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Financial Risk Management for Cryptocurrencies A QUANTITATIVE - PowerPoint PPT Presentation

Oct 07, 2022 •226 likes •351 views

Financial Risk Management for Cryptocurrencies A QUANTITATIVE ANALYSIS Eline Van der Auwera Wim Schoutens Marco Petracco Lucia Alessi ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI Introduction Correlation

  1. Financial Risk Management for Cryptocurrencies A QUANTITATIVE ANALYSIS Eline Van der Auwera Wim Schoutens Marco Petracco Lucia Alessi ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  2. Introduction • Correlation between cryptocurrencies and other asset classes • Distributional properties • Volatile behaviour • ARMA-GARCH • Conclusion ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  3. Correlation 2016 2017 2018 • The cryptocurrency market becomes more correlated over time • In the beginning only coins with similar characteristics, like Bitcoin (BTC) and Litecoin (LTC) were correlated • Many cryptocurrencies are bought using Ether and Bitcoin ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  4. Correlation EUR-USD XR - BTC Gold-BTC S&P500 - BTC • 10-day correlation fluctuates around zero • 180-day correlation never exceeds 30% in absolute value -> Differentiated risk reducer ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  5. Distributional properties • Periods of high returns and low returns cluster together • Fat tails ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  6. Distributional properties • Excess kurtosis • Which standardised distribution fits best? • Maximum likelihood estimation for the best fitting parameters • KS-test statistic to determine the goodness-of-fit T-distribution All distributions ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  7. Volatile behaviour • Extremely volatile • Volatility clustering x10 ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  8. ARMA-GARCH • Returns are anti-persistent (fluctuate heavily + mean reverting) according to Hurst parameter • Returns exhibit autocorrelation -> AR, MA and GARCH part are needed to accurately model the returns ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  9. ARMA(2,2)-GARCH(1,3) • • Ljung-Box test cannot be rejected • No autocorrelation left • Arch LM test cannot be AR rejected • No arch effect left MA ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  10. ARMA-GARCH for VaR prediction Breaches Observations Value at risk ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

  11. Conclusion • The market is extremely inter-correlated and Bitcoin has the first mover advantage • Differentiated risk reducer • Cryptocurrencies have fat tails and high kurtosis -> t-distribution • Volatility clustering and mean-reverting behaviour -> anti-persistent • An ARMA(2,2)-GARCH(1,3) model is the best fitting model to the log returns of Bitcoin • It allows for an accurate VaR prediction ELINE VAN DER AUWERA, WIM SCHOUTENS, MARCO PETRACCO & LUCIA ALESSI

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