Document Type : Original Article
Authors
1 Ph. D Student of Allameh Tabatabai University, Department of Economics, Tehran, Iran
2 Professor of Allameh Tabatabai University, Department of Economics, Tehran, Iran
Abstract
1- INTRODUCTION
Considering that cryptocurrencies exhibit commodity characteristics such as demand shocks, high price fluctuations, etc., cryptocurrencies can be compared with the behavior of the gold and oil markets (except when there is uncertainty about the supply conditions of gold and oil. There is no such uncertainty in the cryptocurrency market). Therefore, due to the commodity nature of Bitcoin, the price of oil and gold can affect the price fluctuations of cryptocurrencies. It seems that cryptocurrencies can play the role of a safe haven for commodity market investors, so the cryptocurrency market can cover the fluctuations in gold and oil prices. Commodity markets, which this study focuses on gold, oil and cryptocurrencies, have a series of characteristics. It seems that the gold market has surpassed the cryptocurrency and oil market in absorbing information, while the cryptocurrency market has higher price fluctuations than the gold and oil markets. Empirical evidence shows that Bitcoin can have a close relationship with the commodity market.Therefore, due to the commodity nature of Bitcoin, the price of oil and gold can affect the price fluctuations of cryptocurrencies.بنابراین، به دلیل ماهیت کالایی بیت کوین، قیمت نفت و طلا می تواند بر نوسانات قیمت ارزهای رمزپایه تأثیر بگذارد.
Therefore, due to the commodity nature of bitcoin, the price of oil and gold can affect the fluctuations of the price of crypto-currencies.
بنابراین، به دلیل ماهیت کالایی بیت کوین، قیمت نفت و طلا می تواند بر نوسانات قیمت ارزهای دیجیتال تأثیر بگذارد.
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2- THEORETICAL FRAMEWORK
Every person in the financial markets who has an asset portfolio tries to increase or maintain the value of his asset portfolio. The position of each asset in the portfolio has two characteristics: return (price change) and risk (price volatility). The behavior of asset portfolio owners is such that they try to increase returns and reduce risk. In this framework, they buy or sell assets in their portfolio in order not only to prevent the value of their asset portfolio from decreasing, but also to increase the value of their wealth. This behavior of the capital owners leads to the creation of connections between the global markets, including oil, gold, and cryptocurrencies, so that their yield fluctuations are connected to each other through the risk spillover effect. The asset allocation models have been investigated in a practical way for about half a century. The most well-known asset allocation model is the mean-variance strategy (modern portfolio theory), which was first developed by Markowitz (1952) to describe the process of optimal capital allocation, assuming a fixed investment opportunity set, between different asset groups over a period.
3- METHODOLOGY
In this study, the 𝑉𝐴𝑅− 𝑀𝐺𝐴𝑅𝐶𝐻 − 𝐺𝐽𝑅 – 𝐵𝐸𝐾𝐾 model has been used in order to investigate the asymmetric effects of turbulence spillover between the oil, gold and bitcoin markets because of the following advantages. First, this model has high flexibility. Second, in this model, unlike constant conditional correlation (CCC) models, the conditional correlation changes over time. In addition, it is possible to check several markets at the same time. The existence of the covariance equation makes it possible to examine the simultaneous relationship between two markets. In the BEKK model, the fluctuations of a market are affected by the fluctuations and shocks of other markets, the shocks of that market and the covariance of the markets. In other words, the effects of the markets on each other, which is reflected in the delayed covariance, have an effect on the fluctuations of the markets. These effects can be symmetrical or asymmetrical. Also, this model makes it possible to have a dynamic dependence between the fluctuations of the variables. The only disadvantage of this model is that it is not suitable for examining more than three or four markets due to the increase in parameters.
4- RESULTS & DISCUSSION
The results indicate that the contribution of the memory of turbulence in explaining the current turbulence is greater than the impact of past shocks. The impact of past impulses and the memory of the turbulences of cryptocurrencies is high on the turbulences of this market. In other words, it can be said that fluctuations in the cryptocurrency market are significantly explained by the past impulses of this market. The results show that there is one-way turbulence spillover from the Bitcoin market to the gold market and the oil market, but the opposite is not true. The results of the study also indicate leverage effects in the markets. The leverage effects of the gold market shock along with the oil and bitcoin market shocks on the gold market are significant. The leverage effects of the oil market shock along with the gold and bitcoin market shocks are also significant on the oil market and the leverage effects are also significant for the bitcoin market.
The results of the study indicate leverage effects in the markets, in other words, positive and negative shocks have different effects on price fluctuations, and bad news has a greater effect than good news on price fluctuations.
نتایج تحقیق حاکی از تأثیرات اهرمی در بازارها است، به عبارت دیگر شوک های مثبت و منفی تأثیر متفاوتی بر نوسانات قیمتی دارند و اخبار بد تأثیر بیشتری نسبت به اخبار خوب بر نوسانات قیمت دارند.
The results of the study indicate leverage effects in the markets, in other words, positive and negative shocks have different effects on price fluctuations and bad news has a greater effect on price fluctuations than good news.
نتایج تحقیق حاکی از تأثیرات اهرمی در بازارها است، به عبارت دیگر شوک های مثبت و منفی تأثیر متفاوتی بر نوسانات قیمتی دارند و اخبار بد تأثیر بیشتری بر نوسانات قیمتی نسبت به اخبار خوب دارند.
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Keywords
Main Subjects
Rostami, M.R., & Farahmandi, S. (2012). Estimating the value at risk of crude oil price and its spillover effects using the multivariable GARCH model, Journal of Investment Knowledge, (4). (In Persian)
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