Financial market volatility based on complex network and fuzzy logic theory

Abstract

Volatility is an inherent attribute of the financial market, which is usually expressed as the degree of volatility of financial asset prices. The volatility of the financial market means that there is uncertainty or risk in the market. This paper mainly studies financial market fluctuations based on complex networks and fuzzy logic theory. This article first systematically organizes and summarizes the theoretical construction of complex networks and fuzzy logic. In terms of complex networks, the definition of complex networks, the theory of commonly used functions (classical models of complex networks) and the solving methods are sorted out. In the construction of fuzzy logic theory, starting with quantifiable financial market volatility indicators, the construction models of realized volatility and implied volatility are discussed, and complex network models of implied volatility and model-free models are discussed. The theoretical construction methods were compared and analyzed. Finally, it summarizes the theoretical construction methods of implied volatility index and points out the advantages of model-free implied volatility as a market volatility and risk measurement index, which contains more effective future risk information and is based on implied volatility. The empirical research on indexes and complex network models has laid a theoretical foundation. Experimental data shows that the bond market and the foreign exchange market have the largest fluctuations in the correlation coefficient, reaching 0.35; followed by the stock market and the bond market, which is about 0.17; the stock market and foreign exchange market with the smallest fluctuations are about 0.08. The experimental results show that the financial market volatility research data based on complex networks and fuzzy logic theory is more accurate.