کاپولا

This paper examines the dependence structure between two currencies (Yuan and Euro) before and after Yuan joins the SDR basket. To this end, we propose SJC copula Time-varying approach for the daily closing price of these currencies for the period 2005-2020. According to the evidence, the behavior of Chinese monetary authorities in response to Euro - Dollar has changed. In the first period, exchange rates are more correlated when they are depreciating against dollar compared to when they are appreciating. This means that the Chinese monetary authorities' priority has been to maintain a competitive advantage in world trade and thus to intervene to depreciate yuan as euro depreciates against dollar. In the second period, with the internationalization of yuan, China's strategy changed and reacted more drastically to the appreciation of Euro against dollar; PBOC intervenes to appreciate Yuan, stabilizing its currency price. In this case, by supporting investors, it encourages them to maintain and even increase the Yuan share in their portfolio.

This research provides a copula-based stochastic programming that is able to determine the optimal amounts of primary energy resources and different technologies to supply the required electrical energy. In this model, the uncertainty caused by random variables is presented in different scenarios, and uncertain interactions between random variables are shown using the copula functions with the different probability distributions and previously unknown correlations. Then, based on the developed approach of Copula-based stochastic programming, urban energy system planning for Tehran and Isfahan is formulated. The results obtained from the solution of the model indicate that the current trend is not consistent with the use of technologies with the optimization results, and it shows that in each case, solar technology compared with combined cycle technology, gas Turbine and steam in providing a part of the demand for electricity in terms of Economic and environmental benefits, and should be prioritized in investment policies. In order to compensate for the supply shortage, the remaining electrical energy should also be supplied by the grid, which will reduce the amount of pollution, compared with the situation at the same cost. In addition, the results show that the uncertainty in the components of the system has significant effects on the output of decision variables and system cost. JEL Classification: C02, L11, Q40, R00

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.

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.

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.

The results show that modeling the relationship between the current and future prices in the form of copula functions is more efficient.

Futures are used as the most important risk hedge tools to reduce the risk of the crude oil market. The optimal hedging risk strategy is determined by calculating the optimal hedging risk ratio. It is important to determine the relationship between the time series of spot prices and futures in calculating the optimal hedging risk ratio. Therefore, in this paper, the OLS, ECM, DCC GARCH and GARCH models based on Copula are used to calculate and evaluate the optimal hedge ratio of spot market hedging risk to the futures market over the period 2018-2013. The results show that the DCC-GARCH model has the highest optimal hedging risk ratio at 0.805. Considering the percentage of variance reduction, it can be concluded that the dynamic strategies of DCC and copula to models Static hedging risk is more efficient. Also, the time-varying, t-student, gamble, and normal capsules show better performance than the DCC model. Also, among the functions mentioned above, the copula t student function has the best performance.

The paper examines the issue of hedging in energy markets. The objective of this study is to select an optimal model that will provide the highest price risk reduction for the selected commodities. We apply the ordinary least squares methods, autoregressive model, autoregressive conditional heteroscedasticity and copula to calculate the appropriate dynamic minimum-variance hedge ratio. The objects of hedging are the spot and futures prices. We use weekly data for the period 2013 to 2018 to estimate our values. Empirical results show that the Copula model is the most effective method for hedging against price risks‏.

At the core of any investment lies the return on investment. To gain a favorable return, an investor should take investment-related risks. The interaction between risk and return can lead to decisions on asset allocation. A key strategy in investment discussions is diversification in investment portfolio. Investment strategy is undetermined in different assets such as security, gold, currency and cryptocurrency. Despite the temporary recession and success of certain assets, it is hard to prioritize investment among assets (in terms of risk and return) to ensure that the investor makes the highest profit at the lowest risk. Thus, the present research used the Mean-CVaR model along with the Extreme value theory (EVT) based on Copula’s theory to estimate the correlations among time series. It used the dynamic conditional correlation (DCC) estimation method to measure the joint distribution of assets regardless of the normality assumption of data, collinearity, priorities and weights of investment in assets as optimal values.

Theoretical Framework:

Diversification of conventional investment portfolio which only involves cash and security with alternative assets such as goods, currency and estate helps to decrease the correlation of assets and increase its resistance to severe changes in stock market. It, thus, helps to improve the performance of investment portfolio (Fischer & Lind-Braucher, 2010). Moreover, due to the low correlation between conventional assets and cryptocurrency and its high-efficiency, cryptocurrency is a good instrument to be combined with diverse investment portfolios and can increase the Sharpe ratio (Chuen et al., 2017).One assumption of the distribution of financial return time series is the normality of data. However, in actuality, many financial return time series are not normal. When the normality assumption is violated, Value at Risk (VaR) is a proper measure.Risk exposure value is not an integrated risk measurement and due to the lack of sub-aggregation property, may be inefficient in optimizing investment portfolio. Thus, researches introduced Conditional Value at Risk (CVaR) as an integrated measure and an alternative for VaR.

As the return on financial assets is marked by a fat tail and is not marked by a normal distribution of data, to better predict the distribution of series, EVT was used. Moreover, Copula’s theory was adopted and the structure of correlations among series as time-varying was modelled via the dynamic conditional correlation estimation and the joint distribution of assets was analyzed regardless of data normality and collinearity. Then, the Mean-CVaR model was used to estimate the risk exposure value and set the investment priorities and weights among assets in Tehran Stock Exchange, Gold Coin, USD and Bitcoin as optimized values.In this research, for an optimal allocation of investment among four above assets in daily return, Tehran Stock Exchange index (TEPIX) was used. The daily return on investment in Gold Coin (the old version) was estimated in Rials. That of USD was estimated in Rials in Tehran free market. Finally, the daily return on investment in Bitcoin was estimated in Rials between October 2014 and April 2018.

A negative correlation was estimated via DCC-Copula between certain assets, which shows that when a particular asset gains higher return on investment (than the mean value), the other asset does not follow the same trend. Thus, investors are capable of diversifying their portfolio accordingly.Prediction of return on assets showed that the highest expected daily return on investment belonged, respectively, to Bitcoin, Gold Coin, Dollar and Tehran Stock Exchange. Moreover, as the optimized weights showed for zero CVaR, due to the low variance, the greatest investment weight was investment in Tehran Stock Exchange. The higher the investor’s risk-taking, the greater the investment weight of Gold Coin and Bitcoin.

Conclusions & Suggestions:

Considering the investor’s risk-taking level, if s/he tolerates the least risks, s/he is suggested to make the most investment in securities. An increase in the minimum expected return on investment is followed by an increase in the investment share of Gold Coin and Bitcoin. Thus highly risk-taking investors are suggested to invest in Bitcoin and low risk-taking counterparts are suggested to invest in Gold Con. Considering the conditional Sharp (C-Sharp) ratio optimal portfolio indicated a better performance of various portfolios than any other asset, and the best performance of the portfolio includes Gold Coin with more than 70% and Dollars and Bitcoins with an equal weight. Furthermore, according to the C-Sharp ratio in the optimal portfolio, the minimum weight of Gold Coin is 60% and the maximum share of Dollar and Bitcoin is 20%.

For investors and policymakers, it is essential to understand the dependencies between the stock market and the foreign exchange market. In this study Copula approach is applied to estimate correlation between the exchange rate, price jump, and the Tehran stock exchange price index (TEPIX) from 2006 to 2015. Moreover, nonparametric approach is used to calculate the mean, diffusion, and price jump of the TEPIX. The empirical results show that the mean of annual return, diffusion, and price jump of the TEPIX are 19 percent, 0.012, and 26, respectively. In addition, the results of Granger causality test show a unidirectional relationship between the exchange rate and TEPIX, running from the exchange rate to the TEPIX. According to Copula approach, the Correlation coefficient between the TEPIX and the exchange rate is equal to 0.85.

The purpose of this study is to calculate Value at Risk (VaR) of a selection of bank's currency portfolio, using GARCH-EVT-Copula (GEC) approach. Today's main challenge of a banking system is to calculate and quantify the risks that the system is encountered. There are numerous approaches to calculate the risks. usually these approaches assume a common known distribution for the assets portfolio and generally a normal distribution is utilized for the experimental models. Nevertheless, the distributions of the assets are fat-tailed distribution and consequently normal distribution assumption may lead to inaccurate estimation. This article does not assume a specific asset distribution.This study applies autoregressive threshold variances (GJR-GARCH) for intertemporal individual's asset variable returns distribution. It also utilizes extreme value theory or the fat-tailed distributions and Coppola functions for all asset returns in an asset portfolio. In this study VaR is estimated using variance-covariance and historical simulation methods. Finally, in order to test the reliability of the applied models Kopic method is used. The sample data of the bank's currency portfolio consists of the market daily figures of the US Dollar, Japan's Yen, Turkish Lire, Emirate Dirham, Korean Won, and Euro exchange rates from 21March 2007 till 19 April 2012. Results show that the estimated VaR using GEC model is higher than those of estimated using the other two methods. They also show that reliability and precision of Kopic test is higher than those of variance-covariance and historical simulation models.

The traditional approaches for estimating VAR assume that the joint distribution is well-known and the most commonly used normality of the joint distribution of the assets return. In reality, the financial asset return distribution has fatter tails than normal distributions. On the other hand, the use of linear correlation to model the dependence structure shows many disadvantages. Therefore, the problem raised from normality could lead to an inadequate VaR estimate. In order to overcome these problems, this paper resorts to the copula theory which allows the joint distribution of the portfolio to be free from any normality and linear correlation. Combining copula and the forecast function of the GARCH model, this paper proposes a new method, called conditional copula-GARCH, to compute the VaR of portfolios. Examined data in this study includes daily price of selected portfolio, composed of 17 equities for 1082 days in Tehran stock exchange. Presented model compared with traditional methods (including the historical simulation method & variance_covariance method). the results show that conditional copula-GARCH model captures the VaR more successfully at 95% confidence¬.