Close Encounters of Economics
관련링크
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" Economic systems are widely acknowledged as extremely complex, and have recently become an interesting research area for physicists as well as economists. A numerous studies analyzing financial data have been carried out to understand the complex economics systems which made up of heterogeneous agents. While the nonlinear and complex analyzing of financial systems such as stocks and foreign exchange rates exhibits diverse features different from the random-walk process based on the efficiency market hypothesis (EMH). The purpose of this talk is to understand the intrinsic characteristics of financial markets, for example, long-memory and clustering behavior of volatility data and interactions between individual stocks.
First, I analyze the statistical and nonlinear characteristics of financial systems. To observe these features, I employs the diverse methods. I have find that the returns of stock market indices follow a universal power-law distribution with an exponent of 3. And no significant long-term memory property is detected in the return series, while a strong long-term memory property find in the volatility time series and can be attributed to the volatility clustering.
Secondly, I analyze the multifractality and randomness of foreign exchange rates by using the multifractal detrended fluctuation analysis (MF-DFA) and approximate entropy (ApEn) methods. I find that the Korean and Thai foreign exchange markets after the Asian currency crisis increases significantly in multifractality compared to Hong Kong and Japan. In addition, I analyze the randomness (or efficiency) from the foreign exchange rates for 17 countries from 1984 to 2004. I find that on average, the ApEn values for European and North American foreign exchange markets are larger than those for African and Asian ones except Japan.
Thirdly, I analyze the stock network property created by using the minimal spanning tree (MST) method. I use individual stock data listed on the S&P500 and the KOSPI stock markets. I shows that the degree distribution in the network between stocks follows a power-law distribution with an exponent 2.31. In addition, in order to quantify the degree of grouping in the stock market, I propose a novel method by using the shortest path length (SPL) between stocks in the MST structure. I find that the degree of grouping for the United States have a higher value than those of the Korean stock market. In particular, for the Korean stock market the conglomerates, which have the companies belonging to diverse industry sectors, have a significant grouping coefficient"
First, I analyze the statistical and nonlinear characteristics of financial systems. To observe these features, I employs the diverse methods. I have find that the returns of stock market indices follow a universal power-law distribution with an exponent of 3. And no significant long-term memory property is detected in the return series, while a strong long-term memory property find in the volatility time series and can be attributed to the volatility clustering.
Secondly, I analyze the multifractality and randomness of foreign exchange rates by using the multifractal detrended fluctuation analysis (MF-DFA) and approximate entropy (ApEn) methods. I find that the Korean and Thai foreign exchange markets after the Asian currency crisis increases significantly in multifractality compared to Hong Kong and Japan. In addition, I analyze the randomness (or efficiency) from the foreign exchange rates for 17 countries from 1984 to 2004. I find that on average, the ApEn values for European and North American foreign exchange markets are larger than those for African and Asian ones except Japan.
Thirdly, I analyze the stock network property created by using the minimal spanning tree (MST) method. I use individual stock data listed on the S&P500 and the KOSPI stock markets. I shows that the degree distribution in the network between stocks follows a power-law distribution with an exponent 2.31. In addition, in order to quantify the degree of grouping in the stock market, I propose a novel method by using the shortest path length (SPL) between stocks in the MST structure. I find that the degree of grouping for the United States have a higher value than those of the Korean stock market. In particular, for the Korean stock market the conglomerates, which have the companies belonging to diverse industry sectors, have a significant grouping coefficient"
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