An ampirical research on the use of cryptocurrencies in risk management

As of March 25, 2021, 8937 cryptocurrencies are traded on cryptocurrency exchanges and their market value is 1 trillion 647 billion USD. It is likely that financial payments will become decentralized in the future and cryptocurrencies will shrink the current electronic money market. This issue attracts the attention of some investors, companies, financial actors and governments. In addition, the factors affecting cryptocurrencies, their risks and how they can be used in risk management are among the important issues. In this thesis, firstly, 11 cryptocurrencies, which dominate the crypto space, have the highest market capitalization and have a history of at least 1273 days, are introduced. These are Bitcoin, Ethereum, Ripple, EOS, Litecoin, Bitcoin Cash, Tether, Binance Coin, Bitcoin SV, Tezos and Cardano. Secondly, the model that best shows the volatility of Bitcoin, which has the largest capitalization and the largest transaction volume in the crypto money market, has been examined. The time series used in this part of the study are the daily closing price and return series in USD (USD) and cover the 2860-day period between April 30, 2013 and February 26, 2021. The most suitable volatility model for Bitcoin was estimated using GARCH class models. It was concluded that the HYGARCH model made the most appropriate estimation among the volatility prediction models used in the research, GARCH, EGARCH, IGARCH, GJR, FIGARCH-BBM, FIGARCH-CHUNG, FIEGARCH, FIAPARCH-BBM, FIAPARCH-CHUNG and HYGARCH. Convergence could not be achieved for the EGARCH and FIEGARCH models. Thirdly, using GARCH class models for Bitcoin, the model that best predicts out-of-sample volatility for one, five and ten-day timeframes is estimated. It was concluded that while the FIAPARCH-BBM model showed the best forecasting performance for 1-day forecasting, the FIGARCH-CHUNG model showed the 5- and 10-day forecasting performance.