Analysis Of Market Risk Based On The Weighted Local Linear Estimation Of The Binuclear

By the impact of economic globalization, information technology and financial theory and other factors, global financial markets have been developing rapidly the last three decades. This makes the global financial markets have become more open, and capital flows faster and more liberalization around the world. Capital of different characteristic risks reconfigure and combine around the global financial markets, which leads to the mode of operation and performance of the risk of the global financial markets generate a great deal of changes.so there has been an unprecedented volatility over the financial market. At the same time, financial institutions launch a series of financial innovation in order to avoid financial risks and enhance the market competitiveness, and these activities appear to be extremely active under the conditions of the technological advances and the simulation of deregulation. With the rapid development of the global economy, financial globalization and financial liberalization gradually strengthened. Market risks generating from which have become a hot attention, experts have carried out research in this area all over the world. How to quantify market risks, that is how to measuring market risks, has become a big problem that needing to be resolved in front of us.The main tools that have being used or have been proposed to measure the risk are Standard Deviation, Absolute Deviation, Deviation, Lower Partial Moment, Value at Risk(VaR), Conditional Value at Risk(CVaR), Conditional Expected Shortfall(CES) and so on. Due to the recognition of the Basel Committee on VaR, global financial analysts have been in favored of VaR, and VaR was chosen as the international standard for risk management of financial institutions. With the growing development of VaR model and its calculation and optimization, after the Artzner proposed the consistency axiom in 1999, the standard that taking VaR as a risk measurement tool has been questioned, because that some researchers confirmed that the VaR don’t satisfied the sub-additivity property in both theoretical and empirical analysis, and thus draw a conclusion that VaR is not a coherent risk measurement tool. In this case, in order to compensate for the lack of VaR, people began to construct and design a risk measurement tool that satisfy both estimate easily and the consistency axiom. Rockafellar and Uryasev proposed CVaR; Scaillet proposed the nonparametric estimation of CES; Artzner put forward the worst conditional expectation(WCE); Acerbi proposed spectral risk measurement, etc. and proved that they are both coherent risk measurement tools and can calculate easily. Due to the advantages relate to VaR, CES and CVaR is widely used as a risk measurement tool.This paper describes research conditions on VaR、CES and CVaR at home and abroad, and introduced four methods of selecting windows width:subjective selection method and the reference standard distribution method, rule of thumb and unbiased least squares cross-empirical method. Here I use the local linear estimate to estimate the conditional expected shortfall and conditional value at risk. The window width was selected by rule of thumb in the simulation. I use the R software program to generate random numbers to simulate CVaR and CES estimates, and display the values at different quantiles and compare to each other, then analyze the changing trends at CVaR and CES. Then, I select the Shanghai Composite Index and Shenzhen Component Index for the study, then I use Eviews to draw the graph of the daily return rate of stock and take ADF test for the daily return rate of stock. and found that the Shanghai Composite Index and Shenzhen Component sequence diagram fluctuate around zero, so that the series is stationary. then I calculate the CVaR and CES values of stock market data by using the Local linear estimation, and calculate the percentage of which exceed the real VaR to verify the simulation conclusions.