Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility

Dimitrakopoulos, Stefanos

doi:10.1016/j.econlet.2016.10.035

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…In this case, these parameters would conform to a non-stationary posterior distributions which has not been addressed in this paper. Some approaches dealing with this type of problems for finance applications can be found in references [196,197]. In addition to this, the recorded data themselves may not necessarily be independently identically distributed, as it was assumed in this paper.…”

Section: Discussionmentioning

confidence: 99%

Sampling methods for solving Bayesian model updating problems: A tutorial

Lye

Cicirello

Patelli

2021

Mechanical Systems and Signal Processing

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This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the context of Bayesian model updating for engineering applications. Markov Chain Monte Carlo, Transitional Markov Chain Monte Carlo, and Sequential Monte Carlo methods are introduced, applied to different case studies and finally their performance is compared. For each of these methods, numerical implementations and their settings are provided.Three case studies with increased complexity and challenges are presented showing the advantages and limitations of each of the sampling techniques under review. The first case study presents the parameter identification for a spring-mass system under a static load. The second case study presents a 2-dimensional bi-modal posterior distribution and the aim is to observe the performance of each of these sampling techniques in sampling from such distribution. Finally, the last case study presents the stochastic identification of the model parameters of a complex and non-linear numerical model based on experimental data.The case studies presented in this paper consider the recorded data set as a single piece of information which is used to make inferences and estimations on time-invariant model parameters.

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Section: Discussionmentioning

confidence: 99%

Sampling methods for solving Bayesian model updating problems: A tutorial

Lye

Cicirello

Patelli

2021

Mechanical Systems and Signal Processing

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“…Güncel uluslararası finans literatüründe çeşitli finansal varlıkların volatilite dinamiklerinin stokastik volatilite modelleri ile incelenmesinin oldukça ilgi gördüğü bilinmektedir. (Örneğin, bakınız: Wang, 2011;Larsson ve Nossman, 2011;Ishihara ve Omori, 2012;Pan ve Li, 2013;Shirota, Hizu ve Omori, 2014;Jensen ve Maheu, 2014;Dimitrakopoulos, 2017;Lafosse ve Rodriguez, 2018). Ayrıca, oldukça sınırlı sayıda olmakla birlikte ulusal yazında da stokastik volatilite modellerine dayalı çalışmalar bulunmaktadır.…”

Section: Introductionunclassified

Genelleştirilmiş Hiperbolik Çarpık Student t Dağılım Varsayımına Dayalı Asimetrik Stokastik Volatilite Modelinin Türk Döviz Piyasasına Uygulanması

Büberkökü

2021

Muhasebe Ve Finansman Dergisi

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Bu çalışmada genelleştirilmiş hiperbolik çarpık student t dağılım varsayımına dayalı asimetrik stokastik volatilite modeli Bayesyen yaklaşımına dayalı MCMC (Markov Chain Monte Carlo, MCMC) algoritması kullanılarak Dolar-TL ve Euro-TL kurlarına uygulanmıştır. Çalışma bulguları bu güncel modelinin Türk döviz piyasalarına etkin bir şekilde uygulanabileceğine işaret etmektedir. Bulgular, yüksek volatilite kalıcılığının ve asimetrik tepkinin her iki döviz kuru için de geçerli olduğu ve Dolar-TL volatilitesinin öngörülebilirliğinin Euro-TL volatilitesine göre daha zor olduğunu göstermektedir. Modelin sunduğu stokastik volatilite değerlerine bağlı olarak hesaplanan VaR (Value-at-Risk) ve beklenen kayıp (Expected shortfall, ES) değerleri de Dolar-TL kurunun piyasa riskinin Euro-TL kuruna göre daha yüksek olduğuna işaret etmektedir.

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“…Superstatistics has been successfully applied to a variety of financial 4 – 6 , environmental 7 – 9 , social 10 , 11 , and biological systems 12 . Moreover, current superstatistical methods can determine not only how frequently certain parameter values are realized but can also pin-point when parameter values change 13 – 16 . However, current methods lack the ability to objectively compare different time-varying parameter models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model selection for complex dynamic systems

et al. 2018

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Time series generated by complex systems like financial markets and the earth’s atmosphere often represent superstatistical random walks: on short time scales, the data follow a simple low-level model, but the model parameters are not constant and can fluctuate on longer time scales according to a high-level model. While the low-level model is often dictated by the type of the data, the high-level model, which describes how the parameters change, is unknown in most cases. Here we present a computationally efficient method to infer the time course of the parameter variations from time-series with short-range correlations. Importantly, this method evaluates the model evidence to objectively select between competing high-level models. We apply this method to detect anomalous price movements in financial markets, characterize cancer cell invasiveness, identify historical policies relevant for working safety in coal mines, and compare different climate change scenarios to forecast global warming.

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Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility

Abstract: This is a repository copy of Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility.

Cited by 5 publications

References 30 publications

Sampling methods for solving Bayesian model updating problems: A tutorial

Sampling methods for solving Bayesian model updating problems: A tutorial

Genelleştirilmiş Hiperbolik Çarpık Student t Dağılım Varsayımına Dayalı Asimetrik Stokastik Volatilite Modelinin Türk Döviz Piyasasına Uygulanması

Bayesian model selection for complex dynamic systems

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