The Research Of Var Risk Management Based On Price Range

Nondeterminacy is one of the key problems in financial theory research. Risk is defined as expected yield of assets property, which normally has two performances:effective yield higher oe lower than expected yield. However, risk is usually been defined as loss in our daily life, finance houses are also concentrated on responding to and controlling loss when doing risk management research. As the latest developments in risk management research, VaR(value at risk) has been widely used in recent years. VaR is integrated in a unified framework to measure the risks faced by financial institutions’ asset portfolio. The VaR system, which is initially designed to quantify the market risk of financial assets, has been gradually extended to measure credit risk and other types of risks after scholars’ theoretical study and the financial institutions’ practice. Financial institutions use VaR to develop internal models to measure credit risk based on the asset portfolio they hold. With the deepening of theoretical and applied research, the VaR has gradually been developed into a risk management system to manage a variety of financial risks from the original as a tool for a single quantitative market risk. Financial institutions find potential problems in the application by backtesting VaR, and meanwhile improve and optimize the company’s VaR risk management system when solving the problems. At the same time, as more investors to participate in the stock market, how to control the risk of investing on stock and derivative products in stock reasonably has become a hot topic among investors.Extensive literature study found that current VaR method either only uses the closing price to calculate the yield, without considering the influence of ceiling price and floor price or only uses the price range between ceiling price and floor price to build AV or CARR models without considering closing price. Thus when the closing price, the ceiling and floor price are in the case of separation, there is no comprehensive consideration in a unified framework, using the information contains price range, and the closing price to estimate the probability distribution of financial assets becomes the research direction, especially describing the variance of price volatility. There has been many fruitful research results about using GARCH model, one of parametric techniques, to calculate VaR. Due to different selected samples, there is no consistent conclusion on the various extensions of the GARCH model and which probability distribution to obey as well. GARCH model uses the logarithmic return rate calculated from closing price to build models, ignoring values contained in other values; we consider the ceiling and floor price as complement of closing price to build models, which contain the information of price range. Two main parts are included to deeply study the intraday price range feature in China and the influence of predictive value of VaR.The first part builds the GARCH model to estimate range of conditional variance of yield. As the prices of financial assets are on the basis of certain assumptions, it can be theoretically proved that the price range is an effective alternate variables of volatility using the formula of estimating the volatility by price range. Thus putting price range into the conditional variance equation in GARCH model to calculate the conditional variance with closing price riches the price information in GARCH model and improves the estimate of conditional variance. We also try to explain characteristics of the conditional variance by using the price range.The second part, according to the VaR calculation principles, use conditional variance calculated by price range-GARCH model to calculate the VaR predictive value. Among the three key factors of calculating VaR by using parametric method, financial assets volatility changes over time, which makes volatility the most difficult factor to determine, price range-GARCH model contains the price range, which allows the conditional variance to reflect the risk of yield faces in time, to improves the effectiveness of VaR value and to lower the potential loss of financial assets as well.Through the above research, this paper gets the following conclusion: 1. In Chinese stock market, sequences of yield show obvious volatility gathered, fat-tailed, information asymmetry features.2. Use conditional price range as the lag of conditional variance instead of variable in price range-GARCH model can explain the continued fluctuating yield more effective than other persistence of GARCH Models (normal distribution and Student’s t distribution).3. Study of Value at Risk (VaR) found that price range-GARCH model of conditional variance and the mean calculated value at risk VaR can better reflect the rate of return facing the risk. Meanwhile, Kupiec likelihood ratio test proves its effectiveness.Overall, the innovation of this paper lies in the following areas:Firstly, this methodology brings daily price range into GARCH model to study Chinese stock market and study VaR of Chinese stock market by using price range-GARCH model as well.Secondly, we can see from the article’s conclusions, bringing the price range into the GARCH model can explain the continued fluctuating yield more effective than other persistence of GARCH Models (normal distribution and Student’s t distribution).Finally, because price range-GARCH model has advantages in depicting the stock price changes and VaR forecasting, under the recommendations of the model to develop appropriate management policies and measures will be more in line with the actual situation of China’s stock market and regulatory policy will be more reasonable and effective.