<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Aim</h3>
<p>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).</p>
</sec>
<sec><h3>Methodology</h3>
<p>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.</p>
</sec>
<sec><h3>Results</h3>
<p>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.</p>
<p>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).</p>
</sec>
<sec><h3>Originality/Value</h3>
<p>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.</p>
</sec>
</abstract>

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.

Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering

Econometrics

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Aim
The paper aims to propose a new estimation method for the Cholesky Multivariate Stochastic Volatility Model based on the iterated filtering algorithm...

Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering

Data publikacji: 13 mar 2024

Zakres stron: 44 - 58

DOI: https://doi.org/10.15611/eada.2023.4.04

Słowa kluczowe
multivariate stochastic volatility, iterated filtering, particle filters, the Cholesky Multivariate Stochastic Volatility (ChMSV) Model

© 2023 Piotr Szczepocki, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering

Data publikacji: 13 mar 2024

Zakres stron: 44 - 58

DOI: https://doi.org/10.15611/eada.2023.4.04

Słowa kluczowemultivariate stochastic volatility, iterated filtering, particle filters, the Cholesky Multivariate Stochastic Volatility (ChMSV) Model

© 2023 Piotr Szczepocki, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Słowa kluczowe
multivariate stochastic volatility, iterated filtering, particle filters, the Cholesky Multivariate Stochastic Volatility (ChMSV) Model