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Empirical Research on the Asymmetric Multifractal Properties in Financial Market Data

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dc.contributor.advisor장우진-
dc.contributor.author이민혁-
dc.date.accessioned2018-05-28T16:11:18Z-
dc.date.available2018-05-28T16:11:18Z-
dc.date.issued2018-02-
dc.identifier.other000000149706-
dc.identifier.urihttps://hdl.handle.net/10371/140590-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 장우진.-
dc.description.abstractAfter the recent financial crisis, the importance of financial market analysis for financial risk management has been emphasized. Financial markets have diverse characteristics that are difficult to explain from the traditional models. Therefore, the effort on describing such characteristics is required. Specifically, many researches are actively conducted on the features of multifractal and asymmetric correlation in financial markets. Multifractal features can be characterized by various fractal features with self-similarity that does not change with scale-
dc.description.abstractit is difficult to represent in a single fractal dimension. This feature can explain the complexity of stock market. The asymmetric correlation, depending on the market trend, represents the asymmetric structure of the financial market. In this context, this dissertation focuses on the asymmetric correlation of multifractal characteristics in the financial market data where the asymmetric market efficiency is measured using asymmetric multifractal property. At first, Price-based Asymmetric Multifractal Detrended Fluctuation Analysis (Price-based A-MFDFA) model is proposed to measure multifractal characteristics which asymmetrically follow the trend of market price. Given that previous models measure the multifractal characteristics based on the entire market, the price-based A-MFDFA model has its advantage by considering the asymmetrical characteristics according to different market conditions. Furthermore, the methods to investigate the cause of multifractal features and the asymmetry are also suggested based on the proposed model. The empirical results in the U.S. financial market data confirms the presence of asymmetric multifractal characteristic and the autocorrelation of the variance in uptrend market and fat-tailed distribution in downtrend market as the cause of multifractality. The results of time-varying asymmetric multifractality show that the difference between the degree of uptrend and downtrend multifractality increases during the financial crisis period. Secondly, a simulation method is applied to prove the ability of capturing the asymmetric multifractal features of the Price-based A-MFDFA model by examining the factors affecting the asymmetric multifractality. In order to mimic the stock market data, an artificial time series with asymmetric features are constructed using the Monte-Carlo simulation. Then, the asymmetric multifractality is observed for each time series using the proposed model. The results show that the proposed model can detect the artificial asymmetric characteristics. In addition, the effects of autocorrelation of time series, autocorrelation of volatility, the skewness and fat-tailed of distribution on the asymmetric long-range dependence and multifractal features are studied. Lastly, a framework for testing the existence of asymmetric long-range dependence and multifractality is proposed. The source of market inefficiency, which has not been identified in previous models, is examined through the uptrend and downtrend multifractal features. The result of thirty four countries suggests that, in the financial crisis period, the difference in the long-range dependence measure and degree of multifractality between uptrend and downtrend increases, whereas the uptrend degree of multifractality has a strong negative correlation with the stock price in financial crisis period. In addition, the relationship between asymmetric long-range dependence and rate of return is tested. In conclusion, the contribution of this dissertation is to further refine the ability of multifractal analysis on asymmetric characteristics in accordance with market conditions as well as the overall market. While past analysis of the overall market focuses on only the downtrend, it is possible to analyze both uptrend and downtrend market through the segmented asymmetric multifractal characteristics. Hence, the proposed model can provide much useful information to various market participants in the perspective of financial risk management.-
dc.description.tableofcontentsChapter 1 Introduction 1
1.1 Resarch motivation and purpose 1
1.2 Theoretical background 5
1.3 Organiation of the research 9
Chapter 2 Asymmetric multi-fractality in the U.S. stock indices using the price-based model of A-MFDFA 10
2.1 Introduction 10
2.2 Price-based A-MFDFA method 13
2.3 Data description 16
2.4 Empirical results of asymmetric scaling behavior 18
2.4.1 Asymmetric fluctuation functions and their dynamics 18
2.4.2 Estimating the generalized Hurst exponent 22
2.4.3 Source of multi-fractality 24
2.4.4 Source of asymmetry 28
2.4.5 Time-varying multi-fractal asymmetry 29
2.5 Conclusion 33
Chapter 3 Study of asymmetric multifractal characteristics through various time series simulations 34
3.1 Introduction 34
3.2 Various probability distribution and time series model 36
3.2.1 Normal distribution 36
3.2.2 Skewed distribution 37
3.2.3 Students t-distribution 37
3.2.4 Autoregressive model 38
3.2.5 Autoregressive conditional heteroscedasticity model 38
3.2.6 Gereralized autoregressive conditional heteroscedasticity model 39
3.3 Method to generate time series using Monte-Carlo simulation 41
3.3.1 Homogeneous time series generating 41
3.3.2 Heterogeneous time series with previous datas sign 41
3.3.3 Heterogeneous time series with precious datas trend 41
3.4 Simulation results 43
3.4.1 Homogeneous time series simulation results 43
3.4.2 Heterogeneous time series with previous datas sign simulation results 50
3.4.3 Heterogeneous time series with precious datas trend simulation results 60
3.5 Conclusion 70
Chapter 4 Evaluating the asymmetric long-range dependence and multifractality of financial markets 72
4.1 Introduction 72
4.2 Methodology 76
4.2.1 Price-based A-MFDFA 76
4.2.2 Evaluating the existence of asymmetric long-range dependence and multifractality 78
4.3 Data description 81
4.4 Results and Discussion 84
4.4.1 Monte Carlo Simulation 84
4.4.2 The results for testing the existence of asymmetric long-range dependence and multifractality in each period 89
4.4.3 Time-varying asymmetric Hurst exponent and multifractality 95
4.5 Conclusion 99
Chapter 5 Concluding Remarks 102
5.1 Summary and contributions 102
5.2 Limitations and future work 106
References 108
Appendix 116
Abstract (in Korean) 149
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dc.formatapplication/pdf-
dc.format.extent2795160 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectMultifractal-
dc.subjectGeneralized Hurst exponent-
dc.subjectAsymmetry-
dc.subjectMarket Efficiency-
dc.subjectFinancial Market Data-
dc.subjectSimulation-
dc.subjectLong-range Dependence-
dc.subject.ddc670.42-
dc.titleEmpirical Research on the Asymmetric Multifractal Properties in Financial Market Data-
dc.typeThesis-
dc.description.degreeDoctor-
dc.contributor.affiliation공과대학 산업공학과-
dc.date.awarded2018-02-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Theses (Ph.D. / Sc.D._산업공학과)
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