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https://doi.org/10.17535/crorr.2019.0021

Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models

Mohamed Saidane ; College of Business and Economics, Qassim University, Buraidah

Puni tekst: engleski, pdf (573 KB) str. 241-255 preuzimanja: 212* citiraj
APA 6th Edition
Saidane, M. (2019). Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models. Croatian Operational Research Review, 10 (2), 241-255. https://doi.org/10.17535/crorr.2019.0021
MLA 8th Edition
Saidane, Mohamed. "Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models." Croatian Operational Research Review, vol. 10, br. 2, 2019, str. 241-255. https://doi.org/10.17535/crorr.2019.0021. Citirano 28.07.2021.
Chicago 17th Edition
Saidane, Mohamed. "Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models." Croatian Operational Research Review 10, br. 2 (2019): 241-255. https://doi.org/10.17535/crorr.2019.0021
Harvard
Saidane, M. (2019). 'Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models', Croatian Operational Research Review, 10(2), str. 241-255. https://doi.org/10.17535/crorr.2019.0021
Vancouver
Saidane M. Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models. Croatian Operational Research Review [Internet]. 2019 [pristupljeno 28.07.2021.];10(2):241-255. https://doi.org/10.17535/crorr.2019.0021
IEEE
M. Saidane, "Forecasting portfolio-Value-at-Risk with mixed factorial hidden Markov models", Croatian Operational Research Review, vol.10, br. 2, str. 241-255, 2019. [Online]. https://doi.org/10.17535/crorr.2019.0021

Sažetak

This paper is concerned with the statistical modeling of the latent dependence and comovement structures of multivariate financial data using a new approach based on mixed factorial hidden Markov models, and their applications in Value-at-Risk (VaR) valuation. This approach combines hidden Markov Models (HMM) with mixed latent factor models. The HMM generates a piece-wise constant state evolution process and the observations are produced from the state vectors by a mixture of factor analyzers observation process. This new switching specification provides an alternative, compact, model to handle intra-frame correlation and unobserved heterogeneity in financial data. For maximum likelihood estimation we have proposed an iterative approach based on the Expectation-Maximisation (EM) algorithm. Using a set of historical data, from the Tunisian foreign exchange market, the model parameters are estimated. Then, the fitted model combined with a modified Monte-Carlo simulation algorithm was used to predict the VaR of the Tunisian public debt portfolio. Through a backtesting procedure, we found that this new specification exhibits a good fit to the data, improves the accuracy of VaR predictions and can avoid serious violations when a financial crisis occurs.

Ključne riječi
mixed latent factor models; hidden Markov models; unobserved heterogeneity; EM algorithm; Value-at-Risk

Hrčak ID: 229884

URI
https://hrcak.srce.hr/229884

Posjeta: 396 *