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Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models. (2023). Fantazzini, Dean.
In: MPRA Paper.
RePEc:pra:mprapa:117141.

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Cocites

Documents in RePEc which have cited the same bibliography

  1. Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models. (2023). Fantazzini, Dean.
    In: MPRA Paper.
    RePEc:pra:mprapa:117141.

    Full description at Econpapers || Download paper

  2. Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?. (2023). Liu, Xianglong.
    In: The Economic Record.
    RePEc:bla:ecorec:v:99:y:2023:i:325:p:288-312.

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  3. Predicting financial crises with machine learning methods. (2022). Wang, BO ; Chen, Chen ; Liu, Lanbiao.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:41:y:2022:i:5:p:871-910.

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  4. Random forest versus logit models: Which offers better early warning of fiscal stress?. (2022). Jarmulska, Barbara.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:41:y:2022:i:3:p:455-490.

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  5. Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death. (2022). Fantazzini, Dean.
    In: MPRA Paper.
    RePEc:pra:mprapa:113744.

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  6. Predicting European Banks Distress Events: Do Financial Information Producers Matter?. (2022). de Comeres, Quentin Bro.
    In: Working Papers.
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  7. Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death. (2022). Fantazzini, Dean.
    In: JRFM.
    RePEc:gam:jjrfmx:v:15:y:2022:i:7:p:304-:d:860084.

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  8. Debt is not free. (2022). Xiang, Yuan ; Gupta, Pranav ; Medas, Paulo ; Badia, Marialuz Moreno.
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  9. .

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  10. Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure. (2021). Fantazzini, Dean ; Calabrese, Raffaella.
    In: MPRA Paper.
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    Full description at Econpapers || Download paper

  11. Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure. (2021). Fantazzini, Dean ; Calabrese, Raffaella.
    In: JRFM.
    RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:516-:d:666046.

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  12. Sovereign Default Forecasting in the Era of the COVID-19 Crisis. (2021). Kristof, Tamas.
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  13. A Bayesian Signals Approach for the Detection of Crises. (2020). Tsionas, Mike ; Michaelides, Panayotis ; Xidonas, Panos.
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  14. A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies. (2020). Fantazzini, Dean ; Zimin, Stephan.
    In: Economia e Politica Industriale: Journal of Industrial and Business Economics.
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  15. Debt Is Not Free. (2020). Xiang, Yuan ; Gupta, Pranav ; Medas, Paulo ; Badia, Marialuz Moreno.
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  16. Predicting stock market crises using daily stock market valuation and investor sentiment indicators. (2020). Wu, Xiang ; Liu, Yufang ; Zhou, Qingling ; Fu, Junhui.
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  17. Random forest versus logit models: which offers better early warning of fiscal stress?. (2020). Jarmulska, Barbara.
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  18. A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies. (2019). Fantazzini, Dean ; Zimin, Stephan.
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  25. Credit Risk modeling for Companies Default Prediction using Neural Networks. (2016). Dima, Alina Mihaela ; Vasilache, Simona.
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  38. Random Survival Forests Models for SME Credit Risk Measurement. (2009). Fantazzini, Dean ; Figini, Silvia.
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  39. Asset price misalignments and the role of money and credit. (2009). Roffia, Barbara ; Reimers, Hans-Eggert ; Gerdesmeier, Dieter.
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  40. Credit Risk Management. (2008). Fantazzini, Dean.
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