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Forecasting extreme financial risk : a score-driven approach
Fuentes, Fernanda; Herrera, Rodrigo; Clements, Adam - In: International journal of forecasting 39 (2023) 2, pp. 720-735
Persistent link: https://www.econbiz.de/10014465107
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Modeling and Forecasting Realized Portfolio Weights
Golosnoy, Vasyl; Gribisch, Bastian - 2020
the S&P500 we find that our direct models effectively minimize either statistical or economic forecasting losses both in …
Persistent link: https://www.econbiz.de/10014352129
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Forecasting value at risk and expected shortfall using high-frequency data of domestic and international stock markets
Wang, Man; Cheng, Yihan - In: Journal of forecasting 41 (2022) 8, pp. 1595-1607
Persistent link: https://www.econbiz.de/10013465725
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Conditional value-at-risk forecasts of an optimal foreign currency portfolio
Kim, Dongwhan; Kang, Kyu Ho - In: International journal of forecasting 37 (2021) 2, pp. 838-861
Persistent link: https://www.econbiz.de/10012792873
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Option-implied information and predictability of extreme returns
Vilkovz, Grigory; Xiaox, Yan - 2013 - This version: January 28, 2013
We study whether prices of traded options contain information about future extreme market events. Our option-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and probability of the market crashes, beyond and above...
Persistent link: https://www.econbiz.de/10010226098
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The economic value of volatility timing with realized jumps
Nolte, Ingmar; Xu, Qi - In: Journal of empirical finance 34 (2015), pp. 45-59
Persistent link: https://www.econbiz.de/10011556992
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A latent dynamic factor approach to forecasting multivariate stock market volatility
Gribisch, Bastian - In: Empirical economics : a journal of the Institute for … 55 (2018) 2, pp. 621-651
Persistent link: https://www.econbiz.de/10011949857
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Cryptocurrencies : a copula based approach for asymmetric risk marginal allocations
Jeleskovic, Vahidin; Meloni, Mirko; Younas, Zahid Irshad - 2020
volatility dynamics of the cryptocurrency market, realized volatility measures computed from different frames (1m, 5m, 15m, 30m …, 1h) are included in the estimation of univariate GARCH models, to be used in combination with copula functions for VaR …/ES Monte Carlo simulations. Even if results lack data frequency ordinality in terms of out-of-sample goodness, Bitcoin and …
Persistent link: https://www.econbiz.de/10012542685
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On equity risk prediction and tail spillovers
Pouliasis, Panos; Kyriakou, Ioannis; Papapostolou, Nikos - In: International journal of finance & economics : IJFE 22 (2017) 4, pp. 379-393
Persistent link: https://www.econbiz.de/10011960379
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Does the tail risk index matter in forecasting downside risk?
Hung, Jui-Cheng; Liu, Hung-Chun; Yang, J. Jimmy - In: International journal of finance & economics : IJFE 28 (2023) 3, pp. 3451-3466
Persistent link: https://www.econbiz.de/10014327761