Introduction: Price fluctuation is one of the most important features of the energy market that leads to price risk and economic instability. In the financial market, one of the best uses of derivative securities is in hedging. The most common way of hedging in the investment is through appropriate derivative instruments. They include options, swaps, futures and forward contracts. Even though there are many criteria used in the derivation of the OPTIMAL HEDGE RATIO, the MINIMUM-VARIANCE (MV) HEDGE RATIO considered by Johnson (1960) has been one of the most popular choices. The basic concept of the MINIMUM VARIANCE hedging risk lies in the combination of investments in the spot and future markets in order to reduce value fluctuations. Thus, the OPTIMAL number of futures contracts that a person must hold to HEDGE against the risk of price fluctuation in the underlying assets can be obtained by calculating the OPTIMAL RATIO of hedging risk. The literature shows that researchers mainly use future contracts to minimize the risk of price fluctuation in the spot market. Accordingly, in these studies, various econometric methods have been used to calculate the OPTIMAL hedging risk RATIO. Also, in order to introduce the best hedging risk model, the performances of different models have been compared. The evaluation of hedging performance is based on the percentage reduction in spot VARIANCE compared to portfolio VARIANCE. Then, the purpose of this study is to choose an OPTIMAL model with the highest degree of hedging risk for the selected commodity. Methodology: Several techniques have been proposed in the literature to estimate the HEDGE RATIO with index futures contracts. Many practitioners and academicians have sought to solve the problem of how to calculate the OPTIMAL HEDGE RATIO accurately. To achieve the goal, we compare the estimates of the HEDGE RATIO from the ordinary least squares methods (OLS), autoregressive model (VAR/VECM), autoregressive conditional heteroscedasticity (ARCH/ GARCH) and copula. Also, to determine the changes in the OPTIMAL hedging risk RATIO, we use the weekly time series of spot and future contract prices for crude oil and natural gas during the five-year period of 2013-2018. In the next step, the rolling window regression technique will be used to compare the performances of the studied models and select an efficient hedging risk model. The results of the weights for future by each of the four above-mentioned models will be used for hedging the spot prices of the two examined commodities. The obtained HEDGE RATIOs are applied on the real data in the following 20 weeks. Thus, the ability to reduce risk in every method is measured and compared during the specified period. Results and Discussion: All the models are able to offer a significate reduction in the portfolio. The conventional approach to estimating the MV HEDGE RATIO involves the regression of the changes in spot prices on the changes in future prices using the OLS technique. As we found, the MINIMUM VARIANCE HEDGE RATIO by the OLS method was 62% for crude oil and 37% for natural gas. However, for the OLS technique to be valid and efficient, the assumptions associated with the OLS regression must be satisfied. Thus, we use an autoregressive model (VAR/VECM). The OPTIMAL hedging risk RATIO obtained from the VECM model is 98% for crude oil and 86% for natural gas. However, the OLS and VAR methods only capture the influence of two risk factors on stock returns in the mean on average but are not sufficient to capture the dependence structure in higher moments or tail dependence. The volatility clustering phenomenon and the existence of ARCH effects demonstrate that HEDGE funds volatility varies over time. Then, we use the conditional autoregressive model (GARCH). Furthermore, we utilize the copula method to capture the general dependence structure between the futures and spot prices. The copula method has been used for multivariate statistical modelling owing to its edibility and convenience to describe its ability to capture the nonlinear relationship of random variables. The copula approach allows us to model the marginal distributions of individual random variables and their dependence structure separately. Our finding show copula serves normally to HEDGE crude oil and natural gas at the rate of 98% and 93% respectively. These rates are the models for crude oil and natural gas copula at 98% and 94% respectively. In this paper, the efficiency of different models of the rolling window regression technique are compared. This section is the core of the research. The results of the effectiveness of the OPTIMAL hedging rates of the crude oil and natural gas market show that copula functions in both markets have been in better conditions than the other models. Thus, the result of the research indicates the high efficiency of the copula functions approach to calculate hedging risk rates. Conclusion: The results show that modeling the relationship between the current and future prices in the form of copula functions is more efficient.

MINIMUM VARIANCE (MV) OPTIMAL HEDGE RATIO is a common approach to risk management but because of severe restrictive and unrealistic assumptions of MV, some alternative approaches have been investigated in literature. Mean Extended Gini (MEG) Coefficient approach which has some advantages over MV is one of these alternatives, but this approach shares some weaknesses as well. Mainly it does not consider the return of investment. M-MEG is another approach which is designed to resolve this weakness by considering both risk and return. This article compares MV, MEG and M-MEG OPTIMAL HEDGE RATIOs using daily spot and futures prices of gold coin in Iran Mercantile Exchange (IME) from 2008/11 to 2012/02. We find that MV OPTIMAL HEDGE RATIO is greater than MEG and M-MEG. Besides, M-MEG OPTIMAL HEDGE RATIO is smaller than MEG for investor with lower risk aversion.

The paper estimates and compares the MINIMUM VARIANCE OPTIMAL HEDGE RATIOs (OHR) for gold coin futures contracts traded in Iran Mercantile Exchange (IME), applying various econometric methods. Results of the paper indicate that considering different maturities for futures prices, brings considerable changes to the outcome, in a way that if the first maturity date is regarded as the price for the futures contract, HEDGE RATIOs shall exceed that of the second maturity date. The findings also reveal that OPTIMAL HEDGE RATIOs computed through alternative econometric methods outperform simple HEDGE strategy (OPTIMAL HEDGE RATIO equal one). The final conclusion is that, time-variant OPTIMAL HEDGE RATIOs derived from GARCH models are not necessarily better capable of reducing the risk, comparing time-invariant OPTIMAL HEDGE RATIOs.

BACKGROUND: The poultry meat and egg production, over the past years, has experienced upward trend in Iran. Production and distribution of products in the poultry industry needed to provide inputs. Corn is one of the most important inputs that can be pointed to it. Much of the corn required is provided by imports. Participation in the world futures markets or launch a futures market in Iran, as a tool of hedging, have significant role in reduce volatility of input and products prices as well as improving the welfare of consumers. The question that arises is that what amount of purchase is necessary as future purchase? This is the HEDGE RATIO.OBJECTIVES: The main objective of this study was to calculate the hedging rate.METHODS: that is calculated with two models: “MINIMUM VARIANCE” and “Mean- VARIANCE”. For this purpose, the monthly data of spot and futures prices of corn and exchange rates in the period March 2010 to February 2014 is used. The data are collected from the Islamic Republic of Iran Customs, the Central Bank of Iran and the Chicago Board.RESULTS: The results show that if 79% of the corn as future purchase, 57% of price risk decreases. With the entry of exchange rate to the models, HEDGE RATIO is greatly increased, and if hedging instruments does not increase, the efficiency decreased.CONCLUSIONS: The results suggest that importers and politicians consider the participation in world futures markets and the creation of a futures market in Iran as a hedging instrument.

This study has attempted to calculate the OPTIMAL HEDGE RATIO for investment in the stock market by investing in the gold market, with VAR-DCC-GARCH approach. To calculate this RATIO, we used the daily price of gold coins and the price index of Tehran stock market during the period of April 2, 2009 to March 18, 2017 in Iran. The results obtained from the OPTIMAL dynamics HEDGE RATIO showed that this RATIO has increased during the period from 2009 to 2013, and decreased during the period from 2013 to 2016, and a change in the regime has observed during the whole period. OPTIMALity, dictates that investors should invest in gold market and consider gold as an item together with stock assets in their portfolio in order to cover the risk of investing in the stock market.

According to the importance of the hedging of gold coin market fluctuations, the purpose of this study is to estimate the minimize VARIANCE of OPTIMAL HEDGE RATIOs for Bahar Azadi coin futures contracts from period of 2013/12/17 to 2017/06/01 using Markov Switching model and comparison of hedging performance computed by it with other commonly used model in this field. For this purpose Effectiveness of Markov Switching dynamic model of OPTIMAL HEDGE RATIOs and static OPTIMAL HEDGE RATIO of Ordinary Least Square model is compared, in two periods in the sample and out of the sample. The results indicate that in the sample period, the HEDGE RATIO of Markov Switching model had the best performance in terms of reducing VARIANCE and increasing utility rates. Also in out of the sample period indicated that the superiority of the Markov Switching model against simple HEDGE depends on the period and the perspective of the investor.

This research is an attempt to introduce a desirable pattern for Dynamic Modeling of the Estimated OPTIMAL RATIO of Gold Coin Risk Coverage. Given the unique nature of Iran in producing and supplying saffron and weakness in the commercial sector of this product, it was necessary to supply this product through the Iranian Stock Exchange. Following the presentation of the Saffron Deposit Certificate in the Iranian Commodity Exchange in 2017, the futures contracts for saffron in the Iranian Commodity Exchange were defined in 2018. On the other hand, the gold coin futures market was halted in 2018 due to the volatility of the gold coin market. Therefore, considering the importance of estimating the risk coverage in financial markets, in this study, we modeled the estimation of the OPTIMAL RATIO of daily gold coin risk with respect to the future contracts of saffron from May 22, 2018 to October 22, 2019, through the Coppola functions and wavelet analysis. And the combination of these two models is discussed. The results of the study show that the saffron futures market is able to estimate the risk coefficient of gold coin cash market risk and that investors can use this market to cover their risk. In addition, in terms of structural dependence on the basis of the Coppola functions and wavelet decomposition, it results in an OPTIMAL estimation of the coverage over the medium and long time periods.