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題名 跳躍幅度相關風險下狀態轉換模型之選擇權定價與實證分析
作者 林士貴;劉惠美;陳亭甫;林琮偉
Lin, Shih-Kuei;Liu, Hui-Mei;Chen, Ting-Fu;Lin, Tsung-Wei
貢獻者 金融系
關鍵詞 Esscher轉換法;跳躍擴散模型;狀態轉換模型
Esscher transformation;jump diffusion model;regime switching model
日期 2012-12
上傳時間 8-一月-2016 16:13:34 (UTC+8)
摘要 本文使用Esscher轉換法推衍跳躍相關風險下狀態轉換模型的選擇權定價公式。透過敏感度分析,驗證股票市場處於高波動之時間、跳躍幅度的波動度及跳躍頻率三者與買權價格呈現正相關。藉由1999年至2011年道瓊工業指數,使用(Lin, Hsu, Hung and Hung, 2012)所發展的Expectation-Maximization(EM)演算法估計模型參數,並進行選擇權評價。透過AIC、BIC等配適度檢定,驗證跳躍相關風險下狀態轉換模型相較於Black-Scholes模型、狀態轉換模型以及跳躍相關風險下狀態轉換模型,在股價指數報酬率有較佳的配適能力。另外,根據選擇權市場資料的驗證結果,跳躍相關風險下狀態轉換模型在價平及價外的定價誤差皆為最小,整體而言,是Black-Scholes模型、狀態轉換模型、跳躍相關風險下狀態轉換模型以及跳躍相關風險下狀態轉換模型之中最適合的評價模型。
In this paper, we derive the option pricing formula of the regime switching model with dependent jump size risks by Esscher transformation. The sensitivity analysis reveals the period of the higher volatility, the volatility of the jump sizes, and the jump frequency are positively correlated with the price of European call index option. The parameters of Black-Scholes model (BSM), regime switching model (RSM), regime switching model with independent jump size risks (RSMIJ) and regime switching model with dependent jump size risks (RSMDJ) are estimated form Dow-Jones industrial average index over the period 1999 to 2011 by EM algorithm inferred by (Lin, Hsu, Hung and Hung, 2012) and we inference that RSMDJ model has relatives goodness to fit the returns of index data by AIC and BIC values. Furthermore, according to the empirical studies of pricing error with option data, RSMDJ model provides the best pricing ability compared with Black-Scholes model, regime switching model and regime switching model with independent jump size risks. Overall, RSMDJ is the relatively best model of fitting the return of stock index and option pricing model.
關聯 風險管理學報,14(2),161-187
資料類型 article
dc.contributor 金融系
dc.creator (作者) 林士貴;劉惠美;陳亭甫;林琮偉zh_TW
dc.creator (作者) Lin, Shih-Kuei;Liu, Hui-Mei;Chen, Ting-Fu;Lin, Tsung-Wei
dc.date (日期) 2012-12
dc.date.accessioned 8-一月-2016 16:13:34 (UTC+8)-
dc.date.available 8-一月-2016 16:13:34 (UTC+8)-
dc.date.issued (上傳時間) 8-一月-2016 16:13:34 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/80472-
dc.description.abstract (摘要) 本文使用Esscher轉換法推衍跳躍相關風險下狀態轉換模型的選擇權定價公式。透過敏感度分析,驗證股票市場處於高波動之時間、跳躍幅度的波動度及跳躍頻率三者與買權價格呈現正相關。藉由1999年至2011年道瓊工業指數,使用(Lin, Hsu, Hung and Hung, 2012)所發展的Expectation-Maximization(EM)演算法估計模型參數,並進行選擇權評價。透過AIC、BIC等配適度檢定,驗證跳躍相關風險下狀態轉換模型相較於Black-Scholes模型、狀態轉換模型以及跳躍相關風險下狀態轉換模型,在股價指數報酬率有較佳的配適能力。另外,根據選擇權市場資料的驗證結果,跳躍相關風險下狀態轉換模型在價平及價外的定價誤差皆為最小,整體而言,是Black-Scholes模型、狀態轉換模型、跳躍相關風險下狀態轉換模型以及跳躍相關風險下狀態轉換模型之中最適合的評價模型。
dc.description.abstract (摘要) In this paper, we derive the option pricing formula of the regime switching model with dependent jump size risks by Esscher transformation. The sensitivity analysis reveals the period of the higher volatility, the volatility of the jump sizes, and the jump frequency are positively correlated with the price of European call index option. The parameters of Black-Scholes model (BSM), regime switching model (RSM), regime switching model with independent jump size risks (RSMIJ) and regime switching model with dependent jump size risks (RSMDJ) are estimated form Dow-Jones industrial average index over the period 1999 to 2011 by EM algorithm inferred by (Lin, Hsu, Hung and Hung, 2012) and we inference that RSMDJ model has relatives goodness to fit the returns of index data by AIC and BIC values. Furthermore, according to the empirical studies of pricing error with option data, RSMDJ model provides the best pricing ability compared with Black-Scholes model, regime switching model and regime switching model with independent jump size risks. Overall, RSMDJ is the relatively best model of fitting the return of stock index and option pricing model.
dc.format.extent 1688210 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) 風險管理學報,14(2),161-187
dc.subject (關鍵詞) Esscher轉換法;跳躍擴散模型;狀態轉換模型
dc.subject (關鍵詞) Esscher transformation;jump diffusion model;regime switching model
dc.title (題名) 跳躍幅度相關風險下狀態轉換模型之選擇權定價與實證分析zh_TW
dc.type (資料類型) article