유러피안 인덱스 옵션 및 아메리칸 인덱스 옵션 가격 추정을 위한 지수 레비 모델의 모수 추정에 관한 실증적 연구

학위논문 (석사)-- 서울대학교 대학원 : 산업·조선공학부, 2015. 2. 이재욱.본 논문은 3년간의 S&P100 인덱스 옵션 데이터를 사용하여 지수 레비 모형의 모수를 추정하고 동 기간의 일주일 후의 유러피안 옵션 및 아메리칸 옵션 데이터를 예측하여 실증적으로 비교하였다. 머튼, VG, CGMY, Kou 네 가지의 지수 레비 모델의 파라메터를 캘리브레이션을 통하여 추정하였다. 이를 바탕으로 하여 Carr-Madan 의 푸리에 변형을 통한 유러피안 옵션 가격 추정, 및 아메리칸 옵션 가격을 추정하였다. 마지막으로 같은 모수로부터 추정된 아메리칸 옵션과 유러피안 옵션의 가격 예측 결과를 비교해보았다. 그 결과 매우 짧은 만기에서는 아메리칸 옵션의 가격추정이 유러피안 옵션의 가격 추정보다 더 좋은 결과를 나타냈으나, 만기가 증가함에 따라 빠른 속도로 오차가 증가하는 결괄르 보였다. 콜 옵션 가격의 경우 풋 옵션보다 전체적으로 좋은 추정결과를 가져왔다. 일반적으로 알려져 있듯이 OTM 옵션의 가격예측이 ITM이나 ATM 옵션 가격 예측보다 어려운 것을 확인할 수 있었다.목 차 제 1 장 Introduction 2 제 2 장 Calibration Lévy models 3 제 1 절 Parametric Lévy models 3 제 2 절 Carr-Madans Fourier transform methods for option pricing 5 제 3 절 Calibration 6 제 3 장 Numerical Methods for Pricing American Options 6 제 4 장 Empirical Results 8 제 1 절 Data 8 제 2 절 Calibration Result 11 제 3 절 Pricing European options 16 제 4 절 Pricing American put options 26 제 5 장 Conclusion 31 Reference 32 Abstract 34 표 목차 [Table 1] S&P 100 index European option data description 9 [Table 2] S&P 100 index American option data description 10 [Table 3] Annual information of S&P 100 index European option data 11 [Table 4] Annual information of S&P 100 index American option data 11 [Table 5] Calibration Result of S&P index European Call options 14 [Table 6] Calibration Result of S&P index European Put options 15 [Table 7] Prediction Result of S&P index European Call options ver1. 21 [Table 8] Prediction Result of S&P index European Put options ver1. 22 [Table 9] Prediction Result of S&P index European Call options ver2. 24 [Table 10] Prediction Result of S&P index European Put options ver2. 25 [Table 11] Prediction Result of S&P index American Put options ver1. 29 [Table 12] Prediction Result of S&P index American Put options ver2. 30 그림 목차 [Figure 1] The European call option prices according to the moneyness Merton and Variance-Gamma model : blue line is call option prices estimated with calibrated parameter sets and dots are true option prices. : Oct.13. 2010 12 [Figure 2] The European call option prices according to the moneyness CGMY and Kou model : blue line is call option prices estimated with calibrated parameter sets and dots are true option prices. : Oct.13. 2010 12 [Figure 3] The European put option prices according to the moneyness Merton and VG model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices. : Sep.15. 2010 13 [Figure 4] The European put option prices according to the moneyness CGMY and Kou model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices. : Sep.15. 2010 13 [Figure 5] European and American Options Pricing Procedure by Using Calibrated Parameter Sets 16 [Figure 6] The European call option prediction according to the moneyness Merton model : blue line is call option prices estimated with calibrated parameter sets and dots are true option prices.: Apr. 27. 2011 17 [Figure 7] The European call option prediction according to the moneyness V-G model : blueline is call option prices estimated with calibrated parameter sets and dots are true option prices.: Apr. 27. 2011 17 [Figure 8] The European call option prediction according to the moneyness CGMY model : blue line is call option prices estimated with calibrated parameter sets and dots are true option prices.: Apr. 27. 2011 18 [Figure 9] The European call option prediction according to the moneyness Kou model : blue line is call option prices estimated with calibrated parameter sets and dots are true option prices.: Apr. 27. 2011 18 [Figure 10] The European put option prediction according to the moneyness Merton model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 30. 2012 19 [Figure 11] The European put option prediction according to the moneyness V-G model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 30. 2012 19 [Figure 12] The European put option prediction according to the moneyness CGMY model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 30. 2012 20 [Figure 13] The European put option prediction according to the moneyness Kou model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 30. 2012 20 [Figure 14] Prediction measure plot and S&P 100 index time series 23 [Figure 15] The American put option prediction according to the moneyness Merton model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 02. 2012 27 [Figure 16] The American put option prediction according to the moneyness V-G model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 02. 2012 27 [Figure 17] The American put option prediction according to the moneyness CGMY model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 02. 2012 28 [Figure 18] The American put option prediction according to the moneyness Kou model : blue line is put option prices estimated with calibrated parameter sets and dots are true option prices.: May. 02. 2012 28Maste

블록체인, 가상화폐, 파생상품 시장을 위한 예측 모형

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 이재욱.This dissertation aims to conduct the empirical analysis for the financial derivative and cryptocurrency market and to develop analytical techniques based on machine learning models suitable for prediction and estimation of each field. In the financial derivative market, a Markov chain Monte Carlo (MCMC) methods employ the candidate probability distribution nearest to the target probability distribution to acquire sample distributed from the posterior density. Choice of the candidate probability distribution affects the practical convergence speed of the MCMC methodology and the fitness of the sample. In this dissertation, we propose a MCMC framework possible to samples from the candidate distribution nearest to the target probability density without the specification of the candidate distribution. We confirm that the jump diffusion models and Bayesian neural networks have the best performance in estimating and predicting given the data of the recent day for the model estimation given S&P index options in 2012. Especially, the jump diffusion model has a very high performance in terms of domain adaptation between the American option and the European option. This difference is reflected in the fact that the jump diffusion model is based on the common asset of the American option and the European option. Based on this empirical precedent study, we proposed a machine learning model called generative Bayesian neural network (GBNN) to overcome the disadvantages of the machine learning model. GBNN maximizes posterior probability through the GBNN obtains prior information from the GBNN data learned up to the previous day, and learns likelihood probability from actual trading data of learning day. We identify that the GBNN model outperform other benchmark models in terms of model prediction. Bitcoin is a successful cryptocurrency, and it has been extensively studied in fields of economics and computer science. In this dissertation, we analyze the time series of Bitcoin price with a BNN using Blockchain information in addition to macroeconomic variables. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the Bitcoin price in Aug. 2017. In addition, we suggested the enhanced GRU model for correlation analysis between cryptocurrency markets. Assuming that the gate value obtained from the GRU model is the parameter of the VAR model, it makes possible to visualize the correlation between various alternative currencies in the cryptocurrency market. As a result, it is confirmed that there is a very significant correlation between the currencies separated from the existing currencies and the existing currencies.Chapter 1 Introduction 21 1.1 Financial derivative market analysis 21 1.2 Cryptocurrency market analysis 24 1.3 Aims of the Dissertation 26 1.4 Outline of the Dissertation 28 Chapter 2 Literature Review 29 2.1 Review of Financial Econometric Models 29 2.1.1 Time series models 29 2.1.2 Option pricing methods 34 2.2 Review of Statistical Machine Learning Models 39 2.2.1 Articial neural networks 39 2.2.2 Bayesian neural networks 39 2.2.3 Support vector regression 43 2.2.4 Gaussian process 45 Chapter 3 Predictive Models for the Derivatives Market 47 3.1 Chapter Overview 47 3.2 A Generative Model Sampler for Inference in State Space Model 51 3.2.1 Backgrounds 51 3.2.2 Proposed methods: generative model sampler 56 3.3 Machine Learning versus Econometric Models in Predictability of Financial Options Markets 59 3.3.1 Data description and experimental design 59 3.3.2 Estimation and prediction performance 62 3.3.3 Robustness and Domain Adaptation Performance of the Models 66 3.4 A Generative Bayesian Neural Networks Model for Risk-Neutral Option Pricing 70 3.4.1 Proposed method 70 3.4.2 Empirical Studies 74 3.5 Chapter Summary 86 Chapter 4 Predictive Models for Blockchain and Cryptocurrency Market 89 4.1 Chapter Overview 89 4.2 Economics of Bitcoin and Blockchain 91 4.3 An Empirical Study on Modeling and Prediction of Bitcoin Prices Based on Blockchain Information 93 4.3.1 Data Specication and Structure of the Experiment 93 4.3.2 Linear Regression Analysis 99 4.3.3 Estimation and Prediction Results of Bitcoin Price 104 4.4 Enhanced GRU Framework for Correlation Analysis of Cryptocurrency Market 111 4.4.1 Enhanced GRU Framework 111 4.4.2 Empricial Studies 113 4.5 Chapter Summary 115 Chapter 5 Conclusion 119 5.1 Contributions 119 5.2 Future Work 122 Bibliography 125 국문초록 161Docto

젊은 세대와 기성 세대의 뉴스 리터러시 차이

학위논문 (석사)-- 서울대학교 대학원 : 사회과학대학 언론정보학과, 2018. 8. 윤석민.This study proposed the concept of 'news literacy' as a basis for analyzing the difference in news literacy between the younger generation and the older generation. Specifically, this study proposed a news literacy model based on previous research and empirically examined how the younger generation and the older generation differ in the three dimensions of news literacy: the knowledge structure, the personal locus, and the competencies and skills. To accomplish the purpose of the study, this study conducted an online survey of 862 adults, including the younger generation in their twenties, and the older generation in their fifties. The main findings were as follows. There was no significant difference between the younger generation and the older generation in the knowledge structure. The younger generation did not differ significantly from the older generation in the perception of traditional news information and awareness of the importance of news. However, the younger generation regarded contextual journalism and watchdog journalism to be more important compared to the older generation. Regarding the personal locus, the younger generation showed more tendency to use news for economic opportunity and education-related information than the older generation, but overall there was no significant difference in the motivation for news use. Finally, the younger generation showed a significant difference in each stage of news use, and it appeared that they use more diverse methods and strategies in news approach, analysis, evaluation, and sharing. In particular, it has been confirmed that the younger generation is a group of demanding news users who appreciate not only the journalism norm but also the pleasant user environment and optimized screen composition when evaluating the news. In other words, the younger generation used the news in more diverse ways and evaluated the news in a wider range of criteria when using the news. The results of this study directly contradict the prejudice that the younger generation is a group of ignorant and indifferent news users. The results show that the younger generation is not a group of news users with low news literacy compared to the older generation, but news users with news literacy composed of different competencies from the older generations. The results of this study are significant in that the multi-faceted generational differences in news literacy were identified through a news literacy model.Chapter 1. Introduction 1 1.1 Research Background 1.2 Composition of Paper Chapter 2. Theoretical Background 20 2.1 Generational Difference 2.1.1 Generation 2.1.2 Generational Difference 2.2 Generational Differences in Media Use 2.2.1 Generational Differences in Media Use 2.2.2 The Concept of Media Literacy 2.3 Generational Differences in News Literacy 2.3.1 The Concept of News Literacy 2.3.2 Maksl et al.s (2015) Model of News Literacy 2.3.3 EAVIs (2009) Framework of Media Literacy Competencies 2.3.4 A Model of Generational Difference in News Literacy Chapter 3. Method 74 3.1 Research Questions 3.2 Method 3.2.1 Sample 3.2.2 Instrumentation 3.2.3 Analysis of Data Chapter 4. Results 95 Chapter 5. Discussions 130 5.1 Overview 5.2 Contribution 5.3 Implications Bibliography 142 Abstract in Korean 185Maste