dynamic Copula function perform better than the conventional OLS model in terms of hedging utility, and the volatility ETF not only has greater hedging utility than the inverse ETF, but also has an advantage of lower hedging
cost.		en_US
dc.description.tableofcontents 中文摘要 ii
Abstract iv
目錄 v
圖目錄 vi
表目錄 vii
第壹章 緒論 1
第貳章 研究方法 7
第一節 邊際分配模型 7
第二節 Copula理論與模型 9
第三節 避險績效衡量 14
第參章 實證結果分析 16
第一節 資料敘述統計 16
第二節 參數估計結果 19
第肆章 結論 33
參考文獻 35
附錄 39		zh_TW
dc.format.extent 2159786 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106351016en_US
dc.subject (關鍵詞) ETFzh_TW
dc.subject (關鍵詞) 避險績效zh_TW
dc.subject (關鍵詞) Copula-GJR-GARCH模型zh_TW
dc.subject (關鍵詞) ETFen_US
dc.subject (關鍵詞) Hedging performanceen_US
dc.subject (關鍵詞) Copula-GJR-GARCH modelen_US
dc.title (題名) 反向型ETF與波動型ETF之避險績效──應用Copula-GJR-GARCH模型zh_TW
dc.title (題名) The hedging performance for inverse ETF and volatility ETF—applying the Copula-GJR-GARCH modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Alexander, C., & Barbosa, A. (2008). Hedging index exchange traded funds. Journal of Banking & Finance, 32(2), 326-337.
Alexander, C., & Korovilas, D. (2012). Diversification of equity with vix futures: Personal views and skewness preference. Available at SSRN 2027580.
Baillie, R. T., & Myers, R. J. (1991). Bivariate Garch Estimation of the Optimal Commodity Futures Hedge. Journal of Applied Econometrics, 6(2), 109-124.
Bartram, S. M., Taylor, S. J., & Wang, Y.-H. (2007). The Euro and European financial market dependence. Journal of Banking & Finance, 31(5), 1461-1481.
Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model. The Review of Economics and Statistics, 72(3), 498.
Chakraborty, A., & Barkoulas, J. T. (1999). Dynamic futures hedging in currency markets. The European Journal of Finance, 5(4), 299-314.
Chang, K.-L. (2012). The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model. Economic Modelling, 29(6), 2298-2309.
Clayton, D. G. (1978). A Model for Association in Bivariate Life Tables and Its Application in Epidemiological Studies of Familial Tendency in Chronic Disease Incidence. Biometrika, 65(1), 141-151.
Curcio, R. J., Anderson, R. I., & Guirguis, H. (2015). On the Use of Leveraged-Inverse ETFs to Hedge Risk in Publicly Traded Mortgage Portfolios. The Journal of Index Investing, 6(3), 40-57.
Ederington, L. H. (1979). The Hedging Performance of the New Futures Markets. The Journal of Finance, 34(1), 157-170.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
Engle, R. F., & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. The Journal of Finance, 48(5), 1749-1778.
Fantazzini, D. (2008). Dynamic Copula Modelling for Value at Risk. Frontiers in Finance & Economics, 5(2), 1-38.
Figlewski, S. (1984). Hedging Performance and Basis Risk in Stock Index Futures. The Journal of Finance, 39(3), 657-669.
Ghorbel, A., & Trabelsi, A. (2009). Measure of financial risk using conditional extreme value copulas with EVT margins. Journal of Risk, 11(4), 51-85.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.
Gumbel, E. J. (1960). Bivariate Exponential Distributions. Journal of the American Statistical Association, 55(292), 698-707.
Hansen, B. E. (1994). Autoregressive Conditional Density Estimation. International Economic Review, 35(3), 705-730.
Hautsch, N., & Inkmann, J. (2003). Optimal hedging of the currency exchange risk exposure of dynamically balanced strategic asset allocations. Journal of Asset Management, 4(3), 173-198.
Hsu, C. C., Tseng, C. P., & Wang, Y. H. (2008). Dynamic hedging with futures: A copula‐based GARCH model. Journal of Futures Markets: Futures, Options, Other Derivative Products, 28(11), 1095-1116.
Joe, H., & Xu, J. J. (1996). The Estimation Method of Inference Functions for Margins for Multivariate Models. Technical Report 166, Department of Statistics, University of British Columbia.
Johnson, L. L. (1960). The Theory of Hedging and Speculation in Commodity Futures. The Review of Economic Studies, 27(3), 139-151.
Kroner, K. F., & Sultan, J. (1993). Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures. The Journal of Financial and Quantitative Analysis, 28(4), 535-551.
Lai, Y.-S. (2018). Dynamic hedging with futures: a copula-based GARCH model with high-frequency data. Review of Derivatives Research, 21(3), 307-329.
Lai, Y., & Tseng, J.-C. (2010). The role of Chinese stock market in global stock markets: A safe haven or a hedge? International Review of Economics & Finance, 19(2), 211-218.
Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54.
Lien, D. (2005). The use and abuse of the hedging effectiveness measure. International Review of Financial Analysis, 14(2), 277-282.
Lien, D., & Li, Y. (2006). Spot-futures spread, time-varying correlation, and hedging with currency futures. Journal of Futures Markets, 26(10), 1019-1038.
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Nelsen, R. B. (1999). An introduction to copulas: Springer-Verlag.
Pan, Z., & Sun, X. (2014). Hedging Strategy Using Copula and Nonparametric Methods: Evidence from China Securities Index Futures. International Journal of Economics and Financial Issues, 4(1), 107-121.
Park, T. H., & Switzer, L. N. (1995). Time-varying distributions and the optimal hedge ratios for stock index futures. Applied Financial Economics, 5(3), 131.
Patton, A. J. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47(2), 527-556.
Reboredo, J. C. (2013). Is gold a hedge or safe haven against oil price movements? Resources Policy, 38(2), 130-137.
Rob, W. J. v. d. G., Genest, C., & Werker, B. J. M. (2005). Bivariate option pricing using dynamic copula models. Insurance, Mathematics & Economics, 37(1), 101-114.
Rodriguez, J. C. (2007). Measuring financial contagion: A Copula approach. Journal of Empirical Finance, 14(3), 401-423.
Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de I’ Institut deStatistique de l’University de Paris(8), 229-231.
Stein, J. L. (1961). The Simultaneous Determination of Spot and Futures Prices. The American Economic Review, 51(5), 1012-1025.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU201900817en_US