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Dynamic Correlation Nonlocal In Time In Complex Financial Systems

Posted on:2017-01-30Degree:DoctorType:Dissertation
Country:ChinaCandidate:L TanFull Text:PDF
GTID:1109330488989980Subject:Theoretical Physics
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Financial markets, as a kind of typical complex systems with many-body interactions, have drawn much attention of scientists of different fields. A great number of financial data piling up makes it possible to study financial systems quantitatively. In recent yeas, physi-cists have applied various concepts and methods to explore financial systems, provided new insight into financial systems and achieved much progress.From the perspective of physicists, financial systems can be studied by exploring the temporal correlations and spatial correlations. The temporal correlations describe dynamic properties in financial markets, and the spatial correlations characterize interactions of s-tocks. Some properties have been discovered to be universal for emerging and mature stock markets. However, some properties vary form market to market. Therefore, comparative s-tudy on emerging Chinese stock market and mature western stock market would deepen our understanding of financial markets. There are three innovations in this work.1. Through constructing a class of dynamic observables, we detect non-zero volatility-return correla-tion for many financial markets, and the correlation is nonlocal in time. The result indicates that the nonlocality is an intrinsic property in financial systems, which provides new insight into financial dynamics.2. By decomposing the price movement of individual stocks into different modes, we reveal the dominating mechanism of the volatility-return correlation nonlocal in time.3. We explore the interactions of stocks on the minute timescale, and discover that the market states in the morning and afternoon are significantly different. Our main results are presented in Chapter 2,3 and 4.In Chapter 1, we introduce the establishment of financial markets, and give a mini review on econophysics. In brief, econophysics is a new interdiscipline in which various concepts and methods are applied to explore the characteristics of financial markets. E-conophysicists consider financial markets as complex systems, financial data as experi- mental data, and endeavor to reveal the underlying physical laws. Finally, we present our motivation and main results.In Chapter 2, we concentrate on the dynamics of prices in financial systems. What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correla-tion functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Considering that the sce-nario in financial markets is very complicated, interactions and thus correlations could be nonlocal in time. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discov-ered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns.In Chapter 3, we introduce a dynamic observable D(t) nonlocal in time to further explore the volatility-return correlation. The observable can be applied to investigate vari-ous complex financial systems, and in this chapter we take the stock markets and foreign exchange markets for example. For many markets, the correlation computed with the ob-servable in Chapter 2 is zero or very weak, while that computed with D(t) is significantly non-zero. For the other markets, a much more prominent correlation is observed. This volatility-return correlation nonlocal in time, which is designated as the "driving effect of dynamic fluctuations", is a robust and intrinsic dynamic property in complex financial sys-tems. To further understand the origin of this effect, we decompose the price movement of stocks into market mode and sector mode, and reveal that the driving effect is dominated by the sector mode in the NYSE, while by the market mode in the SSE.In Chapter 4, we focus on the interactions of stocks. A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is lim-ited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more sta-ble. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.In Chapter 5, our conclusion is summarized.
Keywords/Search Tags:Statistical mechanics, Econophysics, Financial dynamics, Complex sys- tems, Stock market, Temporal correlation, Stock interactions, Market state
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