Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities

This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for...

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Autor principal:	Per Bjarte Solibakke
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	energy forecasting volatility Markov Chain Monte Carlo (MCMC) simulations projection-reprojection stochastic volatility models Risk in industry. Risk management HD61 Finance HG1-9999
Acceso en línea:	https://doaj.org/article/2e5645d8a35c4382a183ba32b496c97a
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spelling	oai:doaj.org-article:2e5645d8a35c4382a183ba32b496c97a2021-11-25T18:08:25ZForecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities10.3390/jrfm141105101911-80741911-8066https://doaj.org/article/2e5645d8a35c4382a183ba32b496c97a2021-10-01T00:00:00Zhttps://www.mdpi.com/1911-8074/14/11/510https://doaj.org/toc/1911-8066https://doaj.org/toc/1911-8074This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The realization establishes a functional form of the conditional distributions, which are evaluated on observed data giving the conditional mean function for the volatility factors at the data points (nonlinear Kalman filter). For both Brent and WTI oil contracts, the first factor is a slow-moving persistent factor while the second factor is a fast-moving immediate mean reverting factor. The negative correlation between the mean and volatility suggests higher volatilities from negative price movements. The results indicate that holding volatility as an asset of its own is insurance against market crashes as well as being an excellent diversification instrument. Furthermore, the volatility data dependence is strong, indicating predictability. Hence, using the Kalman filter from a realization of an optimal multifactor SV model visualizes the latent step ahead volatility paths, and the data dependence gives access to accurate static forecasts. The results extend market transparency and make it easier to implement risk management including derivative trading (including swaps).Per Bjarte SolibakkeMDPI AGarticleenergyforecasting volatilityMarkov Chain Monte Carlo (MCMC) simulationsprojection-reprojectionstochastic volatility modelsRisk in industry. Risk managementHD61FinanceHG1-9999ENJournal of Risk and Financial Management, Vol 14, Iss 510, p 510 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	energy forecasting volatility Markov Chain Monte Carlo (MCMC) simulations projection-reprojection stochastic volatility models Risk in industry. Risk management HD61 Finance HG1-9999
spellingShingle	energy forecasting volatility Markov Chain Monte Carlo (MCMC) simulations projection-reprojection stochastic volatility models Risk in industry. Risk management HD61 Finance HG1-9999 Per Bjarte Solibakke Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities
description	This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The realization establishes a functional form of the conditional distributions, which are evaluated on observed data giving the conditional mean function for the volatility factors at the data points (nonlinear Kalman filter). For both Brent and WTI oil contracts, the first factor is a slow-moving persistent factor while the second factor is a fast-moving immediate mean reverting factor. The negative correlation between the mean and volatility suggests higher volatilities from negative price movements. The results indicate that holding volatility as an asset of its own is insurance against market crashes as well as being an excellent diversification instrument. Furthermore, the volatility data dependence is strong, indicating predictability. Hence, using the Kalman filter from a realization of an optimal multifactor SV model visualizes the latent step ahead volatility paths, and the data dependence gives access to accurate static forecasts. The results extend market transparency and make it easier to implement risk management including derivative trading (including swaps).
format	article
author	Per Bjarte Solibakke
author_facet	Per Bjarte Solibakke
author_sort	Per Bjarte Solibakke
title	Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities
title_short	Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities
title_full	Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities
title_fullStr	Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities
title_full_unstemmed	Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities
title_sort	forecasting stochastic volatility characteristics for the financial fossil oil market densities
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2e5645d8a35c4382a183ba32b496c97a
work_keys_str_mv	AT perbjartesolibakke forecastingstochasticvolatilitycharacteristicsforthefinancialfossiloilmarketdensities
_version_	1718411550345134080

Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities

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