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The study of commodity price behaviour has attracts the attention of many economists and finance specialists. This is due to the fact that many less developed countries rely on the revenues generated by the commodity exports. In this paper, the nonlinear relationship because of regime shifts in four vegetable oil price series was investigated. The multivariate Markov switching vector autoregressive...
We use the study method of Computational Finance to explore the formation and evolution of asset prices from the standpoint of the evolution of investor individual's heterogeneous behavior through building an agent-based artificial financial market. In our model,agent will consider fundamental information and price tendency simultaneously at each period to form expectation that based on personal characters,...
China's monetary authority applies monetary policy instruments of both money supply and interest rates. This paper estimates the impact of positive monetary policy shocks (money supply shock and interest rate shock) on house price, and assesses the effects of house price shock on the real economy, using SVAR model spanning the period of 2001Q1 to 2009Q4. In the money supply system, the impacts of...
We propose a multi-agent-based artificial futures market to understand commodity futures price dynamics, which is called stylized facts. The multi-agent-based artificial futures market includes hedger agents and speculative agents. We use Brenner's stochastic belief learning model (SBL) to describe speculative agents' learning process. The SBL is a social learning process with local information. New...
Agricultural industry chains are increasingly vulnerable to natural disaster such as earthquakes or hurricanes. This paper investigates the ripple effect on the agricultural industry chain which is heavily influenced by the natural disaster. Taking the pig industry chain in the disaster area -Luzhou, Sichuan Province as an illustration, the paper was committed to the dynamics of price adjustment and...
Influenced by various kinds of unpredictable factors, the forecast of crude oil price has always been a worldwide challenge. With its own advantages, fractal theory was considered as an ideal method in solving this problem. As a trend extrapolation model based on historical data, data examine process is necessary to make sure the original data is available for trend extrapolation, and using rescale...
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 relationship between land prices and housing prices has been concerned debated widely by public society for a long time. This article first reviewed the domestic and foreign literature about the relationship between land prices and housing prices. Then, collect the national land prices index and the housing prices index from 2000 Q1 to 2009 Q3 as the basic data, and analyze the relationship between...
As the fundamental model tool, time series filter can help specialist and researcher to examine volatility. The time series filter may play a significant role in statistical model building. The foreign law and market bond the behavior of self-interest in emerging market. The empirical evidence results indicate that Coefficient of GARCH (1) in Table 1 is bigger than Coefficient of GARCH (1) in Table...
In order to use the states of price trends or historical volatility to interpret value-at-risk without distributional assumptions, quantile regression method is used to solve the problem. We present the risk measurement model using five lag returns as explanatory variables. To describe the relationship between risk and status we introduce the explanatory variables of price trend states into the model...
This paper proposes a novel technique to forecast day-ahead electricity prices based on Panel Cointegration (PC). The current researches on the electricity price forecasting focus on the analysis of unstable economic time series. However, due to the difference of the allocation of power resource and consumption in different regions, the time series of electricity consumption and sales price in a single...
In a discrete time option pricing framework, we compare the empirical performance of two pricing methodologies, namely the affine stochastic discount factor and the empirical martingale correction methodologies. Using a dataset of S&P500 index options, the differences are found to be small: the higher order moment correction involved in the SDF approach may not be that essential to reduce option...
When one models dependence solely via correlations, portfolio allocation models can perform poorly. This motivates considering dependence measures other than correlation. Cointegration is one such measure that captures long-term dependence. In this paper we present a new method to simulate cointegrated sample paths using the vector auto-regressive-to-anything (VARTA) algorithm. Our approach relies...
This paper analyses the forecasting price of electric futures at PJM power market. An error correction model is proposed to forecast price of electric futures. The model contains not only the lagged variables such as: futures prices and spot prices but also includes the long relationship between the futures prices and spot prices. Results show that the spot price is an important influence factor in...
Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models. The modeling and forecasting ability of ARFIMA-FIGARCH model is investigated...
The empirical results show that the dynamic conditional correlation (DCC) and the bivariate asymmetric-IGARCH (1, 1) model is appropriate in evaluating the relationship of the U.S. and the Canada's stock markets. The empirical result also indicates that the U.S. and the Canada's stock markets is a positive relation. The average estimation value of correlation coefficient equals to 0.686, which implies...
A statistical model of the regulating market based on the regulating volume is proposed. The modelling process is divided into two steps; a long term and a short term study. The long term study is based on recorded data for 5 years. This analysis provides a statistical model of regulating prices and volumes for the whole market for the considered period. The combination of the long term model with...
The paper examines five International stock index spot and futures data to verify whether there exists long-term steady relationship between the index spot and futures prices. Based on the co-integration theory and error correction model (ECM), conclusions are drawn that index spot and futures are co-integrated in most cases and it is possible to do the corresponding short-term dynamic adjustment...
In this paper, time series models are estimated on daily average day-ahead prices quoted on the Italian power exchange (IPEX) between April 2004 and December 2008. We show that the EGARCH model outperforms ARMAX models in terms of forecasting power, and that IPEX prices are characterized by a leverage effect. We then add dummies to the ARMAX and EGARCH specifications in order to control for the impact...
Price stabilization mechanism adopted for agricultural product is important policy for reasonable food supply under free agricultural trade. A generally used quantity supply control scheme associated with free imports of Taiwan broiler industry is considered in this study. It is argued that efficient quantity control will provide floor price support while at the same time free imports will create...
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