Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process

Szczepocki, Piotr

doi:10.21307/stattrans-2020-019

Cited by 3 publications

(5 citation statements)

References 43 publications

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“…Iterated filtering has been successfully applied to many SSMs, mostly in the context of epidemiology (Bhadra et al, 2011;He et al, 2009;King et al, 2008;Stocks et al, 2020;You et al, 2020), but also in economic modelling, especially for univariate SV models (Bretó, 2014;Szczepocki, 2020).…”

Section: Estimation Methodsmentioning

confidence: 99%

“…In practical applications, the convergence of the algorithm is often assessed via diagnostic plots (see e.g. Bretó, 2014;King et al, 2008;Szczepocki, 2020). Iterated filtering uses only a basic bootstrap particle filter (Gordon et al, 1993), and thus it does not have to evaluate the transition density 𝑓𝑓 𝑡𝑡 (𝑥𝑥 𝑡𝑡+1 |𝑥𝑥 𝑡𝑡 ; 𝜃𝜃).…”

Section: Estimation Methodsmentioning

confidence: 99%

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Estimation of Yu and Meyer bivariate stochastic volatility model by iterated filtering

Szczepocki¹

2023

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In financial applications, understanding the asset correlation structure is crucial to tasks such as asset pricing, portfolio optimisation, risk management, and asset allocation. Thus, modelling the volatilities and correlations of multivariate stock market returns is of great importance. This paper proposes the iterated filtering algorithm for estimating the bivariate stochastic volatility model of Yu and Meyer. The iterated filtering method is a frequentist-based approach that utilises particle filters and can be applied to estimating the parameters of non-linear or non-Gaussian state-space models. The paper presents an empirical example that demonstrates the way in which the proposed estimation method might be used to estimate the correlation between the returns of two assets: Standard and Poor’s 500 index and the price of gold in US dollars. This is accompanied by a simulation study that proves the validity of the approach.

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Section: Estimation Methodsmentioning

confidence: 99%

Section: Estimation Methodsmentioning

confidence: 99%

Estimation of Yu and Meyer bivariate stochastic volatility model by iterated filtering

Szczepocki¹

2023

View full text Add to dashboard Cite

show abstract

Section: Estimation Methodsmentioning

confidence: 99%

Section: Estimation Methodsmentioning

confidence: 99%

Untitled

2023

View full text Add to dashboard Cite

In financial applications, understanding the asset correlation structure is crucial to tasks such as asset pricing, portfolio optimisation, risk management, and asset allocation. Thus, modelling the volatilities and correlations of multivariate stock market returns is of great importance.This paper proposes the iterated filtering algorithm for estimating the bivariate stochastic volatility model of Yu and Meyer. The iterated filtering method is a frequentist-based approach that utilises particle filters and can be applied to estimating the parameters of non-linear or non-Gaussian state-space models.The paper presents an empirical example that demonstrates the way in which the proposed estimation method might be used to estimate the correlation between the returns of two assets: Standard and Poor's 500 index and the price of gold in US dollars. This is accompanied by a simulation study that proves the validity of the approach.

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“…Iterated filtering has demonstrated successful applications to various State Space Models, predominantly within the context of epidemiology (Bhadra et al, 2011;He et al, 2009;King et al, 2008;Stocks et al, 2020;You et al, 2020). Additionally, it has been employed in economic modelling, particularly for univariate (Bretó, 2014;Szczepocki, 2020) and bivariate (Szczepocki, 2022) stochastic volatility models.…”

Section: Iterated Filteringmentioning

confidence: 99%

Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering

Szczepocki

2023

Econometrics

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Aim: The paper aims to propose a new estimation method for the Cholesky Multivariate Stochastic Volatility Model based on the iterated filtering algorithm (Ionides et al., 2006, 2015). Methodology: The iterated filtering method is a frequentist-based technique that through multiple repetitions of the filtering process, provides a sequence of iteratively updated parameter estimates that converge towards the maximum likelihood estimate. Results: The effectiveness of the proposed estimation method was shown in an empirical example in which the Cholesky Multivariate Stochastic Volatility Model was used in a study on safe-haven assets of one market index: Standard and Poor’s 500 and three safe-haven candidates: gold, Bitcoin and Ethereum. Implications and recommendations: In further research, the iterating filtering method may be used for more advanced multivariate stochastic volatility models that take into account, for example, the leverage effect (as in Ishihara et al., 2016) and heavy-tailed errors (as in Ishihara and Omori, 2012). Originality/Value: The main contribution of the paper is the proposition of a new estimation method for the Cholesky Multivariate Stochastic Volatility Model based on iterated filtering algorithm This is one of the few frequentist-based statistical inference methods for multivariate stochastic volatility models.

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Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process

Cited by 3 publications

References 43 publications

Estimation of Yu and Meyer bivariate stochastic volatility model by iterated filtering

Estimation of Yu and Meyer bivariate stochastic volatility model by iterated filtering

Untitled

Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering

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