تعیین علیت مابین ریسک‌های سیستماتیک و غیرسیستمایک موثر بر نوسان قیمت رمز ارزها در سطوح مختلف نااطمینانی

: Cryptocurrency price fluctuations have a non-linear structure and generally do not follow a random process; These fluctuations depend on time and have long-term inherent cyclical and unpredictable trends. These characteristics make it difficult to model the price fluctuations of cryptocurrencies. Therefore, it is very important to identify the most important variables affecting fluctuations in digital currency markets. Based on the above explanations, the complexity of financial relations has caused the high interaction of the cryptocurrency market with other markets; This complexity requires dynamic approaches for a comprehensive understanding and correct attitude towards the fluctuations of this market. In this research, a new and systematic approach using Bayesian averaging models and time-varying parameter vector autoregression models has been used to model the price fluctuations of cryptocurrencies at different levels of uncertainty. Based on this, the aim of the current research is to model the factors affecting the price fluctuations of cryptocurrencies over time at different levels of uncertainty.



Methodology: The present study is based on the purpose of applied studies. Research data were extracted from Economic Information Bank (FRED), Economic Trade Bank and World Bank. In this research, 26 factors were identified. In this research, 26 factors were identified. These factors are divided into two categories of non-market factors (such as gold; the exchange rate of the dollar and the euro; the stock price index of the New York Stock Exchange; the S&P 500 index; the tweets of trailers and market leaders (good and bad news); the Dow Jones Industrial Average; the consumer price index United States; Federal Funds Rate; MSCI Emerging Market Indices; Goldman Sachs Commodity Index; Goldman Sachs Energy Index; US 10-Year Treasury Yield; Oil Prices; Average Precious Metal Prices; US Economic Growth; Euro Area Economic Growth; Global Economic Growth; Deficit US budget) and market-related factors (cryptocurrency opening price; maximum price; minimum price; closing price; volume of exchanges; daily number of cryptocurrency transactions; daily cryptocurrency network difficulty; market value) were categorized. In the first part, the GARCH and random fluctuation models, and in the second part, the TVP-DMA and BMA models, and finally, the output results of the TVP-QVAR model are presented.



Findings and Discussion: Based on the results, SV models were more accurate than GARCH models in extracting fluctuations. The BMA model is more accurate and out of the 26 variables examined; 8 non-fragile variables affecting cryptocurrency price fluctuations were determined by this approach. TVP-Quantile VAR model approach has been used to investigate the relationship between variables. According to the results, in the conditions where the levels of uncertainty are low (5th percentile of uncertainty), there is the most cause of fluctuation transfer from internal factors to price fluctuations of cryptocurrencies. This relationship was observed in a weaker state in external factors as well. No causal relationship was observed in other variables. In the conditions where the levels of uncertainty are medium (fiftieth percentile of uncertainty), there is most of the cause of the transfer of volatility, as in the previous case, from internal factors to price fluctuations of cryptocurrencies; In this case, unlike the previous case, a causal relationship was observed from external factors to internal factors. In the conditions of high levels of uncertainty (ninety-fifth percentile of uncertainty), contrary to the previous situations, the direction of causality has changed and there are internal and external factors among the factors of fluctuations in the price of cryptocurrencies. In other words, the extreme fluctuation of the price of cryptocurrencies can change the environmental conditions.



Conclusion and Policy Implications: Based on the results, SV models are more accurate than GARCH models in extracting fluctuations. Among the TVPDMA, TVPDMS and BMA models, the BMA model was determined to be more accurate in order to identify the most important non-fragile variables affecting the price fluctuations of cryptocurrencies. The results of this approach indicated the fact that the variables of exchange rate of dollar and euro, tweets of trailers and market leaders, number of daily transactions of cryptocurrencies, daily difficulty of cryptocurrency network, economic growth of America, opening price of cryptocurrency and volume of exchanges; The most important variables affecting the fluctuation of the price of cryptocurrencies are. Based on the results, the following suggestions can be made.

In the TVP-Quantile VAR model, in conditions where there are low levels of uncertainty (the fifth percentile of uncertainty), there is the most cause of fluctuation transfer from internal factors to price fluctuations of cryptocurrencies. This relationship was observed in a weaker state in external factors as well. No causal relationship was observed in other variables. In the conditions where the levels of uncertainty are medium (fiftieth percentile of uncertainty), there is most of the cause of the transfer of volatility, as in the previous case, from internal factors to price fluctuations of cryptocurrencies; In this case, unlike the previous case, a causal relationship was observed from external factors to internal factors. In the conditions of high levels of uncertainty (ninety-fifth percentile of uncertainty), contrary to the previous situations, the direction of causality has changed and there are internal and external factors among the factors of fluctuations in the price of cryptocurrencies. In other words, the extreme fluctuation of the price of cryptocurrencies can change the environmental conditions.

Due to the change of causality in different levels of uncertainty in cryptocurrencies; Before implementing any kind of policy in the field of controlling the fluctuations of the cryptocurrency market, it is necessary; The relevant level of uncertainty is determined and a policy based on those conditions should be adopted in any situation. This systemic view reduces the losses caused by the policies and decisions of managers in the field of financial markets.