جستجوی مقالات مرتبط با کلیدواژه "ساختار وابستگی" در نشریات گروه "مالی"

Investigating the probablility of rare events occurring (events that occur with very low probability) is an important issue in portfolio risk management. extreme value theory of value provides the mathematical basis for modeling these events and calculating the risk criteria associated with them, such as the value at risk. The purpose of this paper is to model the dependency structure andextreme value theory of 10 foreign exchange companies of Tehran Stock Exchange (Persian Gulf Holding, Bandar Abbas Refinery, Mobarakeh Steel, Topico, Ghadir, Parsian Oil and Gas, Melli Mes, Gol Gohar, Mobile Communications, Chadormelo).The results indicated the fact that among the stock returns of the top 10 companies evaluated, it is possible that using extreme value theory of value using vine Copula functions, the results of the forecast were greatly increased. Results of the copula function in six modes: simple Copula (t), time-varying Copula (tDCC), and Gaussian distribution-based time-varying Copula (GDCC). tvSJC) was investigated. In all six cases, the use of the Copula -wine method increased the accuracy in predicting optimal stock returns.

This paper examines dividend smoothing based on dependent structure of investment in listed companies on Tehran Stock Exchange (TSE). Dividend smoothing calculated using partial adjustment model of Lintner. This research is an applied research method To do so, we used a sample of 107 firms, from 1386 to 1396, to test five research hypotheses. To test hypotheses were used statistical analysis with panel data method Using software tools, Eviews and linear regression model. In this research, descriptive statistics including tables and charts, central tendencies, dispersion, distributions for describing the sample, and combined regression method were used to analyze the data related to the research aspects. The Chow test was used to determine if the combined data is more efficient to estimate the desired function, and Fisher's statistic was used to examine the significance of the regression model. The findings represent that dividend smoothing decisions in firms is influenced by factors such as dependent structure, concentration of ownership, compensation of management , debt ratio and size.

Studying, and analyzing the dependency structure between the markets at the economic boom and bust have been suggested by the researchers and theorists of different areas. Furthermore, there have been various models to explain the correlation between the financial markets. Among them, the Copula model has a high ability to recognize the asymmetric dependence structure. The present research is going to study the dependency structure in the financial markets of four countries; Iran, the United Arab Emirates, Turkey and China at the boom and bust cycling in the period of 2014-2017, applying conditional heterogeneity variance model (GARCH), the Markov switching approach, and the Copula functions. The results illustrate that there is an asymmetric structure in every regime, as at the recession time, the correlation between these markets and Iranian market would be higher than the boom time.

In this research, the GARCH-EVT-COPULA method is investigated to determine the dependency structure and portfolio risk estimation on the foreign exchange market data in Iran. GARCH-EVT models are used to mariginal distribution of each of four currency returns series. For the joint model, we choose five copuls with different dependence structure such as Frank, Clayton, Gumble, Normal and t-Student copulas. In this research portfolio risk is measured using VaR and CVaR. The statistical sample of this study is the daily exchange rate of USD,EURO, Pound and AED for the free market with 5 working days from September to the end of 1396. Based on the results of the research, using the Akaike information criterion values, the t-student function is the best fitted copula model for investigating the dependency structure. Exchange rates have the same upper and lower tail dependencies. Accordingly, in the markets for boom (severe positive) and stagnation (severe negative), the dependence between the two exchange rates is the same.

In this paper, we apply R-Vine copula -ARMA-APGARCH approach to investigate the dynamic relationship between banking, insurance and pension, investment and other financials sub-indexes in Tehran stock exchange. Using a sample of more than 8 years of daily return observations of the financial sub-indexes, we find evidence of significant and symmetric relationship between these variables. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches. This paper results show that vine copula VaR is accurate at 1% and 5% significance levels. This paper’s findings suggest the flexibility and capacity of vine copula structures in financial dependency modeling and risk management

Studying the countries' stock market and global market interaction has been one of the most important research subjects in the global market. Thus, studying the relationships may have a significant role for the decision making of the investors. An appropriate estimation of the dependence structure has been the significant starting point at an investing period, for the investment risk control. The present research aims to study the interaction between dependence structure at Tehran stock market efficiency and the global price of gold and oil, at the period of 2010-2017, on a daily basis. In doing so, GARCH-Copula approach has been applied. The results show the asymmetric mutual relationship between the studied efficiencies. As it can be seen in the present paper, the t-student Copula functions can have a better recognition than other functions for both efficiencies; 'Tehran stock and gold market', and 'Tehran stock and oil market'. The results indicate that the Tehran stock market has been highly dependent to both oil and gold markets, and their threshold changes may lead to a stronger dependency of the markets together.