Multivariate Markov-switching score-driven models: an application to the global crude oil market

Blazsek, Szabolcs; Escribano, Álvaro; Licht, Adrián

doi:10.1515/snde-2020-0099

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…the asymptotical consistency of the proposed two-step procedure of Blazsek, Escribano, and Licht (2020) for conditional copulas. The EM algorithm of Figueroa-Zúñiga, Niklitschek-Soto, Leiva, and Liu ( 2022) is modified in this study to estimate the dependence parameters subject to both the GAS dynamic and the Markovian structure.…”

Section: A Novel Approach To Estimate Ms-gas Modelmentioning

confidence: 98%

Forecasting Time-varying Value--at--Risk and Expected Shortfall Dependence: A Markov-switching Generalized Autoregressive Score Copula Approach

Makatjane

2024

AJS

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The importance of accurately forecasting extreme financial losses and their effects on the institutions involved in a given financial market has been highlighted by recent financial catastrophes. The flexibility with which econometric models can take into account the highly non-linear and asymmetric dependence in financial returns is a critical component of their capacity to forecast extreme events. Therefore, this study aims to forecast time-varying Value-at-Risk and expected shortfall dependence as a predictive density-based regime changes over time. To achieve this, a non-stationary Markov-switching generalized Autoregressive score model nested with copula is estimated using expectation–maximization (EM) algorithm. Extending this non-stationary model is quite challenging, as it requires specifications not only on how the usual parameters change over time but also those with mass distribution components. Dynamics of the estimated autoregressive score allowed the copula parameters to respond rapidly to time-varying key systemic parameters and risk. This is because regime changes are allowed to oscillated between high and low regimes. This is a clear indication of a regime shift in the parameters of an estimated model. Using the minimum score combining, six extreme value distributions are combined to the estimated MS(2)-GAS(1)-copula model and assessed the performance of each combined model 5 days and 30 days forecasting of value-at-risk and expected shortfall. The results of the forecasting performance indicated that the MS(2)-GAS(1)-GPD is the best model to model and forecast Value-at-risk and expected shortfall for the Botswana stock market. This is a promising technique for stochastic modeling of time-varying Value-at-Risk and Expected Shortfall. In addition, a foundation is provided for future researchers to conduct studies on emerging markets. These results are also important for risk managers and investors.

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Section: A Novel Approach To Estimate Ms-gas Modelmentioning

confidence: 98%

Forecasting Time-varying Value--at--Risk and Expected Shortfall Dependence: A Markov-switching Generalized Autoregressive Score Copula Approach

Makatjane

2024

AJS

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“…For technical details on the statistical inference of score-driven models, we refer to the works of Harvey and Chakravarty (2008), Harvey (2013), Creal et al (2008Creal et al ( , 2011Creal et al ( , 2013, and Blasques et al (2021). For the multivariate score-driven models (such as the score-driven ice-age models), we also refer to the recent works of Blazsek et al (2020Blazsek et al ( , 2021aBlazsek et al ( , 2021b.…”

Section: Score-driven Homoskedastic Ice-age Modelmentioning

confidence: 99%

Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models

Blazsek

Escribano

2022

Econometrics

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We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous orbital variables: eccentricity of the Earth’s orbit, obliquity, and precession of the equinox. We introduce score-driven ice-age models which use robust filters of the conditional mean and variance, generalizing the updating mechanism and solving the misspecification of a recent climate–econometric model (benchmark ice-age model). The score-driven models control for omitted exogenous variables and extreme events, using more general dynamic structures and heteroskedasticity. We find that the score-driven models improve the performance of the benchmark ice-age model. We provide out-of-sample forecasts of the climate variables for the last 100 thousand years. We show that during the last 10–15 thousand years of the forecasting period, for which humanity influenced the Earth’s climate, (i) the forecasts of Icet are above the observed Icet, (ii) the forecasts of CO2,t level are below the observed CO2,t, and (iii) the forecasts of Tempt are below the observed Tempt. The forecasts for the benchmark ice-age model are reinforced by the score-driven models.

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“…In the work of Creal et al (2014), the score-driven t-QVARMA model is introduced for I(0) variables. In the recent works of Blazsek, Escribano, and Licht (2020, 2021a, 2021b, the statistical performance of t-QVARMA is studied and the model is extended to combinations of I(0) and co-integrated I(1) variables, which are further extended in the present work to include exogenous explanatory variables.…”

Section: Score-driven Time Series Modelsmentioning

confidence: 99%

Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions

Blazsek

Kobor

Blazsek

2022

Studies in Nonlinear Dynamics &Amp; Econometrics

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In this paper, the effects of United States (US) policy actions on mortgage-backed security and mortgage loan spreads are measured, by using data before, during, and after the US subprime mortgage crisis. We study the effects of the following policy actions: (i) the placement of Fannie Mae and Freddie Mac into US Government conservatorship; (ii) the US Federal Reserve quantitative easing (QE) programs. We provide the following contributions to the literature: (i) for a robust measurement of policy effects, a new multi-equation score-driven t-QVARMA (quasi-vector autoregressive moving average) model is used. (ii) In addition to the measurement of the effects of QE, the effects of government conservatorship are also measured in this paper. (iii) Furthermore, the data period of the relevant literature is extended to the period of June 1998 to March 2020.

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Multivariate Markov-switching score-driven models: an application to the global crude oil market

Cited by 11 publications

References 46 publications

Forecasting Time-varying Value--at--Risk and Expected Shortfall Dependence: A Markov-switching Generalized Autoregressive Score Copula Approach

Forecasting Time-varying Value--at--Risk and Expected Shortfall Dependence: A Markov-switching Generalized Autoregressive Score Copula Approach

Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models

Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions

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