Research On Integrated Measurement Of Stock Liquidity Risk And Market Risk

The "stock-related disasters" incident in 2015 caused the Chinese stock market to fluctuate,and the panic and impact triggered by it quickly spread to other financial markets,triggering drastic fluctuations in the entire financial market.This liquidity crisis has caused Chinese investors to pay a terrible price,so it is imperative to establish a comprehensive risk monitoring mechanism that incorporates liquidity risk and prevent"risks" before they oecur.How to accurately measure risks has always been a hot topic in the academic world.Different types of risks have different characteristies and cannot be summarized.Liquidity risk and market risk are the main risks in the stock market.There is no simple positive or negative correlation between the two.Investors can influence the stock price in the process of buying or selling stocks.This effect is affected by many factors,such as the investor's psychological expectations,market supply and demand,and the intrinsic value of the stock itself.When market risk arises,price fluctuations will lead to lack of liquidity and even to liquidity risk,which in turn will aggravate price fluctuations and increase market risk.The two risks are related to each other and affect each other.Sometimes they appear to be mutually offsetting and weakening,and sometimes appear as a superposition and expansion of risks.Therefore,how to measure the complex correlation structure between risk sequences with non-normal distribution and quantify this related structure is the focus of this study.This article summarizes the theoretical achievements and empirical data of domestic and foreign scholars,and summarizes the methods of liquidity risk measurement,market risk measurement and integrated risk measurement.On this basis,the concept of integrated risk is analyzed,and the correlation research between liquidity risk and market risk is combed.The theory of liquidity premium suggests that there is a positive correlation between yield and illiquidity,which provides a theoretical premise for the integration of measurement of liquidity risk and market risk.At the same time,past empirical data shows that neither market risk nor liquidity risk obey normal distribution.The former has the phenomenon of "spikes and tails",and the latter shows different statistical characteristics according to different liquidity indicators.In order to more accurately describe risk characteristics,GARCH was used to characterize the marginal distribution of the two risk sequences.The Copula function can accurately characterize the non-linear,asymmetric correlations between assets and can capture changes in this related structure between assets,especially tail-related changes.Therefore,we choose Copula function as the joint distribution of the two risks.To quantify the risk,Monte Carlo simulations were performed based on the optimal Copula parameter estimates and the VaR and CVaR values were calculated.Through the correlation test,it shows that there is a positive correlation between liquidity risk and market risk.The two are mutually leading and causal.Among them,market risk has a strong explanation for liquidity risk,and liquidity risk is relatively weak.This conclusion confirms from the side the existence of the liquidity premium phenomenon in China's stock market.Analyzing the empirical results,we found that both liquidity risk and market risk have a fluctuating accumulation effect.Once the risk arises,it is difficult to eliminate it immediately.External shocks will also increase the risk volatility,and this volatility will continue for some time.According to the joint distribution of the Copula function,it is found that when the bear market,the reduction of the stock market yield is accompanied by a decrease in liquidity;in the bull market,when the stock yield is high,the liquidity will also decrease.That is,when the price of the stock market soars or plungs,liquidity risk is easily generated.In addition,the simple assumption of a simple addition of the VaR value significantly underestimates the underestimation of the stock's integration risk,and the stock integration risk measured by the corresponding Copula function is significantly greater.