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研究生: 陳浚泓
Chen, Jiun-Hung
論文名稱: B-S模式與隨機波動性定價模式之比較:台灣股價指數選擇權之實證
A Comparison between B-S Model and Stochastic Volatility Option Pricing Models: Empirical Evidence from TAIEX Options
指導教授: 許溪南
Hsu, Hsinan
學位類別: 碩士
Master
系所名稱: 管理學院 - 企業管理學系
Department of Business Administration
論文出版年: 2003
畢業學年度: 91
語文別: 中文
論文頁數: 50
中文關鍵詞: 歷史波動性GARCH波動性B-S模式隨機波動性模型台灣股價指數選擇權
外文關鍵詞: TAIEX options, B-S model, stochastic volatility model, GARCH volatility, historical volatility
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  • 自Black and Scholes (1973)發表了著名的選擇權定價模型之後,選擇權的定價理論成為學術界的研究重心,因Black and Scholes 模型簡化的假設引發後續研究對於B-S模型的修正,如隨機波動性模型、隨機利率模型、隨機波動性與跳躍擴散模型等等。對於這些修正模型是否優於B-S模型的議題,國內外許多學者在不同市場的實證探討,大多顯示修正的模型績效優於B-S模型。
    由於台灣股價指數選擇權對國內投資人而言,是一種新的金融商品,其價格行為受到學術界與實務界的關心,但到目前為止文獻上尚未有所評估,因此本研究以台灣股價指數選擇權為標的,採用B-S 模式、Hull & White (1987) 模式及Heston (1993) 模式等三種模型分別配合歷史波動性與GJR GARCH波動性,對台灣股價指數選擇權進行實證研究,衡量及比較理論價格之誤差,並進行誤差原因的分析。本文的實證結果指出:
    1.遠月份台股指數選擇權之平均絕對誤差(MAE)與均方根誤 差(RMSE),其最小誤差絕大多數出現在Heston (1993)模型配合歷史波動性之模式上。
    2.近月份台股指數選擇權在深度價內、價內與價平等價位,所有的模型配合各種波動性均高估了市場的價格;至於價外與深度價外選擇權,平均而言各種模式的理論價格高估了市場價格。
    3.對近月份台股指數選擇權,B-S模型配合歷史波動性為最佳模式,Heston (1993)模型配合GJR GARCH波動性為最差之模式。
    4.對遠月份台股指數選擇權,Heston (1993)模型配合歷史波動性為最佳之模式;Hull & Wtite (1987)模型配合GJR GARCH波動性為最差之模式。
    5.GJR GARCH波動性模型並未優於歷史波動性模型、隨機波動性模型並未優於B-S模型。
    6.各模式之定價誤差與「選擇權價內程度」、「距離到期日時間」、「標的股價指數變動率」、及「波動性」等因素存在有顯著的線性關係。

    Since 1973 Black and Scholes published the famous option pricing model, option pricing theory has become an academic research focus. As a result of many unduly simplified assumptions of the B-S model, many scholars started to modify the B-S model, such as the “stochastic volatility option model”, “stochastic interest rate option model” and “stochastic volatility and poisson jump diffusion option model”. Many scholars devoted themselves to investigate the issue about whether the free-restricted models outperform the B-S model. Most empirical results indicated that the free-restricted models outperform the B-S model.
    Since the stock index option is a new financial derivatives in Taiwan, the pricing behavior of stock index options is concerned by investors as well as academic workers. However, up to date, the pricing behavior of index options has not been found in the literature. This study will bridge this gap. This study investigates the B-S, Hull & White (1987) and Heston (1993) models with historical and GJR GARCH volatilities on “the Taiwan Stock Exchange Capitalization Weighted Stock Index Options.” In particular, this study measures and compares the pricing biases between theoretical and market prices, and proceeds to analyze the cause to pricing biases. The empirical results indicate that:
    1.For far month TAIEX Options, the smallest MAE and RMSE pricing errors take place on Heston (1993) model with historical volatility.
    2.For near month TAIEX Options, all models with two kinds of volatility seem to overprice deep-in-the-money, in-the-money and at money options. However, all models with two kinds of volatilities seem to underprice out-of-the-money and deep-out-of-the-money options.
    3.For near month TAIEX Options, the B-S model with historical volatility outperforms any other models; however, Heston (1993) model with GJR GARCH volatility seems to be the poorest model.
    4.For far month TAIEX Options, the Heston (1993) model with historical volatility seems to be the best model; however, the Hull & White (1987) model with GJR GARCH volatility seems to be the poorest model.
    5.GJR GARCH volatility does not seem to outperform historical volatility and stochastic volatility option pricing model does not seem to outperform the B-S model.
    6.For all option pricing models, the pricing errors are systematically related to the extent to which the options are in-the-money, time to maturity, percentage change in stock index, and the volatility of the underlying assets.

    誌謝 Ⅰ 中文摘要 Ⅱ 英文摘要 Ⅲ 目錄 Ⅴ 表目錄 ⅤⅠ 第壹章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 第三節 本文架構 4 第貳章 文獻探討 5 第一節 國外對於B-S放寬模型相關之實證研究 5 第二節 國內對於認購權證之實證研究 6 第三節 波動性估計方法之文獻 7 第參章 研究方法 12 第一節 選擇權評價模型 12 第二節 參數之估計方法 20 第三節 資料來源 23 第四節 選擇權價格估計程序 24 第五節 定價模型績效評估之方法及誤差分析 25 第肆章 實證結果與分析 28 第一節 台股指數選擇權之定價誤差 28 第二節Wilcoxon 符號秩檢定 33 第三節 台股指數選擇權定價誤差之迴歸分析 36 第伍章 結論與建議 43 第一節 研究結論 43 第二節 對後續研究之建議 45 附錄一 @Risk For Excel 4.5 Versions 蒙地卡羅模擬程式46 附錄二 參數估計之結果 47 參考文獻 48

    一、中文部分
    1.何桂隆(1998),不同波動性估計方法下台灣認購權證評價績效之比較,成功大學企業管理研究所未出版碩士論文。
    2.林佩蓉(2000),Black-Scholes 模型在不同波動性衡量下之表現-股價指數選擇權,東華大學企業管理研究所未出版碩士論文。
    3.林敦舜(2002),台灣認購權證評價之研究-探討二項式及三項式樹狀模型之評價差異,交通大學經營管理研究所未出版碩士論文。
    4.周行一、陳怡雯(2002),「台灣證交所發行量加權股價指數未納入現金股利之再投資因素對投資報酬率及基金績效衡量之影響」,證券市場發展季刊,第14卷第1期,pp. 1-24。
    5.洪啟安(1998),台灣認購權證價格形成之實證研究,長庚大學管理學研究所未出版碩士論文。
    6.陳香君(2001),隨機波動選擇權評價模型之實證-以臺灣認購權證為例,高雄第一科技大學財務管理研究所未出版碩士論文。
    7.張文騰(2001),以電子業為標的之台灣認購權證評價研究-AMM, CRR與B-S模型之比較,輔仁大學管理學研究所未出版碩士論文。
    8.黃大展(2001),隨機波動下的二元樹狀模型之探討,政治大學財務管理研究所未出版碩士論文。
    9.楊玉菁(2001),台灣個股型認購權證評價之研究,彰化師範大學商業教育研究所未出版碩士論文。
    10.趙其琳(1999),波動性預測能力比較—台灣認購權證之實證研究,淡江大學財務金融研究所未出版碩士論文。
    11.蔡立光(1998),台灣上市認購權證定價模型與避險策略之研究,中央大學財務管理研究所未出版碩士論文。

    二、英文部分

    1.Amin, Kaushik, and Robert Jarrow (1992), “Pricing Options on Risky Assets in a Stochastic Interest Rate Economy,” Mathematical Finance, Vol. 2, pp.217-237.
    2.Bakshi, G., Charles Cao and Zhiwu Chen (1997), “Empirical Performance of Alternative Option Pricing Models,” Journal of Finance, Vol. 52, pp.2003-2049.
    3.Bates, David (1996), “Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutschemark options,” Review of Financial Studies, Vol. 9, pp.69-108.
    4.Beckers, Stan (1981), “Standard deviations implied in option prices as predictors of future stock price variability,” Journal of Banking and Finance, Vol. 5, pp.363-382.
    5.Black, F. and M. Scholes (1973), “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, Vol. 81, pp.637-659.
    6.Bollerslev, Tim (1986), “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, Vol. 31, pp.307-327.
    7.Chiras, D. P. and S. Manaster (1978), “The Information Content of Option Prices and a Test of Market Efficiency,” Journal of Financial Economics, pp.213-234.
    8.Corrado, Charles and Tie Su (1998), “An Empirical Test of the Hull-White Option Pricing Model,” The Journal of Futures Markets, Vol. 18, No 4, pp.363-378.
    9.Engle, Robert F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, Vol. 50, pp.987-1007.
    10.Feller, W. (1951), “Two Singular Diffusion Problems,” Annals of Mathematics, Vol. 54, No. 1, pp.173-182.
    11.Gemmill, Gordon (1986), “How useful are implied distributions? Evidence from stock index options,” Journal of Derivatives, Vol. 7, Issue 3, pp.83-96.
    12.Glosten, L. R., Jagannathan R., and Runklr D. (1993), “On the Relation between the Expected Value and the Volatility of Nominal Excess Returns on Stocks,” Journal of Finance, Vol. 48, pp.1779-1801.
    13.Heston, Steven L. (1993), “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options,” Review of Financial Studies, Vol. 6, pp.327-343.
    14.Huang, Yu Chuan and Shing Chun Chen (2002), “Warrants Pricing: Stochastic Volatility vs. Black-Scholes,” Pacific-Basin Journal, Vol. 10, pp.393-409.
    15.Hull, John and Alan White (1987), “The Pricing of Options on Assets with Stochastic Volatilities,” Journal of Finance, Vol. 42, pp.281-300.
    16.Hull, John (2000), Options, Futures, and Other Derivatives, 4th ed., N. J.: Prentice-Hall.
    17.Jiang, George J. (1999), “Index Option Pricing Models with Stochastic Volatility and Stochastic Interest Rates,” European Finance Review, Vol. 3,
    pp.273–310.
    18.Lewis, Alan L. (2000), Option Valuation under Stochastic Volatility with Mathematica Code, Newport Beach: Finance Press.
    19.Lamoureux, Christopher G. and William D. Lastrapes (1993), “Forecasting Stock Return Variance: Toward an Understanding of Stochastic Implied Volatilities,” Review of Financial Studies, Vol. 6, No. 2, pp.293-326.
    20.Long, D. M. and D. T. Officer (1997), “The Relation Between Option Mispricing and Volume in the Black-Scholes Option Model”, The Journal of Financial Research, Vol. 20, pp.1-12
    21.Merton, R. C. (1973), “Theory of Rational Option Pricing,” Bell Journal of Economics and Management Science, Vol. 4, No. 1, pp.141-183.
    22.Nandi, S. (1998), “How important is the correlation between returns and volatility in a stochastic volatility model? Empirical evidence from pricing and hedging in the S&P500 index options market,” Journal of Banking and
    Finance, Vol. 22, pp.589–610.
    23.Nelson, Daniel B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, Vol. 59, pp.347-370.
    24.Salih, N. Neftci (1996), An Introduction to the Mathematics of Financial Derivatives, San Diego: Academic Press.
    25.Scott, L. (1987), “Option pricing when the variance changes randomly: theory, estimation and testing,” Journal of Financial and Quantitative Analysis, Vol. 22, pp.419-438.
    26.Scott, Louis O. (1997), “Pricing Stock Options in a Jump-Diffusion Model with Stochastic Volatility and Interest Rates: Applications of Fourier Inversion Methods,” Mathematical Finance, Vol. 7, pp.345-358.
    27.Vasilellis, G. A. and Meade N. (1996), “Forecasting volatility for portfolio selection,” Journal of Business Finance & Accounting, Vol. 23, pp.125-143.
    28.Wiggins, J. B. (1987), “Option Values under Stochastic Volatility: Theory and Empirical Evidence,” Journal of Financial Economics, Vol. 19, pp. 351-372.
    29.Zakoian, J. M. (1994), “Threshhold Heteroskedastic Models,” Journal of Economic Dynamics and Control, Vol. 18, pp.931-955.

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