Risk modeling in crude oil market: a comparison of Markov switching and GARCH models

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

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Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

International Journal of Financial Studies ◽

10.3390/ijfs9020030 ◽

2021 ◽

Vol 9 (2) ◽

pp. 30

Author(s):

John Weirstrass Muteba Mwamba ◽

Sutene Mwambetania Mwambi

Keyword(s):

South Africa ◽

Crude Oil ◽

South African ◽

Asset Allocation ◽

Markov Switching ◽

Tail Dependence ◽

Lower Tail ◽

Oil Market ◽

Oil Markets ◽

Vine Copula

This paper investigates the dynamic tail dependence risk between BRICS economies and the world energy market, in the context of the COVID-19 financial crisis of 2020, in order to determine optimal investment decisions based on risk metrics. For this purpose, we employ a combination of novel statistical techniques, including Vector Autoregressive (VAR), Markov-switching GJR-GARCH, and vine copula methods. Using a data set consisting of daily stock and world crude oil prices, we find evidence of a structure break in the volatility process, consisting of high and low persistence volatility processes, with a high persistence in the probabilities of transition between lower and higher volatility regimes, as well as the presence of leverage effects. Furthermore, our results based on the C-vine copula confirm the existence of two types of tail dependence: symmetric tail dependence between South Africa and China, South Africa and Russia, and South Africa and India, and asymmetric lower tail dependence between South Africa and Brazil, and South Africa and crude oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using raw returns, MS GARCH returns, and C-vine and R-vine copula-based returns, and optimize it using a Particle Swarm optimization algorithm with a rebalancing strategy. The results demonstrate an inverse relationship between the risk contribution and asset allocation of South Africa and the crude oil market, supporting the existence of a lower tail dependence between them. This suggests that, when South African stocks are in distress, investors tend to shift their holdings in the oil market. Similar results are found between Russia and crude oil, as well as Brazil and crude oil. In the symmetric tail, South African asset allocation is found to have a well-diversified relationship with that of China, Russia, and India, suggesting that these three markets might be good investment destinations when things are not good in South Africa, and vice versa.

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A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

Energies ◽

10.3390/en13010129 ◽

2019 ◽

Vol 13 (1) ◽

pp. 129 ◽

Cited By ~ 3

Author(s):

Oscar V. De la Torre-Torres ◽

Evaristo Galeana-Figueroa ◽

José Álvarez-García

Keyword(s):

Natural Gas ◽

Markov Switching ◽

Gaussian Model ◽

Garch Models ◽

Trading Rule ◽

Treasury Bills ◽

Oil Market ◽

Oil And Natural Gas

In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.

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Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Studies in Economics and Finance ◽

10.1108/sef-09-2021-0352 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mohammadreza Mahmoudi ◽

Hana Ghaneei

Keyword(s):

Stock Market ◽

Crude Oil ◽

Stock Return ◽

Stock Exchange ◽

Markov Switching ◽

Stock Index ◽

Var Model ◽

Content Type ◽

Oil Market ◽

The Impact

Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

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Forecasting crude oil market volatility: A Markov switching multifractal volatility approach

International Journal of Forecasting ◽

10.1016/j.ijforecast.2015.02.006 ◽

2016 ◽

Vol 32 (1) ◽

pp. 1-9 ◽

Cited By ~ 45

Author(s):

Yudong Wang ◽

Chongfeng Wu ◽

Li Yang

Keyword(s):

Crude Oil ◽

Markov Switching ◽

Market Volatility ◽

Oil Market

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Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?

International Review of Economics & Finance ◽

10.1016/j.iref.2018.09.006 ◽

2019 ◽

Vol 59 ◽

pp. 302-317 ◽

Cited By ~ 26

Author(s):

Yue-Jun Zhang ◽

Ting Yao ◽

Ling-Yun He ◽

Ronald Ripple

Keyword(s):

Crude Oil ◽

Regime Switching ◽

Garch Model ◽

Garch Models ◽

Volatility Forecasting ◽

Oil Market

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Markov Switching Constant Conditional Correlation GARCH Models for Hedging on Gold and Crude Oil

Structural Changes and their Econometric Modeling - Studies in Computational Intelligence ◽

10.1007/978-3-030-04263-9_36 ◽

2018 ◽

pp. 463-473

Author(s):

Noppasit Chakpitak ◽

Pichayakone Rakpho ◽

Woraphon Yamaka

Keyword(s):

Crude Oil ◽

Markov Switching ◽

Garch Models ◽

Conditional Correlation

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Contagion of COVID-19 pandemic between oil and financial assets: the evidence of multivariate Markov switching GARCH models

Journal of Investment Compliance ◽

10.1108/joic-01-2021-0001 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Achraf Ghorbel ◽

Ahmed Jeribi

Keyword(s):

Crude Oil ◽

Markov Switching ◽

Stock Index ◽

Garch Models ◽

Financial Assets ◽

Volatility Spillover ◽

Safe Haven ◽

Content Type ◽

Time Frequency ◽

Stock Indexes

Purpose In this paper, we investigate empirically the time-frequency co-movement between the recent COVID-19 pandemic, G7stock markets, gold, crude oil price (WTI) and cryptocurrency markets (bitcoin) using both the multivariate MSGARCH models. Design/methodology/approach This paper examines the relationship between the volatilities of oil, Chinese stock index and financial assets (cryptocurrency, gold, and G7 stock indexes), for the period January 17th 2020 to December 10th 2020. It tests the presence of regime changes in the GARCH volatility dynamics of bitcoin, gold, Chinese, and G7 stock indexes as well as oil prices by using Markov–Switching GARCH model. Also, the paper estimates the dynamic correlation and volatility spillover between oil, Chinese and financial assets by using the MSBEKK-GARCH and MSDCC-GARCH models. Findings Overall, we find that all variables display a strong volatility concentrated in the first four months of Covid-19 outbreak. The paper conducts different backtesting procedures of the 1% and 5% Value-at-Risk forecasts of risk. The results find that gold has the lowest VaR. However, the Canadian and American indices have the highest VaR, for respectively 1% and 5% confidence level. The estimation results of MSBEKK-GARCH prove the volatility spillover between Chinese index, oil and financial assets. Although, the past news about shocks in the Chinese index significantly affects the current conditional volatility of financial assets. Moreover, for the high regime, the correlation increased between Chinese and G7 stock indexes which proving the contagion effect of the COVID-19 pandemic. On the contrary, the correlation decreased between Chinese-gold and Chinese-bitcoin, which confirming that gold and bitcoin can be considered as an alternative hedge for some investors during a crisis. During the COVID-19 pandemic, the correlations for the couples oil-gold and oil-bitcoin peaked. Contrary to gold, bitcoin cannot be considered as a safe haven during the global pandemic when investing in crude oil. Originality/value In contrast, comparative analysis in terms of responses to US COVID-19 pandemic, the US Covid-19 confirmed cases have relative higher impact on the co-movement in WTI and bitcoin. This paper confirms that gold is a safe haven during the COVID19 pandemic period.

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