VEC-MSF models in Bayesian analysis of short- and long-run relationships

Pajor, Anna; Wróblewska, Justyna

doi:10.1515/snde-2016-0004

Cited by 5 publications

(16 citation statements)

References 25 publications

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“…The definition of { t } provided in (2), has been generalized in Pajor and Wróblewska (2017) by introducing random-variability into elements of : where t−1 denotes the past of the process { t } up to time t − 1 , q t is a latent variable, is a vector of parameters, and t = (q t , t−1 , t , ).…”

Section: Bayesian Vec Models With Stochastic Volatilitymentioning

confidence: 99%

“…In order to investigate the influence of assumptions (pertaining to the conditional covariance matrix) on the predictive abilities of the models we consider four structures for matrix t : constant, Multiplicative Stochastic Factor (MSF), SBEKK, and hybrid MSF-SBEKK [type I; see, e.g., Osiewalski (2009); Osiewalski and Pajor (2009); Pajor and Wróblewska (2017)].…”

Section: Bayesian Vec Models With Stochastic Volatilitymentioning

confidence: 99%

“…It remains to formulate the prior distribution of the parameter vector . We assume independence among certain blocks of parameters, and we use the same prior distributions as in Pajor and Wróblewska (2017):…”

Section: Prior Distribution and Sampling Schemementioning

confidence: 99%

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Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships

Pajor

Wróblewska

2022

Eurasian Econ Rev

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The paper is focused on comparing the forecasting performance of two relatively new types of Vector Error Correction - Multiplicative Stochastic Factor (VEC-MSF) specifications: VEC-MSF with constant conditional correlations, and VEC-MSF-SBEKK with time-varying conditional correlations. For the sake of comparison, random walks, vector autoregressions (VAR) with constant conditional covariance matrix, and VAR-SBEKK models are also considered. Based on daily quotations on three exchange rates: PLN/EUR, PLN/USD, and EUR/USD, where the cointegrating vector may be assumed to be known a priori, we show that in econometric models it can be more important to allow for cointegration relationships than for time-varying conditional covariance matrix.

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Section: Bayesian Vec Models With Stochastic Volatilitymentioning

confidence: 99%

Section: Bayesian Vec Models With Stochastic Volatilitymentioning

confidence: 99%

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Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships

Pajor

Wróblewska

2022

Eurasian Econ Rev

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“…The LN-MSF-SBEKK structure is obtained by multiplying the SBEKK conditional covariance matrix

by a scalar random variable

such that

is a Gaussian AR(1) latent process with autoregression parameter

. The hybrid LN-MSF-SBEKK specification has been recognized in the literature [ 32 , 33 , 34 , 35 ] and proved to be useful in multivariate modeling of financial time series as well as of macroeconomic data [ 36 , 37 , 38 , 39 , 40 , 41 ]. The drawback of the LN-MSF-MGARCH process is that it cannot be treated as a direct extension of the MGARCH process with the Student t conditional distribution.…”

Section: Empirical Illustration: Formal Bayesian Comparison Of Hybrid Msv-mgarch Modelsmentioning

confidence: 99%

New Estimators of the Bayes Factor for Models with High-Dimensional Parameter and/or Latent Variable Spaces

Pajor

2021

Entropy

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Formal Bayesian comparison of two competing models, based on the posterior odds ratio, amounts to estimation of the Bayes factor, which is equal to the ratio of respective two marginal data density values. In models with a large number of parameters and/or latent variables, they are expressed by high-dimensional integrals, which are often computationally infeasible. Therefore, other methods of evaluation of the Bayes factor are needed. In this paper, a new method of estimation of the Bayes factor is proposed. Simulation examples confirm good performance of the proposed estimators. Finally, these new estimators are used to formally compare different hybrid Multivariate Stochastic Volatility–Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MSV-MGARCH) models which have a large number of latent variables. The empirical results show, among other things, that the validity of reduction of the hybrid MSV-MGARCH model to the MGARCH specification depends on the analyzed data set as well as on prior assumptions about model parameters.

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“…The author assumed independence among the groups of parameters and used the following prior distributions (cf. Osiewalski and Pajor, 2019;Pajor and Wróblewska, 2017 • the multivariate t distribution for = [ 0 Φ 1 ]: ( | 2 ) = ( |0, , 2 +1 ) with inverted gamma distribution for 2 : ( 2 ) = ( 2 | 3, 2),…”

Section: The Msf-sbekk Modelsmentioning

confidence: 99%

Inverted gamma vs. log-normal innovations in MSFSBEKK models in the forecasting of selected Polish exchange rates

Pajor¹

2020

Śląski Przegląd Statystyczny

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The aim of the paper is to compare the forecasting potentials of two classes of Multiplicative Stochastic Factorscalar BEKK (MSF-SBEKK) models which differ in the type of latent process. In the first class, the innovations of a first order autoregressive structure for the natural logarithm of latent variables are log-normal, whereas in the second class the innovations are inverted gamma distributed. The comparison of the models' forecasting abilities by means of the predictive Bayes factor as well as the log predictive score and energy score were performed based on the Polish exchange rates. The author considered one-to ten-step-ahead predictions during the period beginning from 3 September 2019 and ending on 2 September 2020, which covers the time of the crisis caused by COVID-19. The author concluded that for most of the forecast horizons, the considered log-normal innovations outperformed the inverted gamma ones.

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VEC-MSF models in Bayesian analysis of short- and long-run relationships

Cited by 5 publications

References 25 publications

Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships

Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships

New Estimators of the Bayes Factor for Models with High-Dimensional Parameter and/or Latent Variable Spaces

Inverted gamma vs. log-normal innovations in MSFSBEKK models in the forecasting of selected Polish exchange rates

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