List of Articles Long memory

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  1 - Long memory in four main cryptocurrencies
  gholamreza zomorodian Babak Mahboubi
  10.30495/jfksa.2022.20209
  In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Some of them are widespread and global. This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Three diff More

  In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Some of them are widespread and global. This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Three different long-memory methods (R/S analysis, fractional integration and fractional GARCH extensions) are used to analyze it in the case of the four main cryptocurrencies (BitCoin, Ethereum, LiteCoin and Ripple) over the sample period January 2013– November 2019. Our results are twofold. First, R/S method is prone to detect long memory whereas the findings of ARFIMA and GARCH type models indicate that in the case of two examined cryptocurrencies (BitCoin and Ethereum), long memory exist (there is a positive correlation between its past and future values). Such predictability represents evidence of market inefficiency in their markets: trend trading strategies can be used to generate abnormal profits in these markets. Although our findings show that returns of Litecoin and Ripple don’t have a significant long memory. Manuscript profile
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  2 - Forecasting value-at-risk and expected shortfall using high frequency data modeling
  S. Babak Ebrahimi Negin Mohebbi
  The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk and expected shortfall across 3 industry indices in Tehran Stock Exchange such as chemical, v More

  The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk and expected shortfall across 3 industry indices in Tehran Stock Exchange such as chemical, vehicle and metals. The dataset is composed of daily data covering the period from May, 2011 to May, 2015. According to the result of this research accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-day-ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered in 1-day, 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Manuscript profile
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  3 - Long memory investigation and application of wavelet decomposition to improve the performance of stock market volatility forecasting
  شمس اله شیرین بخش اسماعیل نادری نادیا گندلی علیخانی
  Because of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to ex More

  Because of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to examine the existence of the long memory in both mean and varianceequations in the return series of Tehran stock exchange, Pays to forecasting the volatilityof this index. For this purpose, the daily data from fifth Farvardin 1388 to eighteenthOrdibehesht 1391 is used. Our results confirm the existence of Long Memory in bothmean and variance equations. However, among others, based on the information criteriaand MSE, ARFIMA (1,2)-FIGARCH(BBM) model has been selected as the bestspecification to model and forecast the volatility of Tehran stock exchange’s return. Aswell, in order to Forecasting the volatility of this series, was used Combination of theabove model with Level and decomposed data. The results show that, according to theforecasting error criteria (MSE and RMSE), the result of model’s based on decomposeddata (with wavelet technique), more acceptable. Manuscript profile
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  4 - New approach for estimation of long memory parameters in financial time series
  سید محمد سیدحسینی مسعود باباخانی سید محمد هاشمی نژاد سید بابک ابراهیمی
  When past observations have a high correlation with future and it cannot be ignored,studied time series has long memory. Examining of existing of long memory in timeseries has a lot of application in finance and lots of ways have been created to examine itbut they have More

  When past observations have a high correlation with future and it cannot be ignored,studied time series has long memory. Examining of existing of long memory in timeseries has a lot of application in finance and lots of ways have been created to examine itbut they have lots of mistakes. Bootstrap Approach has been used in this paper for give usa good proxy of sampling distribution in order to estimate of memory parameters. Thisapproach has less limitation than others and can deal with most of difficult problem. Inthis research we use the data of price index of Tehran Stock Exchange for duration ofDecember of 2006 till June of 2010 for estimating parameter of long memory, finally theresults show the estimation of parameter of long memory has improved. Manuscript profile
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  5 - Constant Conditional Correlation Volatility Transmission Model with Long Memory Effect, evidence from Tehran and Dubai Stock Market
  Seyed Mohammad Seyedhosseini Seyed Babak Ebrahimi Masoud Babakhani
  The expansion of Globalization not only affects developed countries’ financial markets, but also the markets in developing countries. This condition causes investors who diversify their asset portfolio in foreign markets, pay serious attention to links between sto More

  The expansion of Globalization not only affects developed countries’ financial markets, but also the markets in developing countries. This condition causes investors who diversify their asset portfolio in foreign markets, pay serious attention to links between stock markets. This fact implies that there is an equilibrium relation between financial markets.Global oil price fluctuation is one of the factors that affect the capital markets in countries where the economy is based on oil revenues.  Most of these markets have long-run memory characteristic which should be considered in modeling and estimation. In this research the Constant Conditional Correlation (CCC) model is expanded in the way to imply long-term memory effect in the estimation. The data which is used is daily return of stock price and oil price in the period December 2006 to January 2010. The results indicate volatility contagion from global oil market to Dubai stock and Tehran stock market and also there is contagion effect between Dubai and Tehran stock market Manuscript profile
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  6 - Effect of long memory dependence structure between the dollar exchange rate and oil products in the Tehran stock Exchange Index: A copula based approach
  Mahdi Salehi Samaneh Zamani Moghadam Sadegh Nekooei
  During the last decade crucial part of the analyzing the time series has devoted to the long memory. Existence of long memory in output of possession has significant application for investing in efficiency of market, Pricing the differential paper, and selecting the pos More

  During the last decade crucial part of the analyzing the time series has devoted to the long memory. Existence of long memory in output of possession has significant application for investing in efficiency of market, Pricing the differential paper, and selecting the possessions basket. In our research the effect of long memory dependence structure between the dollar exchange rate and oil products in the Tehran stock exchange index. First the existence of long memory ARFIMA test review and continue to understand the impact of long memory on the dependence structure of two types, raw data and filtered data (Dollar exchange rate variability data and index Petroleum for the period from 2009-2013) have been used. The result showed that the raw data has a long memory, than the tail dependent data are filtered. Manuscript profile
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  7 - Modelling of capital market returns fluctuations for Tehran Price Index Return: MRS-FI-TGARCH and FI-TGARCH models
  Hajar Moradian Ali Haghighat Hashem Zare Mehrzad Ebrahimi
  The aim of this paper is to expand flexibility of modeling in capital market fluctuations. We achieve the goal by introducing MRS-FITGARCH model for the first time in the world. We use weekly TEPIX changes from 2009 to 2017. The parameters could change through the regim More

  The aim of this paper is to expand flexibility of modeling in capital market fluctuations. We achieve the goal by introducing MRS-FITGARCH model for the first time in the world. We use weekly TEPIX changes from 2009 to 2017. The parameters could change through the regimes. Results show that there are two regimes; regime one with high return mean and high return variance and regime two with low return mean and low return variance. Adding asymmetric effects and long memory potential prediction, are the novation of our new model. Valued Negative asymmetric effects coefficient results that bad news effects on the fluctuations were less than good news. It was not to be valued in regime tow and it means, good news and bad news has the symmetric effects in this regime. In regime one, there is unlimited long memory coefficient but in regime two fluctuations effects decreases in hyperbolic rate.   Manuscript profile
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  8 - Does Exchange Rate Non-Linear Movements Matter for Analyzing Investment Risk? Evidence from Investing in Iran’s Petrochemical Industry
  Alireza Khosrowzadeh Aboutorab Alirezaei Reza Tehrani Gholamreza Hashemzadeh Khourasgani
  10.22034/amfa.2019.1871644.1244
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  9 - Long Memory usage in Portfolio Optimization using the Copula‌ Functions: Empirical evidence of Iran and Turkey Stock Markets
  Hasti Chitsazan Motahareh Moghadasi Reza Tehrani Mohsen Mehrara
  The main objective of this paper is to optimize and manage the portfolio by using copula functions. Copula function has been using as a powerful and flexible tool for the determination of dependency structure. Research data include the Iran stock market index and the Tu More

  The main objective of this paper is to optimize and manage the portfolio by using copula functions. Copula function has been using as a powerful and flexible tool for the determination of dependency structure. Research data include the Iran stock market index and the Turkey stock market index. The present study seeks to find the effect of long memory on the structure of dependence between returns and optimal portfolio structure. In the first step, we compare the dependence structure between the net returns and the filter generated from the ARFIMA-GARCH process returns to investigate the impact of long memory on them. In the second step, the influence of the dependence structure between net returns and filtered returns on portfolio optimization has been investigated. The results indicated that the model can be fitted to the return of time series and the best pattern is the frank pattern. The results also indicated the existence of long memory in the mean and variance of stock return on the Iran stock market and the existence of long memory in the variance of the Turkey stock market. All models allocate more percentage of capital to Iran stock market and lower percent to Turkey stock market. Manuscript profile
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  10 - Forecasting Volatility & Risk Management in Tehran Stock Exchange through Long memory impacts
  ehsan Taiebysani Madihe Changi Ashtiani
  In  this  paper  we explored  the  relevance  of  asymmetry  and  long  memory  in  modeling  and  forecasting  the  conditional volatility and market risk of equity market in Iran capital M More

  In  this  paper  we explored  the  relevance  of  asymmetry  and  long  memory  in  modeling  and  forecasting  the  conditional volatility and market risk of equity market in Iran capital Market (Tehran Stock exchange(TSE) and Iran Fara Bourse(IFB)). A broad set of the most popular linear and nonlinear GARCH (generalized autoregressive conditional Heteroskedasticity)-type models is used to investigate this relevancy of asymmetry and long memory. Our in sample and out-of-sample results  displayed  that volatility  of commodity  returns can be  better described  by  nonlinear volatility models accommodating the long memory and asymmetry features. In particular, the FIAPARCH (Fractionally Integrated Asymmetric Power ARCH) model is found to be the best suited for estimating the VaR forecasts for both short and long trading positions. This model given a risk exposure at the 99% confidence interval level have Several implications for equity market risks, policy regulations and hedging strategies can be drawn from the obtained results of this paper. Manuscript profile