臺灣博碩士論文加值系統

國內於今年(91)6月通過金融資產證券化條例，正式宣告我國進入金融資產證券化時代，以美國為例，其中將以不動產抵押證券商品為未來主流，遂為此文來探討美國MBS商品定價及避險。本文運用kernel-density regression approach 為定價避險的主要方法，並與其他方法進行比較，以及探討不同抽樣方式、不同期間的差異，另外亦從SAS來篩選適合kernel-density regression approach 的獨立變數。實證結果顯示，kernel-density regression approach 較其他方法為佳，且在隨機抽樣方式下表現較佳，而且，SAS MAXR procedure及principal component analysis比其他方法更能為其篩選出較佳的獨立變數。

The Financial Asset Securitization Law was just passed by Taiwan’s legislature in June, 2002. The law is expected to address stagnancy in Taiwan''s capital markets, it could potentially reinvigorate domestic banks by allowing their asset-backed loans to be packaged into securities. Hence, this new law facilitates the mechanism whereby banks can re-package collateral and sell it to investors as securities, helping to increase the "liquidity of banks" and also diversifying their risks. Financial assets such as mortgages, car loans or credit card receivable accounts can be repackaged into small values in the form of securities certificates or beneficiary certificates for sales to investors. Among all kinds of financial asset products, mortgage-backed security is the most popular in US market.
The current way of solving this valuation problems has been to assume a stochastic process for term structure movements and to employ either a simulation/forecasting pricing approach or an empirical/statistical approach for prepayment behavior and price process. In this article, we propose a nonparametric pricing method, kernel-density regression approach, to price weekly TBA (to be announced) GNMA securities. Here we have three goals: the first is to find out what is the best way to reduce the number of independent variables to use for the kernel model and other model and what is the remaining inputs, the second is to assess the pricing effect of kernel-density regression approach versus other pricing models. Finally, we want to recognize the hedging effectiveness of kernel-density regression approach and other models. For comparison, we use another two popular pricing approaches: ordinary least squares (OLS) and a parametric model (proprietary practitioner model).
According empirical results, we find that kernel-density regression model perform more effectively on estimating MBS price than the other two models mentioned in this article, except in out-of-sample of time-series sampling. Moreover, kernel-density regression model have better pricing effect on random sampling than on time-series sampling, especially in out-of-sample. In addition, SAS MAXR procedure and principal component analysis can effectively reduce the number of independent variables used for both kernel-density regression model and OLS model. In regard to the hedging effect, the results of in-of-sample are approximately the same with pricing effect analysis. But, in contrast with in-of-sample, Kernel(3-month rate) is the best way to hedge the MBS in out-of-sample, especially on random sampling.

Content
1. Intoduction… ..1
2. The Pricing Model 5
2.1 MBS Pricing 5
2.2 Kernel-Density Regression Model 6
2.3 Proprietary Practitioner Model 9
2.4 The OLS Approach 10
2.2 The Hedging Error Calculation 10
3. Data Description 11
4. Empirical Results 14
4.1 Independent Variables Analysis 14
4.2 Pricing Effect Analysis 16
4.2.1 In-of-Sample of Time-Series Sampling………..17
4.2.2 In-of-Sample of Random Sampling……………….20
4.2.3 Out-of-Sample of Time-Series Sampling………24
4.2.4 Out-of-Sample of Random Sampling…………………28
4.2.5 Summary ………………………………………….32
4.3 Hedging Effect Analysis 33
4.3.1 In-of-Sample of Time-Series Sampling…………33
4.3.2 In-of-Sample of Random Sampling……………………34
4.3.3 Out-of-Sample of Time-Series Sampling…………36
4.3.4 Out-of-Sample of Random Sampling….…….…37
4.3.5 Summary………………………………………………38
5. Conclusion and Further Extensions 39
References…… 40
Appendix A…. 42
Appendix B…. 60

References
Ait-Sahalia,Y.,1996, Nonparametric Pricing of Interest Rate Derivative Securities, Econometrica 64, 527-560.
Andrew L. Brunson, James B. Kau, and Donald C. Keenan, 2001, A Fixed-Rate Mortgage Valuation Model in Three State Variables, The Journal of Fixed Income 10,17-27.
Bernard Dumas, Jeff Fleming, and Robert E. Whaley, 1998, Implied Volatility Functions: Empirical Tests, The Journal of Finance 53(6), 2059-2106.
Black, F., and N. Scholes, 1973, The Pricing of Options and Corporate Liabilities, Journal of Political Economy 81,637-659.
Boudoukh, J., M. Richardson, R. Stanton, and R. F. Whitelaw, 1995, A New Strategy for Dynamically Hedging Mortgage-Backed Securities, Journal of Derivatives 2, 60-77.
------, 1997, Pricing Mortgage-Backed Securities in a Multifactor Interest Rate Environment: A Multivariate Density Estimation Approach, Rreview of Financial Studies 10, 405-446.
Campbell, T.S., and J.K. Dietrich., 1983, The Determinants of Default on Insured Conventional Residential Mortgage Loans, Journal of Finance 38(5), 1569-1581.
Chen, R., and L. Scott, 1995, Interest Rate Options in Multi-Factor Cox-Ingersoll-Ross Models of the Term Structure. The Journal of Derivatives 2, 53-72.
------, 1992, Pricing Interest Rate Options in a Two-Factor Cox-Ingersoll-Ross Model of the Term Structure, Review of Financial Studies 5, 613-636.
Cox, J. C., J. E. Ingersoll, and S. A. Ross, 1985a, An Intertemporal General Equilibrium Model of Asset Prices, Econometrica 53, 363-384.
------, 1985b, A Theory of the Term Structure of Interest Rates, Econometrica 53, 385-407.
Cunningham, D. F., and P. H. Hendershott. , 1984, Pricing FHA Mortgage Default Insureance, Housing Finance Review 3(4), 373-392.
Dunn. K. B., and J. J. McConnell., 1981, Valuation of GNMA Mortgage-Backed Securities, Journal of Finance 36, 599-616.
Epanechnikov, V., 1969, Nonparametric Estimates of Multivariate Probability Density, Theory of Probability and Applications 14, 153-158.
Epperson, J. F., J. B. Kau, D. C. Keenan, and W. J. Muller., 1985, Pricing Default Risk in Mortgages, AREUEA Journal 13, 261-272.
Frank J. Fabozzi, 2000, Bond Markets, Analysis and Strategies, Prentice-Hall, New Jersey.
Harvey, C., 1991, The Specification of Conditional Expectations, working paper, Duke University.
Hutchison, J. M., A. W. Lo, and T. Paggio, 1994, Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks, Journal of Finance 49, 851-889.
Kau, J.B, D.C. Keenan, W.J. Muller Ⅲ, and J.F. Epperson., 1987, The Valuation and Securitization of Commercial and Multifamily Mortgages, Journal of Banking and Finance 11, 525-546.
------, 1992, A Generalized Valuation Model for Fixed-Rate Residential Mortgages, Journal of Money, Credit, and Banking 24, 279-299.
------, 1994, The Value at Origination of Fixed-Rate Mortgages with Default and Prepayment, Journal of Real Estate Finance and Economics 11, 5-36.
LaCour-Little, M. ,M. Marshoun, and C. Maxam, 1999, Estimating Prepayments on Fixed Rate Mortgages: A Multi-Variate Kernel Regression Approach Using Loan-Level Data, Freddie Mac Working Paper.
LaCour-Little, M., and C. Maxam, 2001, Applied Nonparametric Regression Techniques: Estimating Prepayments on Fixed Rate Mortgage-Backed Securities, Journal of Real Estate Finance and Economics 23, 139-160.
Litterman, R., and J. Scheinkman, 1991, Common Factors Affecting Bond Returns, Journal of Fixed Income 1, 54-61.
Litterman, R., J. Scheinkman, and L. Weiss, 1991, Volatility and the Yield Curve, Journal of Fixed Income 1, 49-53.
Longstaff, F., and E.S. Schwartz, 1992, Interest Rate Volatility and the Term Structure: A Two-Factor General Equilibrium Model, Journal of Finance 47, 1259-1282.
Maxam, C., 1996, Multi-Variate Kernel Density Estimation Methodology in Applied Finance, Indiana University School of Business Working Paper.
Richard, S. F., and R. Roll, 1989, Prepayments on Fixed-Rate Mortgage-Backed Securities, Journal of Portfolio Management 15, 74-82.
Schwartz, E. S., and W. N. Torous, 1989, Prepayment and the Valuation of Mortgage-Backed Securities, Journal of Finance 44, 375-392.
Scott, D. W., 1992, Multivariate Density Estimation: Theory, Practice and Visualization, Wiley, New York.
Stanton, R. H., 1995, Rational Prepayment and the Valuation of Mortgage-Backed Securities, Review of Financial Studies 8, 677-708.