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Volatility Characteristics And Prediction Of SHIBOR

Posted on:2016-01-21Degree:MasterType:Thesis
Country:ChinaCandidate:S C ZhouFull Text:PDF
GTID:2309330479488596Subject:Quantitative Economics
Abstract/Summary:PDF Full Text Request
Along with the marketization of interest rate, to build benchmark interest rate for the conditions of money market, which can provide pricing reference in monetary market, is the focus of financial reforms in China. Compared with interest rate marketization in developed countries, the pace of China’s interest rate market is relatively backward. At present, our country is in the important period of comprehensive reforms, so all the policies should be cautiously executed. One feasible way is to learn from the experience of foreign interest rate liberalization of financial markets in developed countries, such as LIBOR, and then realize the establishment and improvement of the benchmark interest rate system of our country, according to the contrast analysis of SHIBOR and LIBOR.This paper focus on the comparative analysis of the fluctuation characteristics of SHIBOR and LIBOR, the causal relationship between SHIBOR and LIBOR, and prediction of SHIBOR, so as to understand mechanism of Shanghai inter-bank offered rate. The results are as follows: First, both SHIBOR and LIBOR are non-stationary, but LIBOR is more stable than SHIBOR; Second, LIBOR is the Granger reason for SHIBOR, but SHIBOR is not the Granger reason for LIBOR; Third, SHIBOR is more vulnerable to the impact of market factors, while the LIBOR is more easily affected by the policy factors and the ability to feedback the price of long-term funds is stronger; Fourth, combining empirical model decomposition method(EMD) and Elman neural network, the model can obtain better prediction effect. Finally, according to the results of empirical analysis, this paper puts forward to three suggestions to improve the mechanism of SHIBOR.
Keywords/Search Tags:inter-bank offered rate, error correction model, empirical model decomposition, neural network, prediction
PDF Full Text Request
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