Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach

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姓名

林書賢(Shu-Shian Lin) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
(Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach)

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摘要(中)

本研究旨在探討歷史模擬法以及將其考慮GARCH(1,1)模型後，與Monte Carlo模擬方法的比較。本文利用中國市場之指數並藉由模擬選擇權報酬後檢定其與原始價格路徑之選擇權報酬差異來探討三種模擬方法何者之root mean squared pricing error (RMSE)較小。
本文首先以數值模擬之方法模擬出股價之可能路徑，並利用回測方法迭代求算不同選擇權價性與到期日之履約價格比率，進而計算各價格路徑之歐式買權價格，而後估計之。最後再檢定各模擬方法之選擇權報酬估計值與原始價格路徑之選擇權報酬之RMSE。
研究結果顯示各模擬方法之選擇權報酬估計值大多顯著異於原始價格路徑之選擇權報酬，但發現經GARCH(1,1)模型調整過後的歷史模擬法之RMSE顯著小於歷史模擬法以及Monte Carlo模擬方法。

摘要(英)

This paper utilized a proposed historical simulation, where the effects of a GARCH (1,1) model on an asset’s price path were considered. The Monte Carlo approach was also used to examine the difference in option payoff values between the simulation approaches and the original path. Furthermore, this paper used the root mean squared pricing error (RMSE) to show which simulation model would have a smaller RMSE by examining the RMSE difference between the approaches. This paper applied the approaches to simulate option payoff values on three security indexes series in China from January 4, 2000 to December 31, 2009, using the common back-testing approach. The results showed that the estimated option values were significantly different from the actual option payoff values for the observed period. Finally, it was found that the RMSE of the adjusted historical simulation was less than that of the other two simulation approaches.

關鍵字(中)

★ 模擬方法
★ 選擇權報酬
★ 價格路徑
★ 選擇權評價
★ GARCH

關鍵字(英)

★ Valuation
★ GARCH
★ Option payoff values
★ Price paths
★ Simulation approaches

論文目次

CONTENTS
論文摘要 i
誌謝 iv
CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
Chapter 1: Introduction 1
Chapter 2: Non-parametric pseudo random disturbance simulations and Monte Carlo approach: A comparison of simulated option payoffs 4
2.1 Introduction 4
2.2. Data and Methodologies 6
2.2.1 The Pseudo Random Disturbances approach: The PRD model 6
2.2.2 Introducing GARCH (1,1) volatility: The adjusted PRD model 7
2.2.3 Mixing the Pseudo Random Disturbances: The MPRD-model 8
2.2.4 Monte Carlo simulation method: The MC model 11
2.2.5 The back-tested option payoff values of Hu-Shen 300 12
2.3 Option valuation of the MPRDs and Monte Carlo approach 15
Chapter 3: Comparisons of non-parametric disturbance simulations and Monte Carlo approach 23
3.1. Introduction 23
3.2. Data and Simulations Design 26
3.2.1 The Pseudo Random Disturbances approach: The PRD model 26
3.2.2 Introducing GARCH (1,1) volatility: The adjusted PRD model 27
3.2.3 Mixing the Pseudo Random Disturbances: The MPRD-model 28
3.2.4 Monte Carlo simulation method: The MC model 30
3.3. The back-tested option payoff values of Shenzhen Composite Index 31
3.4. Option valuation of the MPRDs and Monte Carlo approach 35
Chapter 4: A non-parametric simulation with pseudo random disturbance, and Monte Carlo approach 42
4.1. Introduction 42
4.2. Literature Review 44
4.3. Data and Methodologies 47
4.3.1 The Pseudo Random Disturbances approach: The PRD model 47
4.3.2 Introducing GARCH (1,1) volatility: The adjusted PRD model 48
4.3.3 Mixing the Pseudo Random Disturbances: The MPRD-model 49
4.3.4 Monte Carlo simulation method: The MC model 52
4.3.5 The back-tested option payoff values of Shanghai Composite Index 52
4.4. Option valuation of the MPRDs and Monte Carlo approach 56
Chapter 5: Conclusion 64
Reference 66
LIST OF FIGURES
Fig. 2.1 Price paths of original, PRD model and adjusted PRD model 8
Fig. 2.2 Price paths of mixing pseudo random disturbances model (200 paths) 10
Fig. 2.3. Price paths of adjusted mix pseudo random disturbances model (200 paths) 11
Fig. 2.4 Implied volatility surfaces on Hu-Shen 300 15
Fig. 2.5 The RMSE of different moneyness relative to ATM on different time to expiration in the MPRD 21
Fig. 2.6 The RMSE of different moneyness relative to ATM on different time to expiration in the adjusted MPRD 21
Fig. 2.7 The RMSE of different moneyness relative to ATM on different time to expiration in the MC 21
Fig. 3.1 Price paths of original, PRD model and adjusted PRD model 28
Fig. 3.2 Price paths of mixing pseudo random disturbances model (200 paths) 29
Fig. 3.3. Price paths of adjusted mix pseudo random disturbances model (200 paths) 30
Fig. 3.4 Implied volatility surfaces on Shenzhen Composite Index 35
Fig. 3.5 The RMSE of different moneyness relative to ATM on different time to expiration in the MPRD 40
Fig. 3.6 The RMSE of different moneyness relative to ATM on different time to expiration in the adjusted MPRD 40
Fig. 3.7 The RMSE of different moneyness relative to ATM on different time to expiration in the MC 40
Fig. 4.1 Price paths of original, PRD model and adjusted PRD model 49
Fig. 4.2 Price paths of mixing pseudo random disturbances model (200 paths) 50
Fig. 4.3. Price paths of adjusted mix pseudo random disturbances model (200 paths) 51
Fig. 4.4 Implied volatility surfaces on Shanghai Composite Index 56
Fig. 4.5 The RMSE of different moneyness relative to ATM on different time to expiration in the MPRD 62
Fig. 4.6 The RMSE of different moneyness relative to ATM on different time to expiration in the adjusted MPRD 62
Fig. 4.7 The RMSE of different moneyness relative to ATM on different time to expiration in the MC 62
LIST OF TABLES
Table 2.1. Unconditional moments of the Hu-Shen 300 Security Index returns, 2005-2009 8
Table 2.2. Statistical feature of the original price returns and the simulated ones 11
Table 2.3. Statistics of estimated option payoff values, p-value and the RMSE 19
Table 2.3. Statistics of estimated option payoff values, p-value and the RMSE (continued) 20
Table 2.4. t-Test of RMSE differences between the MPRDs and Monte Carlo approaches 22
Table 3.1. Unconditional moments of the Shenzhen Composite Index returns, 2005-2009 27
Table 3.2. Statistical feature of the original price returns and the simulated ones 30
Table 3.3. Statistics of estimated option payoff values, p-value and the RMSE 38
Table 3.3. Statistics of estimated option payoff values, p-value and the RMSE (continued) 39
Table 3.4. t-Test of RMSE differences between the MPRDs and Monte Carlo approaches 41
Table 4.1. Unconditional moments of the Shanghai Composite Index returns, 2005-2009 48
Table 4.2. Statistical feature of the original price returns and the simulated ones 51
Table 4.3. Statistics of estimated option payoff values, p-value and the RMSE 60
Table 4.3. Statistics of estimated option payoff values, p-value and the RMSE (continued) 61
Table 4.4. t-Test of RMSE differences between the MPRDs and Monte Carlo approaches 63

參考文獻

1. Akgiray, V., 1989, Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts, The Journal of Business, 62 (1), 55-80
2. Barone-Adesi, G., K. Giannopoulos and L. Vosper, 1999, VaR without correlations for portfolios of derivative securities, Journal of Futures Markets, 19 (5), 583-602
3. Barone-Adesi, G., K. Giannopoulos and L. Vosper, 2002, Back-testing derivative portfolios with FHS, European Financial Management, 8(1), 31-58
4. Black, F., 1976, The pricing of commodity contracts, Journal of Financial Economics, 3, 167–179
5. Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31 (3), 307–327
6. Boudoukh, J., M. Richardson and R. Whitelaw, 1998, The best of both words, Risk, 11, 64–67
7. Boyle, P., 1977, Options: A Monte Carlo approach, Journal of Financial Economics, 4 (3), 323–338
8. Britten-Jones, M. and S.M. Schaefer, 1999, Non-linear value-at-risk, European Finance Review, 2, 161–187
9. Chu, S.H. and S. Freund, 1996, Volatility Estimation for Stock Index Options: A GARCH Approach, Quarterly Review of Economics and Finance, 36 (4), 431-450
10. Deutsche, H.P., 2002, Derivatives and Internal Models, Palgrave Publishers Ltd., Basingstoke, Hampshire, England.
11. Eberlein, E., J. Kallsen and J. Kristen, 2003, Risk management based on stochastic volatility, Journal of Risk, 5 (2), 19–44
12. Figlewski, S., 2002, Assessing the incremental value of option pricing theory relative to an informationally passive benchmark, Journal of Derivatives, 10 (1), 80–96.
13. Glasserman, P., P. Heidelberger and P. Shahabuddin, 2002, Portfolio value-at-risk with heavy tailed risk factors, Mathematical Finance, 12 (3), 239-269.
14. Goncalves, S. and M. Guidolin, 2006, Predictable dynamics in the S&P 500 index options volatility surface, Journal of Business, 79 (3), 1591-1636
15. Hull, J. and A. White, 1998, Incorporating volatility updating into the historical simulation method for value at risk, Journal of Risk, 1(1), 5-19
16. Okten, G. and W. Eastman, 2004, Randomized quasi-Monte Carlo methods in pricing securities, Journal of Economic Dynamics & Control, 28 (12), 2399-2426
17. Papageorgiou, A. and J. Traub, 1996, Beating Monte Carlo, Journal of Risk, 9 (6), 63–65
18. Rouvinez, C., 1997, Going Greek with VaR, Risk, 10 (2), 57–65.
19. Stutzer, M., 1996, A simple nonparametric approach to derivative security valuation, Journal of Finance, 51 (5), 1633–1652.
20. Tompkins, R.G., 2001, Implied volatility surfaces: Uncovering regularities for options on financial futures, The European Journal of Finance, 7 (3), 198–230.
21. Tompkins, R.G. and R.L. D’Ecclesia, 2006, Unconditional return disturbances: A non-parametric simulation approach, Journal of Banking & Finance, 30 (1), 287-314
22. Wilson, T., 1999, In: Alexander, C. (Ed.), Value at Risk in Risk Management and Analysis, 1. Wiley, Chichester, England, 61–124.

指導教授

羅庚辛(Keng-Hsin Lo)

審核日期

2010-9-28

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