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Šíření volatility na kryptoměnových trzích
Krampla, Dominik
This thesis investigated the identification of conditional volatility in cryptocurrency markets and explored how uncertainty spreads among various cryptocurrencies. Using GARCH family models, conditional volatility was modeled, and the DCC-GJR- GARCH(1,1) model was applied to identify the spread of conditional volatility, accounting for the impact of asymmetric shocks. The empirical analysis was based on high-frequency 15-minute data for five cryptocurrencies – Bitcoin, Ethereum, Ripple, Cardano, and Litecoin – from 23. April 2021 to 31. March 2022, with total number observations of 32 904 per cryptocurrency. The results suggest that uncertainty spreads most significantly between Bitcoin and Ethereum, while Ripple and Cardano are less affected by the spread of uncertainty from Bitcoin. The study also examines suitable combinations of cryptocurrency weights in various portfolio formation strategies, with the DCC-GJR-GARCH (1,1) strategy achieving the lowest risk.
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Šíření podmíněné volatility na kryptoměnových trzích
Hořava, Martin
Hořava, M. Conditional volatility spillovers in the cryptocurrency markets. Diploma thesis. Brno: Mendel University, 2022. The purpose of this thesis was to identify conditional volatility in cryptocurrency markets and the mutual dynamic volatility spillover between individual assests. The literature review describes conditional volatility and methods of its estimation. In the empirical part, the DCC-GARCH model was used and the portfolio was optimized. The results showed that cryptocurrencies are higly interconnected, but can still be diversified. At the end of the thesis, specific recommendations for the portfolio managers are provided.
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Value-at-risk forecasting with the ARMA-GARCH family of models during the recent financial crisis
Jánský, Ivo ; Rippel, Milan (advisor) ; Seidler, Jakub (referee)
The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting ac- curacy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index sepa- rately. Unlike other works in this eld of study, the thesis does not assume the log-returns to be normally distributed and does not explicitly select a partic- ular conditional volatility process. Moreover, the thesis takes advantage of a less known conditional coverage framework for the measurement of forecasting accuracy.
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Value-at-risk forecasting with the ARMA-GARCH family of models during the recent financial crisis
Jánský, Ivo ; Rippel, Milan (advisor) ; Seidler, Jakub (referee)
The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting accuracy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index separately. Unlike other works in this field of study, the thesis does not assume the log-returns to be normally distributed and does not explicitly select a particular conditional volatility process. Moreover, the thesis takes advantage of a less known conditional coverage framework for the measurement of forecasting accuracy.
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Value-at-risk forecasting with the ARMA-GARCH family of models during the recent financial crisis
Jánský, Ivo ; Rippel, Milan (advisor) ; Seidler, Jakub (referee)
The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting ac- curacy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index sepa- rately. Unlike other works in this eld of study, the thesis does not assume the log-returns to be normally distributed and does not explicitly select a partic- ular conditional volatility process. Moreover, the thesis takes advantage of a less known conditional coverage framework for the measurement of forecasting accuracy.
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