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

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.

Download Full-text

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.

Download Full-text

Forecasting crude-oil market volatility: Further evidence with jumps

Energy Economics ◽

10.1016/j.eneco.2017.09.002 ◽

2017 ◽

Vol 67 ◽

pp. 508-519 ◽

Cited By ~ 18

Author(s):

Amélie Charles ◽

Olivier Darné

Keyword(s):

Crude Oil ◽

Market Volatility ◽

Oil Market

Download Full-text

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.

Download Full-text