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Long Memory In The Modeling Of Financial Markets Applications

Posted on:2010-10-08Degree:DoctorType:Dissertation
Country:ChinaCandidate:L DengFull Text:PDF
GTID:1119360302457578Subject:Quantitative Economics
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Since 1980s, the long memory theory has been developed very fast in the Econometrics area, and been put into application in the Finance widely. However, as far as the theoretical research is concerned, there is no consensus among the question on how to test a stochastic process is I(0), I(d), or I(1) . Moreover, the estimation and test method is only fit for the simple model, and there is no mature analysis when there is affection of short memory term and structural break. Therefore, theoretical improvement is highly needed. As to the empirical research, there is evidence that the return series and its volatility show long memory properties in the financial market, for example, the stock market, which means that the Efficient Market Hypothesis (EMH) is no longer existed. In addition, shock from government policy may change the long term characteristics of the market, lose sight of which may influence the EMH judgment and the related policy. So it is great importance of exact model specification on the investment portfolio and risk management. To make all of the above goals into realization, the research on long memory theory must be systematical improved and then put into application in a correct way.Based on the available literatures, this thesis aims at the solution of the present theoretical and empirical problems and makes research on the long memory theory with its application in the financial market. The main contribution of the thesis are as follows: Firstly, based on an AKFIMA(p, d, q) process, it compares the finite sample properties of three semiparametic estimator on the fractional integration parameter d, and makes simulations on the influence of the short memory term on the estimation of d and the test of long memory under the alternative hypothesis by Monte Carlo approach. Secondly, the influence of the different structural breaks terms is analyzed based on an AO model in which the error term is a fractional noise. And then, the performance of BP test on unknown muli-break points is simulated via Monte Carlo method under small samples. Thirdly, taking the change of the long memory parameter as the market efficiency's altering, the latent impact of structural breaks to China's stock market is studied. At last, the effect of the government policy is evaluated and advices are given.The study on the long memory theory with its application on financial market is deployed gradually. The whole thesis comprises six chapters. The first one is introduction, which briefly demonstrates the background and significance of the research, the goals and method used, and the innovations. The next three are theoretical study. Thereinto, the second chapter is the long memory process and model study in which we introduce the definition, characteristics, and ARFIMA and FIGARCH model with the estimation. Above on this, the third and forth chapter analyze the impact of short term and structural breads on the modeling of long memory process. The fifth chapter is empirical research which starts with an verification of the "Joseph effect" in the oil futures market, and then the EMH in China's stock and government intervention is researched via the long memory and struck breaks approach. The last chapter is conclusion and future research interest. The main conclusions of the thesis are as follows:Firstly, When the process is a general ARFIMA(p, d, q) rather than fractional noise, the distributions of the semi-estimators are still normal, however, the mean has some deviation from the real value, which cause power and size problems in the test. And the AR and MA parameters have different short term effects. Moreover, we suggest chose longer bandwidth in the practice.Secondly, through the long memory test on an AO model with fractional noise errors, we find that the structural breaks especially the trend breaks can cause overestimate of the long memory parameter, and lead to high power and size. In this situation, the GPH estimator is more robust with smaller bias. Next, the Bai and Perron(1998, 2003) test is extended into long memory conditions and shows a fairly well performance except that d is close to 0.5. Especially when d<0, the distribution of the estimator and the test are even better. Besides, the test is relatively robust to the location and change of the break points.Lastly, through the empirical research on the oil futures market, it is indicated that the shock of innovation to the investment risk is a long memory process. Therefore, the FIGARCH model should be used in practice to forecast the oil futures price and its volatility and the related policy should be make to keep the stable economic growth. Through the retest of the long memory properties in our stock market, it shows that the change of the price have something to do with the government policy, which has different impact to the market efficiency in different periods. Therefore, the policy-leading way on management of the stock market should be abandoned in the future which is the exact way for the market to go to efficiency.
Keywords/Search Tags:Long memory, Fractional noise, Structural break, Market efficiency
PDF Full Text Request
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