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Working paper
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Working paper
Multiple Chains Hidden Markov Models for Bivariate Dynamical Systems
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Working paper
Forecasting cryptocurrency volatility
In: International journal of forecasting, Band 38, Heft 3, S. 878-894
ISSN: 0169-2070
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Forecasting volatility with time-varying leverage and volatility of volatility effects
In: International journal of forecasting, Band 36, Heft 4, S. 1301-1317
ISSN: 0169-2070
Semiparametric Modeling of Multiple Quantiles
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Bitcoin at High Frequency
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Modelling Crypto-Currencies Financial Time-Series
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Forecasting cryptocurrencies under model and parameter instability
In: International journal of forecasting, Band 35, Heft 2, S. 485-501
ISSN: 0169-2070
Predicting the Volatility of Cryptocurrency Time–Series
In: Catania , L , Grassi , S & Ravazzolo , F 2018 , Predicting the Volatility of Cryptocurrency Time–Series . in M Corazza , M Durbán , A Grané , C Perna & M Sibillo (eds) , Mathematical and Statistical Methods for Actuarial Sciences and Finance . Springer , pp. 203-207 , MAF Conference , Madrid , Spain , 04/04/2018 .
Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regu- lation and their market far from being "efficient", require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evo- lution of the cryptocurrencies' volatility has been found to exhibit at the same time differences and similarities with other financial time–series, e.g. foreign exchanges returns. This short note focuses on predicting the conditional volatility of the four most traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investi- gate the effect of accounting for long memory in the volatility process as well as its asymmetric reaction to past values of the series to predict: one day, one and two weeks volatility levels.
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Generalized Autoregressive Score Models in R: The GAS Package
In: Journal of Statistical Software, Band 88, Heft 6, S. 1-28
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Dynamic Multiple Quantile Models
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