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研究生:高瑞朋
研究生(外文):BONALA, GOWTHAM REDDY
論文名稱:標的資產流動性對選擇權價格影響- 台指選擇權為例
論文名稱(外文):THE IMPACT OF LIQUIDITY OF UNDERLYING ASSET ON OPTION PRICE - TXO AS AN EXAMPLE
指導教授:陳俊洪陳俊洪引用關係
指導教授(外文):CHEN, JUN-HOME
口試委員:莊明哲林麗嬌
口試委員(外文):CHUANG,MING-CHELIN, LISA
口試日期:2022-06-01
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:企業管理系碩士班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:51
中文關鍵詞:幾何布朗運動平賭過程均數修正法流動性Lévy 過程
外文關鍵詞:Geometric Brownian motionNormal Inverse GaussianVariance GammaMean-Correcting Martingale MeasureReturn over trading volume
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  • 點閱點閱:104
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  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:0
由於金融數據報酬分配有厚尾(Heavy-tailed)與高狹峰(Leptokurtic)現象,而過去文獻在評價選擇權價格時大都假設標的資產動態過程服從幾何布朗運動(Geometric Brownina Motion, GBM),因此,無法精確捕抓到上述情況。本研究利用NIG與VG兩個time-changed Lévy 過程來建構標的資產動態過程,並透過動差法來估計模型的參數。平賭過程(Martingale Process)則利用Mean-Correting 法來求得。實證結果發現NIG與VG模型的評價績效較GBM模型好,此外,考量標的資產流動性對於選擇權的價格影響,將樣本區分為高流動性與低流動性,實證結果亦發現標的資產流動性較佳的情況下,有助於提升評價的績效。
In this paper, I evaluate the European option by assuming that underlying stock returns are not normally distributed. The main reason for this research is that financial securities always have fat tails and excess kurtosis. Apart from this, there are also return volatilities and jumps in the underlying stock price which varies stochastically over a period of time and also results in non-normal returns. The Geometric Brownian Motion (GBM) model is usually used for depicting the underlying asset in option pricing. However, the GBM model cannot depict the stylized features existing in the financial asset return well. So we acquire with time changed levy’s processes Normal inverse Gaussian (NIG) and Variance Gamma (VG) distribution methods called which was proposed by Barndorff-Nielsen (1995, 1998) and Madan and Senata (1990), respectively for option pricing. Due to these two time-changed levy’s processes takes more stylized features of the underlying asset. I use the moment method to calculate the parameters for the model and apply the mean-correcting method to derive the martingale process of the underlying asset dynamic. Here, we use liquidity proxy to show the liquidity impact in option pricing the entire sample has been classified into two categories: Low and High liquidity according by taking the Return over the Trading Volume (RTV) as the proxy to evaluate the liquidity this classification is done based on average trading volume. Finally, numerical illustrations are provided and analyzed.
摘要 i
ABSTRACT ii
ACKNOWLEDGEMENT iii
TABLE OF CONTENTS iv
LIST OF TABLE vi
LIST OF FIGURES vii
CHAPTER 1 1
INTRODUCTION 1
1.1 Background 1
1.2 Formulation of the problem 6
1.3 Purpose of the study 7
1.4 Benefits of Research 7
1.4.1 Theoretical Aspects 7
1.4.2 Practical Aspects 7
1.5 Limitation of the Study 7
1.6 Report Systematics 8
CHAPTER 2 10
LITERATURES REVIEW 10
2.1 Option pricing by using Monte Carlo simulation Method 10
2.2 Option pricing using NIG model 11
2.3 Option pricing using VG model 13
2.4 Liquidity Effect in Option Pricing 15
2.5 Previous Research in option Pricing 17
CHAPTER 3 20
RESEARCH METHODS 20
3.1 Research Procedure 20
3.2 Research Objects 21
3.3 Research Methods 22
3.4 Description about European option model 22
3.5 Specification of Stock Price: 22
3.6 Measure change by Mean-Correcting Martingale: 24
3.7 Parameter estimation method: 25
3.8 Population and Sampling Method 26
3.8.1 Population 26
3.8.2 Sampling Method 27
3.9 Liquidity Proxy 28
CHAPTER 4 29
RESULT AND DISCUSSION 29
CHAPTER 5 35
CONCLUSION 35
REFERENCE 36
APPENDIX A 40
A.1 Software Specification 40
A.2 Derivation of Moments 41
A.3 Four Moments of NIG Distribution 42
A.4 Four Moments of VG Distribution 42
APPENDIX B 44
B.1 Matlab code for TWSE Option Pricing Simulation 44
B.2 Matlab code for Checking Liquidity Effect on High and Low Liquidity 48
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