long-term memory

Open Access Article

1 - Evaluating and study of the Fractal Properties of Capital Markets Based on DE trended Fluctuation Analysis (Case Study: Exchange Market and Stock Index of Tehran)
Arash Azaryoun narges yazdanian seyedalireza mirarab baygi hoda hemmati

In this study, the long-term memory of the stock market index and exchange rate (dollar) was estimated using detrended fluctuation analysis. In order to detrend the data, the GARCH approach was proposed and the long-term memory estimation model was implemented separatel More

In this study, the long-term memory of the stock market index and exchange rate (dollar) was estimated using detrended fluctuation analysis. In order to detrend the data, the GARCH approach was proposed and the long-term memory estimation model was implemented separately for both conventional and GARCH methods. For this purpose, daily data of stock market index and dollar exchange rate in the market during the years 2014 to 2020 were used. The results showed that the conventional method in calculating the detrended fluctuations is not able to estimate the long-term memory of the exchange rate, while the results for the stock index showed the existence of short-term memory. The results showed that the proposed method in detrending data and calculating detrended fluctuations based on Garch model has a higher power in controlling changes in market fluctuations and according to the findings of this method, stock index and dollar exchange rate have long-term memory. The results showed that these two methods provide significantly different estimates of long-term memory of the market and according to the results of the correlation test between the values of long-term memory of data and the value of parameter q in detrended fluctuation analysis; it was observed that the stock market index and exchange rate in Iran have multifractal properties. Manuscript profile

Open Access Article

2 - Modeling of long-term memory and regime changes in Tehran Stock Exchange stock returns and asymmetric effects of oil market shocks on it
Mojtaba Almasi Ali Falahati Shahram Fattahi Alireza Rostami

In this research, by presenting a completely new model at the national and international levels, a practical framework for accurately determining the shocks of foreign markets on stock returns has been provided; so that, using monthly data from 1998 to 2017 and the Mark More

In this research, by presenting a completely new model at the national and international levels, a practical framework for accurately determining the shocks of foreign markets on stock returns has been provided; so that, using monthly data from 1998 to 2017 and the Markov Switching Fractionally Integrated Threshold GARCH (MS-FITGARCH) model attempts to investigate the oil price shocks on stock market returns and the comprehensive modeling of Heteroscedasticity characteristics, leverage effect, Volatility clustering, and long-term memory in the framework of various recession and expansion regimes of the stock market returns. In addition, the Dynamic Conditional Correlation-Fractionally Integrated Threshold GARCH (DCC-FITGARCH) model has been used to investigate the relationship between oil market and stock market fluctuations. The results of this study indicate the significance of the model's coefficients and the necessity of using the model introduced in the research to model the fluctuations of Tehran Stock Exchange. Based on the results, the regime one capture the recession conditions and the regime two capture the expansion conditions of Tehran Stock Exchange. The results of the MS-FITGARCH model indicate a significant positive effect of oil price shocks on the stock return average in the expansion regimes, so that the effects in the recession regime are not significant. Also, the results of the DCC-FITGARCH model are in line with the first model and represent a more positive conditional correlation between the fluctuations of the stock market and the oil market during the expansion economic periods. Manuscript profile

Open Access Article

3 - A comparative study between the effectiveness of ARIMA and ARFIMA models in predicting the interest rate and the treasury exchange rate in Iran
mohadeseh razaghi hashem nikomaram Alireza Heidarzadeh Hanzaei farhad ghaffari Mahdi Madanchi Zaj

Due to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Ir More

Due to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Iran interbank offered rate and Iran treasury exchange rate as interest rates indicators for facilitating interest rate risk management. Two econometric models including ARFIMA and ARIMA have been used for forecasting. Thus, the ARFIMA model considering long-term memory and the ARIMA model without considering long-term memory have been considered. The evaluation of the prediction accuracy of the two models using the monthly Iran interbank offered rates data and also the monthly Iran treasury exchange rates data shows that both the interbank offered rates data and the Islamic treasury bond rates data, ARIMA model has a better performance compared to ARFIMA model in predicting data. Manuscript profile

Open Access Article

4 - Modeling volatility and conditional VaR measure using GARCH models and theoretical EVT in Tehran Stock Exchange
Saeed Fallahpoor Reza Raee Saeed Mirzamohammadi seyed mohammad hasheminejad

Trying to identify an appropriate model to enhance measurement accuracy by using value at risk measures is of particular importance. Conditional Value at Risk (CVaR) with having some of the shortcomings of VaR, is a more reliable measure. In this study, the characterist More

Trying to identify an appropriate model to enhance measurement accuracy by using value at risk measures is of particular importance. Conditional Value at Risk (CVaR) with having some of the shortcomings of VaR, is a more reliable measure. In this study, the characteristics of the Tehran Stock Exchange index data usage FIGARCH-EVT model to calculate value at risk if states have been more accurate. GARCH-EVT hybrid implementation model and its development, FIGARCH-EVT model, we found that the effect of clustering, dynamic and long-term memory has been included in the modeling. FIGARCH model for log data output index, which will be modeled in terms of the above properties. In addition, the wide trail property index return data using extreme value theory (EVT) is used for residual FIGARCH model. To compare the results, NORMAL-GARCH models and t-Student-GARCH, historical simulation and GARCH-EVT indicator is used for data output. The results of the model using retrospective tests were evaluated. The results of this study indicate that the data distribution is skewed and asymmetrical index returns do not follow a normal distribution. The tests Standardized Exceedance Residuals and The Cumulative Violation Process and  Expected shortfall backtesting and loss function Lopez FIGARCH-EVT model over other models is more accurate. Manuscript profile

Open Access Article

5 - The pervasive risk of the financial crisis in the Iranian banking system with the ARFIMA-FIGARCH-Delta CoVaR approach and the expected marginal Shortfall
leila barati mirfeiz falahshams farhad ghafari Alireza Heidarzadehhanzaee

Systemic risk refers to the risk of failure of the financial system or failure of the entire market. This risk can arise from instability or crisis in financial institutions and can be transmitted to the entire financial system as a result of transmission. The purpose o More

Systemic risk refers to the risk of failure of the financial system or failure of the entire market. This risk can arise from instability or crisis in financial institutions and can be transmitted to the entire financial system as a result of transmission. The purpose of this paper was to assess the pervasive risk of a financial crisis in the Iranian banking system. In this study, statistical information of banks during the years 1392-1397 has been used. In the first part, the comprehensive risk indicators of the financial crisis are calculated using the Delta CoVaR index, then the risk susceptibility is assessed using the ARFIMA-FIGARCH method. In the first step, the unit root test indicates the existence of a deficit root in the bank stock price index. Comprehensive risk indicators are then calculated and systemic risk transmission modeling is discussed. The results of the model indicated that the systemic risk situation in the country's banking system was abnormal, which was due to the leverage situation of the country's banks. Using the results of this study, it can also be stated that different financial sectors are required to consider sufficient capital for systemic Manuscript profile

Open Access Article

6 - A Survey of Long-Term Memory in the Digital Currency Index
Shima Alizadeh hossein safarzadeh

Long-term memory, also referred to as long-range dependence, explains the correlation structure of values of a time series at long intervals. According to the efficient market hypothesis, prices follow a randomized step process, so returns on assets can not be predicted More

Long-term memory, also referred to as long-range dependence, explains the correlation structure of values of a time series at long intervals. According to the efficient market hypothesis, prices follow a randomized step process, so returns on assets can not be predicted based on past price changes. Long-term memory is a weak point of the business-market hypothesis. Long-term processes have important implications for asset yields and play a crucial role in time series analysis. This study examines the existence of long-term memory in the price index of crypto currencies equals $ 1 and lower for the period from September 1, 2015 to September 1, 2018. To estimate the parameter d, the OLS method is used in the EVIEWS software package. The ARFIMA model is used to test hypotheses. The results indicate that long-term memory is in the currencies of DIGIBYTE, Dodge Coin, EMER Coin, BITSHARES, MAIDSAFE COIN, XEM, Redd Coin, NXT, Verge and Ripple, and on the other hand, three currencies of Byte Coin, SIA Coin, STELLAR lacks long-term memory, and therefore these currencies are among the most efficient market products. Manuscript profile

List of Articles long-term memory

Sanad

Links

Related Centers

Technical Support

Official pages