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Exploring entropy-based portfolio strategies : empirical analysis and cryptocurrency impact

Giunta, Nicolò; Orlando, Giuseppe; Carleo, Alessandra; … - In: Risks : open access journal 12 (2024) 5, pp. 1-26

This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR),...

Persistent link: https://www.econbiz.de/10014636599

When the US stock market becomes extreme?

Aboura, Sofiane - In: Risks : open access journal 2 (2014) 2, pp. 211-225

real estate bubble burst, credit crunch and banking panics. As a response, extreme value theory (EVT) provides a set of …

Persistent link: https://www.econbiz.de/10010399734

Estimating and forecasting conditional risk measures with extreme value theory : a review

Bee, Marco; Trapin, Luca - In: Risks : open access journal 6 (2018) 2, pp. 1-16

financial econometrics literature have developed several models based on Extreme Value Theory (EVT) to carry out these tasks …

Persistent link: https://www.econbiz.de/10011866456

Multivariate spectral backtests of forecast distributions under unknown dependencies

Balter, Janine; McNeil, Alexander J. - In: Risks : open access journal 12 (2024) 1, pp. 1-15

Under the revised market risk framework of the Basel Committee on Banking Supervision, the model validation regime for internal models now requires that models capture the tail risk in profit-and-loss (P&L) distributions at the trading desk level. We develop multi-desk backtests, which...

Persistent link: https://www.econbiz.de/10014480976

Value-at-Risk effectiveness : a high-frequency data approach with semi-heavy tails

Contreras-Valdez, Mario Ivan; Sahu, Sonal; … - In: Risks : open access journal 12 (2024) 3, pp. 1-23

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing...

Persistent link: https://www.econbiz.de/10014497426

Modelling Australian dollar volatility at multiple horizons with high-frequency data

Long Hai Vo; Duc Hong Vo - In: Risks : open access journal 8 (2020) 3/89, pp. 1-16

Long-range dependency of the volatility of exchange-rate time series plays a crucial role in the evaluation of exchange … analysis. Findings from our analysis indicate that long-range dependence in volatility is observed and it is persistent across … horizons. However, this long-range dependence in volatility is most prominent at the horizon longer than daily. Policy …

Persistent link: https://www.econbiz.de/10012293280

Risk model validation : an intraday VaR and ES approach using the multiplicative component GARCH

Summinga-Sonagadu, Ravi; Narsoo, Jason - In: Risks : open access journal 7 (2019) 1/10, pp. 1-23

analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in …

Persistent link: https://www.econbiz.de/10012018629

High frequency price change spillovers in bitcoin markets

Giudici, Paolo; Pagnottoni, Paolo - In: Risks : open access journal 7 (2019) 4/111, pp. 1-18

The study of connectedness is key to assess spillover effects and identify lead-lag relationships among market exchanges trading the same asset. By means of an extension of Diebold and Yilmaz (2012) econometric connectedness measures, we examined the relationships of five major Bitcoin exchange...

Persistent link: https://www.econbiz.de/10012127873

Performance of the realized-GARCH model against other GARCH types in predicting cryptocurrency volatility

Queiroz, Rhenan G. S.; David, Sergio A. - In: Risks : open access journal 11 (2023) 12, pp. 1-13

growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional … Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has … been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an …

Persistent link: https://www.econbiz.de/10014446600

Conditional variance forecasts for long-term stock returns

Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - In: Risks : open access journal 7 (2019) 4/113, pp. 1-22

of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much …

Persistent link: https://www.econbiz.de/10012127861