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Risk measurement of cryptocurrencies using value at risk and expected shortfall

Cao Thi Hong, Van LU (2022) NEKN02 20221
Department of Economics
Abstract
Cryptocurrencies are highly volatile and risky assets, therefore, it is of vital importance to find an appropriate model for risk measurement. This thesis compares three parametric and three non-parametric estimation methods to estimate the value at risk and the expected shortfall of five cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Binance coin (BNB), Ripple coin (XRP), and Cardano (ADA). We estimate the value at risk and expected shortfall using these methods at the confidence level of 95% and 99%. We then perform five backtesting procedures and use these test results to compare the performance of these estimation methods. Consequently, we can conclude that the volatility-weighted historical simulation (VWHS) method using the... (More)
Cryptocurrencies are highly volatile and risky assets, therefore, it is of vital importance to find an appropriate model for risk measurement. This thesis compares three parametric and three non-parametric estimation methods to estimate the value at risk and the expected shortfall of five cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Binance coin (BNB), Ripple coin (XRP), and Cardano (ADA). We estimate the value at risk and expected shortfall using these methods at the confidence level of 95% and 99%. We then perform five backtesting procedures and use these test results to compare the performance of these estimation methods. Consequently, we can conclude that the volatility-weighted historical simulation (VWHS) method using the exponential weighted moving average (EWMA) model and GARCH-type models to rescale cryptocurrency loss for VaR and ES estimation perform the best in most cases. The basic historical simulation (BHS) method and the peak over threshold (POT) method also show positive performance in several cases. Meanwhile, the age-weighted historical simulation (AWHS) has a poor performance in almost all cases. (Less)
Please use this url to cite or link to this publication:
author
Cao Thi Hong, Van LU
supervisor
organization
course
NEKN02 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
cryptocurrencies, value at risk, expected shortfall, risk measurement, parametric methods, non-parametric methods, EWMA, GARCH, EGARCH, GJRGARCH, backtesting
language
English
id
9084496
date added to LUP
2022-10-10 09:29:06
date last changed
2022-10-10 09:29:06
@misc{9084496,
  abstract     = {{Cryptocurrencies are highly volatile and risky assets, therefore, it is of vital importance to find an appropriate model for risk measurement. This thesis compares three parametric and three non-parametric estimation methods to estimate the value at risk and the expected shortfall of five cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Binance coin (BNB), Ripple coin (XRP), and Cardano (ADA). We estimate the value at risk and expected shortfall using these methods at the confidence level of 95% and 99%. We then perform five backtesting procedures and use these test results to compare the performance of these estimation methods. Consequently, we can conclude that the volatility-weighted historical simulation (VWHS) method using the exponential weighted moving average (EWMA) model and GARCH-type models to rescale cryptocurrency loss for VaR and ES estimation perform the best in most cases. The basic historical simulation (BHS) method and the peak over threshold (POT) method also show positive performance in several cases. Meanwhile, the age-weighted historical simulation (AWHS) has a poor performance in almost all cases.}},
  author       = {{Cao Thi Hong, Van}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Risk measurement of cryptocurrencies using value at risk and expected shortfall}},
  year         = {{2022}},
}