The movements in OIL PRICEs are complex and, therefore, seem to be unpredictable. The traditional linear structural models have not been promising when applied to forecasting, particularly in the case of complex series such as OIL PRICEs. Although linear and nonlinear time series models have done much better job in forecasting OIL PRICEs, there is yet room for an improvement. If the data generating process is nonlinear, applying linear models could result in misleading forecasts. Model specification in nonlinear modeling can also be very case dependent and time-consuming. In this paper, we model and forecast daily FUTURES OIL PRICE, listed in NYMEX, applying ARIMA, and GARCH models, for the period April June 1983 - Jan. 2003. Then, we test for chaos using BDS, Lyapunov exponent, Neural Networks, and Embedding Dimension methods. Finally, we will set up a nonlinear and flexible ANN model to forecast the series. Since the tests for chaos indicate that the OIL PRICE in FUTURES markets is chaotic, the ANN model should make better forecasts. The forecasts comparison among the models approves that.

Due to PRICE volatility in the OIL market, market players are exposed to large risks. Value at Risk (VaR) is one of the main methods to measure market risk in various asset markets including commodities .,. In this study, Upside and Downside Risks are estimated by using the GED-GARCH method that is appropriate for leptokurtic distributions with fat tail. The daily spot and FUTURES OIL PRICEs data from January 1986 to December 2010 data for "in sample" and from January 2011 to July 2012 for "out of sample" are our data sample. To test the reliability of estimated VaR, the Kupiec test is used. Also by using Granger Causality analysis, the spillover effect risk between spot and FUTURES OIL PRICE returns are investigated. Results show that spot and FUTURES returns have leptokurtic distribution with fat tails. There is also a significant upside spillover effect risk from FUTURES to spot PRICE returns at 99% confidence level as for OIL PRICE increases during 2000s.

This paper surveys OIL FUTURES markets and identifies relevant risks in order to propose a model that establishes an acceptable relationship between future and spot PRICEs. Based on this mechanism the paper then proposes a framework for predicting OIL PRICEs and resultant revenues from OIL sales and purchases. The framework is then used to predict PRICEs expected revenues from OIL transactions using data mining and modeling based on an adaptive expectations model and technical analysis rules. Results of three different scenarios are then compared: 1-when inputs are the outputs of adaptive expectations model 2- when inputs are derived from technical analysis rules and 3- combination of these two approaches. The results demonstrate that the combined model besides reducing uncertainty increases the predictive power of the model by respectively 70% and 10% compared to scenarios 1 and 2 respectively. In addition, applying the adaptive expectations together with technical analysis results in more precise predictions.

Agricultural activities are risky activities. In these activities, various natural, social and economic risks have created fragile and vulnerable situation for producers. PRICE risk in agricultural products has caused financial problems for many producers and farmers. To deal with these PRICE risks and PRICE fluctuations, there are varieties of tools. This paper focused on FUTURES markets instruments as risk management tools for date's PRICE risks management. This study used monthly FUTURES PRICEs of dates and used mean-variance framework to determine the optimal hedge ratio with OLS method. Due to the conditional variances autocorrelated in residuals in regression models, time varying hedge ratios with Bivariate GARCH models were determined. Bivariate GARCH model was used in this study to determine hedge ratios. Therefore, first, time varying conditional variance covariance matrix with using multivariate models based on heterogeneous variance BEKK (1, 1) was estimated. Then, using the results of this matrix, the optimal time varing hedge ratios was calculated. Dates future PRICE series were predicted using artificial neural networks pattern and the GARCH model. The results of hedge performance showed that time varying hedge ratios were eliminated more PRICE risk than the OLS method. The results showed that the average hedge ratios of Bivariate GARCH model is 0.7, meaning that about 70% of the date's PRICE risk could be reduced with sales in the FUTURES market.

The aim of this study is to investigate the relationship between spot and FUTURES PRICE of gold coins in Iran and how market information spread between these markets, and analysis between changes in cash and FUTURES PRICE volatility is the daily gold coins. Daily data for the future PRICE of gold coins collected in 2012 from Iran Mercantile Exchange, has been used. To examine the relationship between spot PRICE and FUTURES PRICE of gold coins multiple regression models, vector Autoregressive, GARCH and Granger causality test is used. The results showed that changes in FUTURES and spot coin PRICE does not have a significant relation in the VAR model and multiple regression, But the spot and FUTURES PRICE volatility have an effective relationship. Granger causality tests also showed there is a relation from the spot PRICE to the FUTURES PRICE changes. But the spot PRICE and FUTURES PRICE volatility results indicate that this is Duplex relationship between markets, there is information flow between the two markets as a perfect.

Iran is in a special place in terms of OIL exports, and it is natural that in this valley traders are also looking for a better market and more profits. With development and progress of the Internet and new technologies, the world economy has taken an upward trend towards e-commerce. The questions that will be answered are the questions that can be answered between the Iranian Stock Exchange and the International Forex Market, which one can be suitable for OIL traders and what kind of contracts they sign to make more profits. Therefore, this research aimed to explain the nature and fundamentals of stock and forex markets and future contracts and their differences, to have a comparative view on their advantages and disadvantages towards each other and to investigate the conclusion of FUTURES contracts in these two markets. The findings indicate that the international forex market in Iran is legal and legitimate and has many advantages for OIL traders compared to the stock market and it is proved that trading in this market is not gambling and usury. Also, if you use contracts for difference or CFDs in their transactions, they will receive more points. Special formalities are needed to conclude future contracts on the stock market and Forex markets.

In recent years, the theories of nonlinear systems in general and chaotic systems in particular have received a great deal of attention in the economic research.Based on the assumption that at least part of the underling process is nonlinear, chaos analysis evaluates whether that process in deterministic.In this research, first by applying Correlation Dimension (CD) and Largest Lyapunov Exponent (LLE), the evidence of chaos in OIL future PRICEs (1996-99) appeared to be true. Then the logestic map is explained and finally the Largest Lyapunov Exponent (LLE) estimate of the first sectiona is compared with the calculated LLE under the logistic function.

Derivatives are alternative financial instruments which extend traders opportunities to achieve some financial goals. They are risk management instruments that are related to a data in the future, and also they react to uncertain PRICEs. Study on pricing FUTURES can provide useful tools to understand the stochastic behavior of PRICEs to manage the risk of PRICE volatility. Thus, this study evaluates commodity FUTURES contracts by considering Ross (1995) one-factor future pricing model as a function of spot PRICE, Gibson and Schwartz (1990) two-factor FUTURES pricing model as a function of spot PRICE and convenience yield and finally Schwartz (1997) three-factor FUTURES pricing model as a function of spot PRICE, convenience yield and instantaneous interest rate by adding jump to stochastic behavior of commodity spot PRICE. For this purpose, it is assumed that spot PRICE follows Jump-diffusion stochastic process with exponential probability distribution of jump domain. Finally, commodity pricing future relations in three basic models are presented as a function of above factor (s) and jump parameters by using Duffy-Pan-Singleton approach.