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Two Essays in Financial Economics: I. Empirical Performance of the Constant Elasticity Variance Option Pricing Model. II. Default Prediction of Alternative Structural Credit Risk Models and Implications of Default Barriers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Two Essays in Financial Economics: I. Empirical Performance of the Constant Elasticity Variance Option Pricing Model. II. Default Prediction of Alternative Structural Credit Risk Models and Implications of Default Barriers // Han-Hsing Lee.
作者:	Lee, Han-Hsing,
面頁冊數:	1 electronic resource (184 pages)
附註:	Source: Dissertations Abstracts International, Volume: 69-10, Section: A.
Contained By:	Dissertations Abstracts International69-10A.
標題:	Essays. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3293898
ISBN:	9780549378198

Two Essays in Financial Economics: I. Empirical Performance of the Constant Elasticity Variance Option Pricing Model. II. Default Prediction of Alternative Structural Credit Risk Models and Implications of Default Barriers /
Lee, Han-Hsing,

Two Essays in Financial Economics: I. Empirical Performance of the Constant Elasticity Variance Option Pricing Model. II. Default Prediction of Alternative Structural Credit Risk Models and Implications of Default Barriers /Han-Hsing Lee. - 1 electronic resource (184 pages)

Source: Dissertations Abstracts International, Volume: 69-10, Section: A.

I. In this essay, we empirically test the Constant-Elasticity-of-Variance (CEV) option pricing model by Cox (1975, 19961) and Cox and Ross (1976), and compare the performances of the CEV and alternative option pricing models, mainly the stochastic volatility model, in terms of European option pricing and cost-accuracy based analysis of their numerical procedures. In European-style option pricing, we have tested the empirical pricing performance of the CEV model and compared the results with those by Bakshi, Cao and Chen (1997). The CEV model, introducing only one more parameter compared with Black-Scholes formula, improves the performance notably in all of the tests of in-sample, out-of-sample and the stability of implied volatility. Furthermore, with a much simpler model, the CEV model can still perform better than the stochastic volatility model in short term and out-of-the-money categories. The empirical evidence also shows that the CEV model has similar stability of implied volatility to those models tested by Bakshi, Cao and Chen (1997). When applied to American option pricing, high-dimensional lattice models are prohibitively expensive. One distinguished feature of the CEV model as opposed to other stochastic volatility models is that it requires only a single dimensional lattice (Nelson and Ramaswamy (1990) and Boyle and Tian (1999)). Therefore, we also compare pricing performances of the CEV and stochastic volatility numerical methods in terms of two aspects: numerical accuracy and computational efficiency. Our experiments clearly show that the CEV model performs much better in terms of the speed of convergence to its closed form solution, while the implementation cost of the SV model is too high and practically infeasible for empirical work. In summary, with a much less implementation cost and faster computational speed, the CEV option pricing model could be a better candidate than more complex option pricing models, especially when one wants to apply the CEV process for pricing more complicated path-dependent options or credit risk models. II. Following the seminal work of Black and Scholes (1973) and Merton (1974), the structural credit risk modeling literature has developed into an important area of research in finance. While most of the empirical studies try to test the performance of structural models in bond and credit derivatives pricing, little results are provided for default prediction. Therefore, in this study, we empirically compare four structural credit risk models-the Merton, the Brockman and Turtle (2003), the Black and Cox (1976), and the Leland (1994) models-for their default prediction capabilities. We adopt the Maximum Likelihood Estimation method proposed by Duan (1994) and Duan et al. (2004), which views the observed equity time series as a transformed data set of unobserved firm values with the theoretical equity pricing formula serving as the transformation. This method has been shown by Ericsson and Reneby (2005) through simulation experiments to be superior to the commonly adopted volatility restriction approach in the literature. Our empirical results surprisingly show that the simple Merton model has a similar capability in default prediction as that of the Black and Cox model. The Merton model even outperforms the Brockman and Turtle model, and the difference of predictive ability is statistically significant. In addition, we also find that the inferior performance of the Brockman and Turtle model may be the result of its unreasonable assumption of the flat barrier. In the one-year out-of-sample test, the Leland model outperforms the Merton model in the non-financial sector and the results hold for two alternative definitions of default. In summary, our empirical results indicate that exogenous default barriers, flat or exponential, are not crucial in default prediction. In contrast, modeling endogenous barrier has significant improvement in long term prediction for non-financial firms. However, we should note that the performance of the Leland model compared to the Merton model is weakened as the default prediction horizon shortened. 1 A revised version of the paper was published by the Journal of Portfolio Management (1996).

English

ISBN: 9780549378198Subjects--Topical Terms:

642973
Essays.
Subjects--Index Terms:

CEV

Two Essays in Financial Economics: I. Empirical Performance of the Constant Elasticity Variance Option Pricing Model. II. Default Prediction of Alternative Structural Credit Risk Models and Implications of Default Barriers /
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520 $a I. In this essay, we empirically test the Constant-Elasticity-of-Variance (CEV) option pricing model by Cox (1975, 19961) and Cox and Ross (1976), and compare the performances of the CEV and alternative option pricing models, mainly the stochastic volatility model, in terms of European option pricing and cost-accuracy based analysis of their numerical procedures. In European-style option pricing, we have tested the empirical pricing performance of the CEV model and compared the results with those by Bakshi, Cao and Chen (1997). The CEV model, introducing only one more parameter compared with Black-Scholes formula, improves the performance notably in all of the tests of in-sample, out-of-sample and the stability of implied volatility. Furthermore, with a much simpler model, the CEV model can still perform better than the stochastic volatility model in short term and out-of-the-money categories. The empirical evidence also shows that the CEV model has similar stability of implied volatility to those models tested by Bakshi, Cao and Chen (1997). When applied to American option pricing, high-dimensional lattice models are prohibitively expensive. One distinguished feature of the CEV model as opposed to other stochastic volatility models is that it requires only a single dimensional lattice (Nelson and Ramaswamy (1990) and Boyle and Tian (1999)). Therefore, we also compare pricing performances of the CEV and stochastic volatility numerical methods in terms of two aspects: numerical accuracy and computational efficiency. Our experiments clearly show that the CEV model performs much better in terms of the speed of convergence to its closed form solution, while the implementation cost of the SV model is too high and practically infeasible for empirical work. In summary, with a much less implementation cost and faster computational speed, the CEV option pricing model could be a better candidate than more complex option pricing models, especially when one wants to apply the CEV process for pricing more complicated path-dependent options or credit risk models. II. Following the seminal work of Black and Scholes (1973) and Merton (1974), the structural credit risk modeling literature has developed into an important area of research in finance. While most of the empirical studies try to test the performance of structural models in bond and credit derivatives pricing, little results are provided for default prediction. Therefore, in this study, we empirically compare four structural credit risk models-the Merton, the Brockman and Turtle (2003), the Black and Cox (1976), and the Leland (1994) models-for their default prediction capabilities. We adopt the Maximum Likelihood Estimation method proposed by Duan (1994) and Duan et al. (2004), which views the observed equity time series as a transformed data set of unobserved firm values with the theoretical equity pricing formula serving as the transformation. This method has been shown by Ericsson and Reneby (2005) through simulation experiments to be superior to the commonly adopted volatility restriction approach in the literature. Our empirical results surprisingly show that the simple Merton model has a similar capability in default prediction as that of the Black and Cox model. The Merton model even outperforms the Brockman and Turtle model, and the difference of predictive ability is statistically significant. In addition, we also find that the inferior performance of the Brockman and Turtle model may be the result of its unreasonable assumption of the flat barrier. In the one-year out-of-sample test, the Leland model outperforms the Merton model in the non-financial sector and the results hold for two alternative definitions of default. In summary, our empirical results indicate that exogenous default barriers, flat or exponential, are not crucial in default prediction. In contrast, modeling endogenous barrier has significant improvement in long term prediction for non-financial firms. However, we should note that the performance of the Leland model compared to the Merton model is weakened as the default prediction horizon shortened. 1 A revised version of the paper was published by the Journal of Portfolio Management (1996).
546 $a English
590 $a School code: 0461
650 4 $a Essays. $3 642973
650 4 $a Research. $3 531893
650 4 $a Accuracy. $3 3559958
650 4 $a Random variables. $3 646291
650 4 $a Estimates. $3 3561047
650 4 $a Laplace transforms. $3 3690299
650 4 $a Numerical analysis. $3 517751
650 4 $a Efficiency. $3 753744
650 4 $a Finance. $3 542899
650 4 $a Copyright. $3 601694
650 4 $a Stochastic models. $3 764002
650 4 $a Parameter estimation. $3 567557
650 4 $a Integral equations. $3 589037
650 4 $a Brownian motion. $3 3774160
653 $a CEV
653 $a Constant elasticity of variance
653 $a Credit risk
653 $a Default prediction
653 $a Financial economics
653 $a MLE
653 $a Option pricing
653 $a Option pricing model
653 $a Structural model
690 $a 0508
710 2 $a Rutgers The State University of New Jersey, Graduate School - Newark. $e degree granting institution. $3 3828420
720 1 $a Lee, Cheng-Few $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 69-10A.
790 $a 0461
791 $a Ph.D.
792 $a 2007
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