جستجوی مقالات مرتبط با کلیدواژه « Copula Functions » در نشریات گروه « مالی »

The main goal of this research is to investigate the formation and spread of bubbles in the financial markets of the stock exchange, currency and gold markets using semi-experimental studies, considering that previous studies in this field mostly study the effect of volatility transmission or the effect of the return of one asset on the return of another market. Therefore, no study has been done in this field or it is limited; In this study, the data was collected in the period from 1389 to 1400 and was analyzed by descriptive and econometric statistical methods. The results of the analysis of the right-sequence unit root test show the existence of bubbles in all three markets under study. It shows the results of the analysis of vector auto-regression tests and copula (joint) functions. The structure of dependence between the three financial markets is quite dynamic and this dependence is greater when the market is in a developing situation than in a recession. Also, the sequential dependence between the gold coin and the exchange rate is much stronger than the dependence between the stock market and gold.

In this study, a hybrid copula-stochastic volatility model based on Monte-Carlo Markov chain is developed to evaluate the latent volatilities of the TSE Index. The data used to estimate the models include the values of the total index of the TSE from the beginning of 2020 to the beginning of 2021 on a daily basis with a frequency of 30 minutes. Also, in order to determine the error, data from the date (03/27/2021) to (12/21/2021) has been used in 15-minute intervals. the square logarithm distribution of returns as a measure of realized volatilities is first simulated using a stochastic volatility model to obtain latent volatilities and then using a mixture of copula family distributions and the MCMC, modeling and estimation were performed in the training phase and finally in the test phase using out-of-sample data to estimate the stochastic volatility of the test phase was investigated. The results show that among the functions of Copula Gumble, Galambos, Joe, Clayton and Frank in the test phase, 3 Copula Gumble, Galambos, Joe have acceptable performance and among these functions, the Gumble-Stochastic Volatility based on MCMC with the lowest error rate among the out-sample data recorded better performance.

In this study, latent volatility modeling and Bayesian analysis of stochastic Volatility of intraday data of Tehran Stock Exchange index based on Markov Monte Carlo chain in uncertainty conditions (downward crisis of stock market index) have been developed. The method of the current research is a correlational description. For this purpose, at first, the distribution of the logarithm of the squared return as a measure of the realized volatilities was simulated using the stochastic Volatility model to obtain the latent volatilities, and then by using the hybrid MCMC-Copula model, the parameters affecting the stochastic Volatilities were identified and estimated in the training phase. Finally, using the results obtained from the training phase, in the test phase, the comparison of Copula and GARCH models was done. The results showed that the Copula Gumble, Galambos, Joe, Clayton and Frank provide similar and lower MSE and RMSE indices than the GARCH base model, and therefore the model based on copula provides the possibility of serial dependence in the latent volatility process. The findings of the current research can be useful for financial and investment companies for portfolio management and portfolio management in different conditions of market volatilities in order to achieve the investor's goals and increase the value of the portfolio.

This study determines the optimal investment portfolio of insurance companies by considering underwriting activities. investment decisions in insurance companies are affected by underwriting activities. In this paper, the investment optimization problem of insurers is modeled using the copula-based conditional risk value, taking into account the results of insurance activities. Also, since the emphasis is on tails of distribution, the probability distribution of variables in tails is estimated using Pareto distribution and in other parts of the distribution using the Empirical probability distribution. Data collected on monthly basis covers two periods in-sample, between 2006 to 2015 and out-of-sample, between 2016 to 2019.The findings show that the optimum portfolio includes eighty percent of risky assets (stock and real estate) and only twenty percent of risk-free assets (bank deposits) and it is outside the legal constraints set by Central Insurance Therefore, legal constraints prevent insurance companies from the optimal selection of investment portfolio. Also, the comparison of out-of-sample performance with in-sample performance of portfolios shows that portfolios based on copula functions have better and more robust performance than traditional models.

Fluctuations in commodity prices in global markets have always influenced the behavior and decisions of investors in financial markets. In this research, using the Copula family models, financial contagion or volatility spillover on global price of petrochemical products and base metals on the on the stock price index of eight selected industries of Tehran Stock Exchange listed companies during a period of 10 years (2008-2018) has been reviewed. The research method is descriptive-analytical in nature and applied in terms of purpose. The research hypotheses were tested using an econometric approach based on Copula models and programming in MATLAB software. The results show that the effects of overflow of these variables on the index of selected industries are significant but different.Examination of different models of Copula method showed that T-Student model is most suitable for transmitting spillover effects, which indicates the symmetrical effects of price variables in global markets of petrochemical products and base metals on the index performance of selected industries. And then Clayton and Gumble models are in the next rank.

One of the main problems of shareholders in the stock market is the discovery, quantification and calculation of market risk. In many studies, one-way distributions are used to estimate risk metrics that usually do not give credible results to the investor. Because the distribution of assets is generally a broad sequence, and the results of computations are not acceptable for the consideration of the univariate normal distribution and the use of parametric methods. In this paper, using the Coppola theory, we calculate risk-weighted value (VaR) and conditional value-at-risk (CVaR). After estimating the multivariate T- Copula and the normal distribution of multivariate, the Monte Carlo method is used to generate a scenario for calculating the variance of the portfolio as well as risk estimation. Also, the calculations performed using the loss function method are tested and the accuracy of the approximations is verified. Finally, the minimum value of the copula based on the variance of the portfolio as well as its CVaR value is considered as the function of the portfolio planning, and the optimal portfolio is obtained by considering the weight of each share index. In the calculation of the 1200 index, we consider a sample basket of different industries, by calculating VaR and CVaR with confidence levels of 95 and 99 percent. The results obtained from the efficiency and reliability of the Monte Carlo simulation by the Copula T-Student versus the normalized multivariate distribution.

The present research examines the Financial Contagion or Volatility Spillover by financial assets such as exchange rates, gold and global variables on the stock market index. The correlation and Contagion between variables of global prices of gold, oil, and the dollar exchange rate on the index of 8 selected Tehran stock exchange industries over a period of 10 years (2008-2018) was examined. Method of the research is applied in terms of purpose and analytical-descriptive in terms of the nature. To test the research hypotheses using econometric approach based on Copula models, programming was performed in MATLAB software. The results of the show that the effects of volatility spillover of these variables on the index of selected industries are significant but different. The different models of the Copula method show that the Clayton and Gumbel models are most suitable for transmitting spillover effects in the upper and lower distribution of the range. The t-student model is in the next rank. In other words, the overflow effects of macro variables mostly affect one of the high (positive return) and low (negative return) domains, which indicates the existence of asymmetric effects on the return behavior of the selected industries of the stock exchange.