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A bivariate functional coefficients regression model for characterizing dynamics of security price is presented in this paper to reflect the feature of real financial market that is presence of repelling and mingling of different opinions amongst traders. The local polynomial regression approach is adopted to estimate the functional coefficients resulting from bayesian updating mechanism. After applying...
Inflation forecasts becomes a key input of monetary policy decision. CPI is a measure of inflation, however, an important economic indicator. Based on the monthly CPI data from January 2000 to December 2009, the thesis firstly statistically indentifies the correlation function and the partial correlation function of consumer price index, tests the stationarity of ADF, then uses ARIMA model to test...
The volatility of the oil future price is extremely complex, therefore an accurate forecasting on oil price is an important and challenging topic. This paper presents a GRNN forecasting model for Brent crude oil price. Careful attention is paid on finding number of features as input data to achieve best performance for model. Also to overcome unforeseen critical conditions, a crisis index is defined...
It is well known that distributions of financial return are fat-tailed and many models have been developed to capture fat-tail. It is not so well known that distributions are skewed. We construct skewed distributions based on symmetric distributions using Fernandez-Steel method and research the prediction of volatility together with APARCH model. Empirical results show that there are significant influences...
Stock index in security market directly reflects the trend and level of the overall market stock price. Therefore, the price prediction directly affects investment decisions and is closely related to economic interest of investors. However, with specific volatility and uncertainty in stock market, changes in stock price index are influenced by many factors, which make it very difficult for the traditional...
A modified GM(1,1) model is constructed by producing new data sequence with the method of transforming every datum of raw data sequence into its n-th root. It is demonstrated that the property of the modified GM(1,1) model is superior to GM(1,1) model by numerical experiment. Moreover the modified model is applied in the prediction of fruit price index in China. The actual results also show that the...
Since the subprime crisis, the variance of housing price is receiving increasing attention especially because of its complexity and practical applications. This paper applies the flexible neural tree model for forecasting the housing price index (HPI). The optimal structure is developed using the modified breeder genetic programming (MBGP) and the free parameters encoded in the optimal tree are optimized...
The reverse mortgage is an important financing instrument which introduced housing finance or housing mortgage loan, it will effectively solve the problem of scarce resources for the aged; however, many problems occurred when brought into effect. One of the most critical problems is pricing. Since the pricing of Reverse Mortgage is such a complex problem, the paper would focus on the fluctuation of...
Stock index forecast is not an easy job as it is subject to influence of various factors. Since 1980s, many researchers have used Back Propagation Neural Network BPNN to forecast stock price fluctuations. However, there are some limitations with BPNN. With slow convergent speed and low learning efficiency, BP learning algorithm is easy to get in local minimum and is far from being perfect in stock...
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