Mohammad Qezelbash; Saeid Tajdini; Farzad Jafari; Majid Lotfi Ghahroud; Mohammad Farajnezhad
Abstract
In recent years, cryptocurrency has attracted more attention and is a new option in the economy and the financial sector. The purpose of this study is to the volatility and “herd behavior” of the cryptocurrency, gold, and stock markets in the US. This research is aimed at investor “herd ...
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In recent years, cryptocurrency has attracted more attention and is a new option in the economy and the financial sector. The purpose of this study is to the volatility and “herd behavior” of the cryptocurrency, gold, and stock markets in the US. This research is aimed at investor “herd behavior” and how it correlates with the volatility of three assets: the Standard & Poor's 500 indexes, Bitcoin, and gold. Also, A new formula by applying the conditional standard deviation (risk), maximum return, minimum return, and average return to quantify the herding bias is designed in this research. In this study, the generalized autoregressive conditional heteroscedasticity model (GARCH) and the autoregressive moving average model (ARMA) were both employed. Research results show that Bitcoin is 3.3 times as volatile as the S&P 500 and 4.6 times as volatile as gold. The results of this novel equation also show that the herding bias of Bitcoin is more than 26 times higher than the global average and 10 times higher than the S&P 500. Also, it’s important to consider the energy consumption and sustainability of investments when evaluating their long-term viability and risk. In some cases, investments in companies with strong sustainability practices and low carbon footprints may be seen as lower risk. Since Bitcoin relies on a network of computers to validate transactions based on proof of work and it is an energy consumption consensus mechanism, investment in Bitcoin may be seen as a higher risk.
Majid Lotfi Ghahroud; Farzad Jafari; Saeid Tajdini; Mohammad Farajnezhad; Mohammad Qezelbash
Abstract
This study examines the dynamics of the Iranian foreign exchange market and its impact on the exchange rate used by traders, and not the official rate in Iran. The study aims to extend Fama's theory of market efficiency and proposes a new model to define the opposite point called "Historical bias". The ...
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This study examines the dynamics of the Iranian foreign exchange market and its impact on the exchange rate used by traders, and not the official rate in Iran. The study aims to extend Fama's theory of market efficiency and proposes a new model to define the opposite point called "Historical bias". The study applied the ARIMA and Markov switching models and dynamic conditional correlation to measure the speed of information circulation and to investigate the origin of the Iranian foreign exchange market's impact on the trader rate of the Dollar market. The study analyzed the convergence of the Iranian foreign exchange market based on different rates, the exchange rate used by traders, and the official rate and its effect on developing CBDC in Iran. The results of this study show that based on Fama's theory of market efficiency the foreign exchange market in Iran could have a 15% history-oriented bias, which is significant and would be an important problem for the launching of CBDC in Iran.
Farzad Jafari; Amir Hamooni; Saeid Tajdini; Mohammad Qezelbash; Niloufar Ebrahimiyan
Abstract
In this study, based on the monetary behavior theory, which considers the mean and standard deviation of GDP per capita besides the inflation difference between two countries, we first present a model for determining the fair value of the Russian ruble in the long run from 1999 to 2021 based on macroeconomic ...
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In this study, based on the monetary behavior theory, which considers the mean and standard deviation of GDP per capita besides the inflation difference between two countries, we first present a model for determining the fair value of the Russian ruble in the long run from 1999 to 2021 based on macroeconomic indicators including inflation, and GDP per capita. And then we modeled the effect of widespread Russian economic sanctions on the value of the Russian ruble during the turbulent days of February 9 to April 9. Our research results show that there is not much difference between market value and fair value in the long run. Also, by observing the behavior of the ruble during the turbulent days of February 25, 2022, to April 26, 2022, and by entering the conditional risk factor and weighted average of the ruble, the USD to ruble equality between 76.23 and 91.6 was evaluated
Saeid Tajdini; Farzad Jafari; Majid Lotfi Ghahroud
Abstract
According to the literature on risk, bad news induces higher volatility than good news. Although parametric procedures used for conditional variance modeling are associated with model risk, this may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification ...
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According to the literature on risk, bad news induces higher volatility than good news. Although parametric procedures used for conditional variance modeling are associated with model risk, this may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. For inferring non-linear financial time series, various parametric and non-parametric models are generally used. Since the leverage effect refers to the generally negative correlation between an asset return and its volatility, models such as GJRGARCH and EGARCH have been designed to model leverage effects. However, in some cases, like the Tehran Stock Exchange, the results are different in comparison with some famous stock exchanges such as the S&P500 index of the New York Stock Exchange and the DAX30 index of the Frankfurt Stock Exchange. The purpose of this study is to show this difference and introduce and model the "reversed leverage effect bias" in the indices and stocks in the Tehran Stock Exchange.
