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題名 資產模型建構與其資產配置之應用
Asset Modeling with Non-Gaussian Innovation and Applications to Asset Allocation
作者 陳炫羽
Chen, Hsuan Yu
貢獻者 黃泓智
Huang, Hong Chih
陳炫羽
Chen, Hsuan Yu
關鍵詞 厚尾
偏態
峰態
多元仿射JD
多元仿射VG
多元仿射NIG
資產配置
heavy-tailness
skewness
kurtosis
multivariate affine JD
multivariate affine variance gamma
multivariate affine normal inverse Gaussian
asset allocation
日期 2012
上傳時間 22-Jul-2013 11:19:02 (UTC+8)
摘要 因為股票市場常具有厚尾、偏態和峰態的特性且在國際的股票市場之間，股票報酬長存在有尾端相依的情況，所以我們的資產模型不能選用Gaussian分配。
近幾年來，常用GH 分配建構單維度的股票報酬。這篇文章將利用多元仿射JD、多元仿射VG 和多元仿射NIG分配去建構風險性資產的報酬並請應用到資產配置。

建構風險性資產的報酬後，我們提供兩種不同形式的投資組合並且可以導出投資組合的期望值、變異數、偏態和峰態。我們嘗試以投資組合的期望值、變異數、偏態和峰態當成我們的目標函數，然後得出未來最佳的投資組合的權重。為了讓我們的資產配置更加動態和有效率，我們重新估計模型的參數、選擇最佳的投資組合權重，然後重新評估最佳的資產配置在每個決策日期。實證結果發現當股票市場的表現好的時候，我們建議資產配置應使用偏態當成我們的目標函數，但是當股票市場的表現太好的時候，我們建議資產配置應使用變異數當成我們的目標函數。
Since the stock markets always have the characteristics of heavy-tailness, skewness and kurtosis and there exists tail dependence among the international stock markets, we can’t use the Gaussian distribution as our model. Recently, the generalized hyperbolic (GH) distribution has been suggested to fit the single stock returns. This article will use the multivariate affine JD (MAJD), multivariate affine variance gamma (MAVG) and multivariate affine normal inverse Gaussian (MANIG) distributions to construct the risky asset returns, and apply them to asset allocation.

After constructing the risky asset returns, we provide two different forms of portfolio and obtain the mean, variance, skewness, kurtosis of portfolio. We can try to select the optimal weights of portfolio by using the mean, variance, skewness, kurtosis of portfolios as our objective functions. To make our asset allocation more dynamic and efficient, we re-estimate all parameters for our models, select the optimal weights of portfolio, and re-assess the optimal asset allocation at each decision date. Empirically, when the performances of stock markets are good, we suggest that our asset allocation uses the skewness as the objective function. When the performances of stock markets are not good, we suggest that our asset allocation uses the variance as the objective function.
參考文獻 Arditti, Fred D., 1967, Risk and the required return on equity, Journal of Finance, 22, 19-36.
Alles, L.A. and J.L. Kling (1994):Regularities in the Variation of Skewness in Asset Returns, Journal of Financial Research, 17, 427-438.
Barndorff-Nielsen, O. 1977. Exponentially decreasing distributions for the logarithm of particle size.Proceedings of the royal society london A, 353, 401–419.
Barndorff-Nielsen, O. E., 1978. Hyperbolic distributions and distributions on hyperbolae. Scandinavian Journal of Statistics 5, 151-157.
Blasild, P., and J. L. Jensen, 1981, Multivariate distributions of hyperbolic type. In: C. Taillie, G. Patil, and B. Baldessari (Eds.), Statistical distributions in scientific work, Dordrecht Reidel, 4, 45-66.
Bekaert, G., C.B. Erb, C.R. Harvey and T.E. Viskanta (1998): Distributional Characteristics of Emerging Market Returns and Asset Allocation, Journal of Portfolio Management, 24, Winter, 102-116.
Chamberlain, G. (1983): A Characterization of the Distributions That Imply Mean-Variance Utility Functions, Journal of Economic Theory, 29, 185-201.
Eberlein, E., Keller, U., 1995. Hyperbolic Distributions in Finance. Bernoulli, 281-299.
Erb C.B., C.R. Harvey and T.E. Viskanta (1999): New Perspective on Emerging Market Bonds, Journal of Portfolio Management,25, Winter, 83-92.
Fama, Eugene (1965), The Behavior of Stock Prices, Journal of Business, 47, 244-280.
Fajardo, J., Farias, A., 2004. Generalized hyperbolic distributions and Brazilian data.
Brazilian Review of Econometrics 24, 249-271.
Fajardo, J., Farias, A., 2009. Multivariate Affine Generalized Hyperbolic Distributions: An Empirical Investigation. International Review of Financial Analysis 18, 174-184.
Fajardo, J., Farias, A., 2010. Derivative Pricing Using Multivariate Affine Generalized Hyperbolic Distributions. Journal of Banking and Finance 34, 1607-1617.
Jurczenko E. and Maillet B. (2006). Multi-Moment Asset Allocation and Pricing Models. Wiley.
Kon S.J. (1984): Models of Stock Returns - A Comparison, Journal of Finance, 39, 147-165.
Markowitz, H.M. (1952): Portfolio Selection, Journal of Finance, 7, 77-91.
Mandelbrot, B. (1963): The Variation of Certain Speculative Prices, Journal of Business, 36, pp. 394-419.
Machina M. and Muller S. M. (1987). Moment preferences and polynomial utility. Economics Letters, 23, 349-353.
Maroua M. and Jean-Luc P. (2010). International Portfolio Optimization with High Moments. International Journal of Economics and Finance,16 , 157-169.
Prause, K., 1997. Modelling Financial Data Using Generalized Hyperbolic Distributions. FDM Preprint 48, University of Freiburg.
Prause, K., 1999. The Generalized Hyperbolic Models: Estimation, Financial Derivatives and Risk Measurement. PhD Thesis, Mathematics Faculty, University of Freiburg.
Raj Aggarwal, Ramesh P. Rao and Takato Hiraki, 1989, “Skewness and Kurtosis in
Japanese Equity Returns: Empirical Evidence”, The Journal of Financial Research, Vol. XII, No.3.
Scott, Robert C., and Philip A. Horvath (1980), On the direction of preference for moments of higher order than the variance, Journal of Finance, 35, 915-919.
Simkowitz, Michael A. and William L. Beedles (1980): Asymmetric Stable Distributed Security Return, Journal of the American Statistical Association, 75, 306-312.
Schmidt, R., Hrycej, T., Stutzle, E., 2006. Multivariate Distribution Models with Generalized Hyperbolic Margins. Computational Statistics and Data Analysis 50, 2065-2096.
Tobin, J. (1958): Liquidity Preference as Behavior Toward Risk, Review of Economic Studies, 25, 65-85.
Theodossiou, P. (1998): Financial Data and the Skewed Generalized t Distribution, Management Science, 44, 1650 1661.
William L. Beedles, 1979, “On the Asymmetry of Market Returns”, Journal of Financial and Quantitative Analysis, Vol. 14, Issue 3.
William L. Beedles, 1986, “Asymmetry in Australian Equity Returns”, Australian Journal of Management, Vol. 1, Issue 1.
描述 碩士
國立政治大學
風險管理與保險研究所
100358023
101
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1003580231
資料類型 thesis
dc.contributor.advisor 黃泓智zh_TW
dc.contributor.advisor Huang, Hong Chihen_US
dc.contributor.author (Authors) 陳炫羽zh_TW
dc.contributor.author (Authors) Chen, Hsuan Yuen_US
dc.creator (作者) 陳炫羽zh_TW
dc.creator (作者) Chen, Hsuan Yuen_US
dc.date (日期) 2012en_US
dc.date.accessioned 22-Jul-2013 11:19:02 (UTC+8)-
dc.date.available 22-Jul-2013 11:19:02 (UTC+8)-
dc.date.issued (上傳時間) 22-Jul-2013 11:19:02 (UTC+8)-
dc.identifier (Other Identifiers) G1003580231en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/58943-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 風險管理與保險研究所zh_TW
dc.description (描述) 100358023zh_TW
dc.description (描述) 101zh_TW
dc.description.abstract (摘要) 因為股票市場常具有厚尾、偏態和峰態的特性且在國際的股票市場之間，股票報酬長存在有尾端相依的情況，所以我們的資產模型不能選用Gaussian分配。
近幾年來，常用GH 分配建構單維度的股票報酬。這篇文章將利用多元仿射JD、多元仿射VG 和多元仿射NIG分配去建構風險性資產的報酬並請應用到資產配置。

建構風險性資產的報酬後，我們提供兩種不同形式的投資組合並且可以導出投資組合的期望值、變異數、偏態和峰態。我們嘗試以投資組合的期望值、變異數、偏態和峰態當成我們的目標函數，然後得出未來最佳的投資組合的權重。為了讓我們的資產配置更加動態和有效率，我們重新估計模型的參數、選擇最佳的投資組合權重，然後重新評估最佳的資產配置在每個決策日期。實證結果發現當股票市場的表現好的時候，我們建議資產配置應使用偏態當成我們的目標函數，但是當股票市場的表現太好的時候，我們建議資產配置應使用變異數當成我們的目標函數。		zh_TW
dc.description.abstract (摘要) Since the stock markets always have the characteristics of heavy-tailness, skewness and kurtosis and there exists tail dependence among the international stock markets, we can’t use the Gaussian distribution as our model. Recently, the generalized hyperbolic (GH) distribution has been suggested to fit the single stock returns. This article will use the multivariate affine JD (MAJD), multivariate affine variance gamma (MAVG) and multivariate affine normal inverse Gaussian (MANIG) distributions to construct the risky asset returns, and apply them to asset allocation.

After constructing the risky asset returns, we provide two different forms of portfolio and obtain the mean, variance, skewness, kurtosis of portfolio. We can try to select the optimal weights of portfolio by using the mean, variance, skewness, kurtosis of portfolios as our objective functions. To make our asset allocation more dynamic and efficient, we re-estimate all parameters for our models, select the optimal weights of portfolio, and re-assess the optimal asset allocation at each decision date. Empirically, when the performances of stock markets are good, we suggest that our asset allocation uses the skewness as the objective function. When the performances of stock markets are not good, we suggest that our asset allocation uses the variance as the objective function.		en_US
dc.description.tableofcontents Catalog...I
List of Table...III
List of Figure...V
l. Introduction...1
2. The JD, VG and NIG Distributions...5
2.1 Introductions of the JD, VG and NIG Distributions...5
2.1.1 Introduction of the JD Distribution...5
2.1.2 Introduction of the VG Distribution...6
2.1.3 Introduction of the NIG Distribution...7
2.2 The Standardization Approaches of the JD, VG and NIG Distributions...8
2.2.1 The Standardization Approaches of the JD Distribution...8
2.2.2 The Standardization Approaches of the VG Distribution ...9
2.2.3 The Standardization Approaches of the NIG Distribution ...9
2.2.4 Estimate the Parameters of the JD, VG and NIG Distribution...9
3. The MAJD, MAVG and MANIG Distributions...12
3.1 Introduction of the MAJD, MAVG and MANIG Distributions ...12
3.1.1 Introduction of the MAJD Distribution...12
3.1.2 Introduction of the MAVG Distribution...12
3.1.3 Introduction of the MAVG Distribution...13
3.2 Estimate the parameters of the MAJD, MAVG and MANIG Distributions...13
3.3 MAJD, MAVG and MANIG Processes for Asset Returns...14
3.4 Portfolio selection...15
4. Empirical analysis...21
4.1 The source of the data and the statistics of data...21
4.2 Estimate the parameters of the data...23
4.3 The funding value for our objective functions under MAJD, MAVG and MANIG distributions...24
4.3.1 The funding value on developed countries...24
4.3.2 The funding value on developing countries...32
4.3.3 The funding values on the assets of mixing of developed countries and developing countries...40
5. Conclusion...48
Reference...49
Appendix A...52
Appendix B...54
Appendix C...56
Appendix D...76
Appendix E...95
Appendix F...107
Appendix G...119		zh_TW
dc.format.extent 4323628 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1003580231en_US
dc.subject (關鍵詞) 厚尾zh_TW
dc.subject (關鍵詞) 偏態zh_TW
dc.subject (關鍵詞) 峰態zh_TW
dc.subject (關鍵詞) 多元仿射JDzh_TW
dc.subject (關鍵詞) 多元仿射VGzh_TW
dc.subject (關鍵詞) 多元仿射NIGzh_TW
dc.subject (關鍵詞) 資產配置zh_TW
dc.subject (關鍵詞) heavy-tailnessen_US
dc.subject (關鍵詞) skewnessen_US
dc.subject (關鍵詞) kurtosisen_US
dc.subject (關鍵詞) multivariate affine JDen_US
dc.subject (關鍵詞) multivariate affine variance gammaen_US
dc.subject (關鍵詞) multivariate affine normal inverse Gaussianen_US
dc.subject (關鍵詞) asset allocationen_US
dc.title (題名) 資產模型建構與其資產配置之應用zh_TW
dc.title (題名) Asset Modeling with Non-Gaussian Innovation and Applications to Asset Allocationen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Arditti, Fred D., 1967, Risk and the required return on equity, Journal of Finance, 22, 19-36.
Alles, L.A. and J.L. Kling (1994):Regularities in the Variation of Skewness in Asset Returns, Journal of Financial Research, 17, 427-438.
Barndorff-Nielsen, O. 1977. Exponentially decreasing distributions for the logarithm of particle size.Proceedings of the royal society london A, 353, 401–419.
Barndorff-Nielsen, O. E., 1978. Hyperbolic distributions and distributions on hyperbolae. Scandinavian Journal of Statistics 5, 151-157.
Blasild, P., and J. L. Jensen, 1981, Multivariate distributions of hyperbolic type. In: C. Taillie, G. Patil, and B. Baldessari (Eds.), Statistical distributions in scientific work, Dordrecht Reidel, 4, 45-66.
Bekaert, G., C.B. Erb, C.R. Harvey and T.E. Viskanta (1998): Distributional Characteristics of Emerging Market Returns and Asset Allocation, Journal of Portfolio Management, 24, Winter, 102-116.
Chamberlain, G. (1983): A Characterization of the Distributions That Imply Mean-Variance Utility Functions, Journal of Economic Theory, 29, 185-201.
Eberlein, E., Keller, U., 1995. Hyperbolic Distributions in Finance. Bernoulli, 281-299.
Erb C.B., C.R. Harvey and T.E. Viskanta (1999): New Perspective on Emerging Market Bonds, Journal of Portfolio Management,25, Winter, 83-92.
Fama, Eugene (1965), The Behavior of Stock Prices, Journal of Business, 47, 244-280.
Fajardo, J., Farias, A., 2004. Generalized hyperbolic distributions and Brazilian data.
Brazilian Review of Econometrics 24, 249-271.
Fajardo, J., Farias, A., 2009. Multivariate Affine Generalized Hyperbolic Distributions: An Empirical Investigation. International Review of Financial Analysis 18, 174-184.
Fajardo, J., Farias, A., 2010. Derivative Pricing Using Multivariate Affine Generalized Hyperbolic Distributions. Journal of Banking and Finance 34, 1607-1617.
Jurczenko E. and Maillet B. (2006). Multi-Moment Asset Allocation and Pricing Models. Wiley.
Kon S.J. (1984): Models of Stock Returns - A Comparison, Journal of Finance, 39, 147-165.
Markowitz, H.M. (1952): Portfolio Selection, Journal of Finance, 7, 77-91.
Mandelbrot, B. (1963): The Variation of Certain Speculative Prices, Journal of Business, 36, pp. 394-419.
Machina M. and Muller S. M. (1987). Moment preferences and polynomial utility. Economics Letters, 23, 349-353.
Maroua M. and Jean-Luc P. (2010). International Portfolio Optimization with High Moments. International Journal of Economics and Finance,16 , 157-169.
Prause, K., 1997. Modelling Financial Data Using Generalized Hyperbolic Distributions. FDM Preprint 48, University of Freiburg.
Prause, K., 1999. The Generalized Hyperbolic Models: Estimation, Financial Derivatives and Risk Measurement. PhD Thesis, Mathematics Faculty, University of Freiburg.
Raj Aggarwal, Ramesh P. Rao and Takato Hiraki, 1989, “Skewness and Kurtosis in
Japanese Equity Returns: Empirical Evidence”, The Journal of Financial Research, Vol. XII, No.3.
Scott, Robert C., and Philip A. Horvath (1980), On the direction of preference for moments of higher order than the variance, Journal of Finance, 35, 915-919.
Simkowitz, Michael A. and William L. Beedles (1980): Asymmetric Stable Distributed Security Return, Journal of the American Statistical Association, 75, 306-312.
Schmidt, R., Hrycej, T., Stutzle, E., 2006. Multivariate Distribution Models with Generalized Hyperbolic Margins. Computational Statistics and Data Analysis 50, 2065-2096.
Tobin, J. (1958): Liquidity Preference as Behavior Toward Risk, Review of Economic Studies, 25, 65-85.
Theodossiou, P. (1998): Financial Data and the Skewed Generalized t Distribution, Management Science, 44, 1650 1661.
William L. Beedles, 1979, “On the Asymmetry of Market Returns”, Journal of Financial and Quantitative Analysis, Vol. 14, Issue 3.
William L. Beedles, 1986, “Asymmetry in Australian Equity Returns”, Australian Journal of Management, Vol. 1, Issue 1.		zh_TW