Optimal Prediction with Conditionally Heteroskedastic Factor Analysed Hidden Markov Models

Saidane, Mohamed; Lavergne, Christian

doi:10.1007/s10614-009-9181-7

Cited by 5 publications

(1 citation statement)

References 55 publications

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“…In this case, ∀ l = 1, ..., k, each latent factor f lt follows a first order generalized quadratic autoregressive conditionally heteroskedastic, GQARCH-type processes (Saidane, 2009). The l-th element of the diagonal covariance matrix H jt , is given by:…”

Section: Dynamic Factor Structurementioning

confidence: 99%

Risk assessment in cryptocurrency portfolios: a composite hidden Markov factor analysis framework

Saidane

2024

Stat., optim. inf. comput.

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In this paper, we deal with the estimation of two widely used risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES) in a cryptocurrency context. To face the presence of regime switching in the cryptocurrency volatilities and the dynamic interconnection between them, we propose a Monte Carlo-based approach using heteroskedastic factor analysis and hidden Markov models (HMM) combined with a structured variational Expectation-Maximization (EM) learning approach. This composite approach allows the construction of a diversified portfolio and determines an optimal allocation strategy making it possible to minimize the conditional risk of the portfolio and maximize the return. The out-of-sample prediction experiments show that the composite factorial HMM approach performs better, in terms of prediction accuracy, than some other baseline methods presented in the literature. Moreover, our results show that the proposed methodology provides the best performing crypto-asset allocation strategies and it is also clearly superior to the existing methods in VaR and ES predictions.

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Section: Dynamic Factor Structurementioning

confidence: 99%

Risk assessment in cryptocurrency portfolios: a composite hidden Markov factor analysis framework

Saidane

2024

Stat., optim. inf. comput.

View full text Add to dashboard Cite

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Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

Chen

Shin

Kim

2016

Stoch Environ Res Risk Assess

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Switching latent factor value-at-risk models for conditionally heteroskedastic portfolios: A comparative approach

Saidane

2022

Communications in Statistics: Case Studies, Data Analysis and A

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Optimal Prediction with Conditionally Heteroskedastic Factor Analysed Hidden Markov Models

Abstract: Latent factor models, EM algorithm, Conditional heteroskedasticity, HMM, Time series segmentation, Forecasting, 62H25, 62M05, 62M10, 62P20,

Cited by 5 publications

References 55 publications

Risk assessment in cryptocurrency portfolios: a composite hidden Markov factor analysis framework

Risk assessment in cryptocurrency portfolios: a composite hidden Markov factor analysis framework

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

Switching latent factor value-at-risk models for conditionally heteroskedastic portfolios: A comparative approach

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