The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Two models are examined in this study: Vector Autoregressive Model (VAR) and Vector Error Correction Model (VECM). Based on three indices: S&P 500, Nikkei 225 (NIKKEI), and Morgan Stanley EAFE (MSCIEAFE or MSCI EAFE) Index, we implement VAR and VECM models, including the pre-estimation diagnostics, model estimation and interpretation and post-estimation tests, etc. By testing, we find that while...
The increasingly fierce competition requires more and more flexible forecasting method of product diffusion behavior. According to combined forecasting, a model can be constructed by combining different product diffusion models to meet the demand for forecasting different product diffusion behaviors. The cooperative game method is applied to determining the weights of base models in linear combined...
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...
Pavement Preventive Maintenance (PPM)technologies is adopted to predict the performance of pavement and select effective strategies on the expressway, the method can enormously extends the service life of pavement and result in lower maintenance costs. This paper describes prediction of highway pavement performance by means of gray system theory, and the gradual solution, which leads to the optimal...
This paper uses GMDH method to establish a prediction model to forecast the number of civil vehicles owned in Guangxi, since the original samples of the civil vehicle population of Guangxi are less enough to be used with the traditional methods. Compared with traditional linear regression and artificial neural network, the predicted results show that GMDH method is an effective way to predict car...
The analysis and modeling of high-frequency financial data are new research fields in financial econometrics. The realized covariance matrix, gotten by expanding realized volatility based on univariate high-frequency data to multivariate high-frequency data, can describe volatility and correlation of multivariate time series. The paper gains the realized covariance matrix of the high-frequency data...
This article firstly presents an analysis and survey regarding the traditional evaluation and forecasting model on fuzzy time series. lt is pointed out that the maximum Subordination degree method and Subordination degree-Weighted average method is not suitable to attribute space usually, and a new evaluation model is proposed. The empirical study show that the new evaluation model is better able...
The paper selects listed companies which were merged or acquired in 2005 as the empirical samples, chooses 24 specific indexes related to financial condition and governance structure of firms. Then the logistic regression analysis is used to establish one forecast model with 73.6 percent general accuracy basing on success rate and another with 75.3 percent general accuracy basing on M&A performance...
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...
This paper proposes an effective hybridization of grey relational analysis (GRA) and Backpropagation Particle Swarm Optimization (BP_PSO) for time series forecasting. The hybridization employs the complementary strength of these two appealing techniques. Additionally the combination of GRA and BP as cooperative feature selection (CFS) has successfully assessed the importance of each input variable...
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...
In this paper an ARIMA model is used for time-series forecast involving wind speed measurements. Results are compared with the performance of a back propagation type NNT. Results show that ARIMA model is better than NNT for short time-intervals to forecast (10 minutes, 1 hour, 2 hours and 4 hours). Data was acquired from a unit located in Southern Andalusia (Pentildeaflor, Sevilla), with a soft orography...
Livestock husbandry is a systems engineering. It canpsilat develop without the support of environment. Doing research on carrying capacity of livestock breeding environment means a lot to continuous development of livestock husbandry. This paper takes Heilongjiang Province as an example, using analysis method and grey forecasting model to analyze and forecast on carrying capacity of livestock breeding...
It is well known that the grey forecasting model has been successfully adopted in various fields and its further improved model is proposed ceaselessly. In this paper, we focus on the prediction problem of Shanghai Stock Index by using GM(1,1) model and OSDGM model. Our simulation results show that two models can effectively predict the developing system.
In this paper, we investigate the statistical properties of fluctuations of Chinese stock index. According to the theory of artificial neural network, a stochastic time effective function is introduced in the forecasting model of the index in the present paper, which gives an improved neural network - the stochastic time effective neural network model. In this model, a promising data mining technique...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, more accurate measures and better forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility, they...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.