Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

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.

scholarly journals Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

2021 ◽  
pp. 1-21
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Inflation Rate ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Impulse Response Functions ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Long Run ◽  
The Us

Abstract Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

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

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.

scholarly journals Modeling the Relationship between Crude Oil and Agricultural Commodity Prices

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1344 ◽  
Author(s):  
Duc Hong Vo ◽  
Tan Ngoc Vu ◽  
Anh The Vo ◽  
Michael McAleer
Keyword(s):  
Crude Oil ◽  
Global Financial Crisis ◽  
Aggregate Demand ◽  
Commodity Prices ◽  
Impulse Response Functions ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Crude Oil Prices ◽  
Oil Markets

The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

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

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.

scholarly journals Effects of crude oil prices on copper and maize prices

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Byrne Kaulu
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Commodity Price ◽  
Policy And Practice ◽  
Significance Level ◽  
Vector Autoregressive ◽  
Crude Oil Prices ◽  
Long Run ◽  
The One ◽  
Vector Error Correction Models

AbstractThis study explains the effects of crude oil prices on copper and maize prices. Vector autoregressive and vector error correction models are used to study the relationship between oil prices and prices of copper and maize. The commodity price data used consist of average monthly prices of each of the commodities: crude oil, copper and maize for the months January 1982 to June 2021. For robustness, the analysis was also run on a sample of the same data for the period January 2000 to June 2021. A long-run relationship was found between crude oil and copper prices on the one hand and maize prices on the other for the 1982 to 2021 period at the 5% significance level. The same was not true for the shorter sample (2000 to 2021). Granger causality flowing from crude oil prices alone to copper and maize prices was not found. Recommendations that are useful for energy, mining, agriculture and general development policy and practice are made. The findings are also useful for bilateral and multilateral aid discussions. The limitations of the study and recommendations for future scholarship are also made.

scholarly journals Macroeconomic Switching Regimes and Monetary Policy in Canada

2017 ◽  
Vol 4 (4) ◽  
pp. 17 ◽  
Author(s):  
Ronald Henry Lange
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
Inflation Targeting ◽  
Markov Switching ◽  
Output Gap ◽  
Impulse Response Functions ◽  
Switching Regimes ◽  
Long Run ◽  
Nominal Anchor ◽  
Policy Multipliers

This study examines the behaviour of monetary policy in Canada over the last 40 years using a Markov-switching VAR model of the macroeconomy. The Markov-switching estimates capture three continuous regimes that are interpreted as the ‘surprise’ regime from 1972Q1 to 1982Q2, the ‘recovery’ regime from 1982Q3 to 1991Q3 and the ‘target’ regime from 1991Q4 to 2014Q4. Monetary policy multipliers for the output gap are greater than one for all three regimes, suggesting that the central bank does not accommodate any expected changes in inflation over the long-run due to the domestic relationship between the output gap and future inflation. The long-run multipliers for inflation are equal to one in the surprise and recovery regimes, indicating that monetary policy also responds to offset inflation shocks. Overall, the policy multipliers and impulse response functions indicate a proactive central bank that responds systematically to movements in the output gap in order to control expected future inflation and to inflation surprises in the three regimes. The regime-dependent behaviour of monetary policy indicates a central bank pursuing an implicit form of inflation targeting as a means of achieving a nominal anchor for policy. The implicit inflation tar­gets are consistent with historical episodes of inflation in Canada over the past 40 years.

scholarly journals Modeling Nonlinear Granger Causality and Co-Integration Between Gold Price Returns and Crude Oil Price Returns

2018 ◽  
Vol 14 (2) ◽  
pp. 105-116
Author(s):  
Nawaz Ahmad ◽  
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
Oil Prices ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Long Run ◽  
Gold Market ◽  
Oil Markets ◽  
Causality Tests ◽  
Multiplicative Effects

To model the nonlinear analysis of commodities, Gold market and crude oil market have importance to test their lead and lag price mechanism between the two. For this purpose, the log transformation has been done to calculate easier multiplicative effects. However, to record the dynamic effects of long run cointegreation model applied and tested to find the significance of the problem statement issues. Furthermore, granger causality approach also uses to examine the fundamental linkages between Gold Prices and Crude Oil prices. Meanwhile, the study of Gold markets and oil markets gained popularity among development economists during in last some decades. And try to find out stochastic relationship between the two nonlinear markets. The academic practitioners paved their efforts to run casual time series models in order to find out the robust results which help the economists and financial experts to drive the industry indicator in positive way. This study confirmed that there is cointegration between the two important indicators of large market commodities i.e Gold and crude oil and also casual interactions. Pairwise Granger Causality Tests concluded that Gold Prices return has Granger Cause on Oil Prices return in the long run and if the βeta change in the prices of gold may affect on the prices of crude oil in the long run.

Structural Vector Autoregressive Models

2020 ◽  
Author(s):  
Luca Gambetti
Keyword(s):  
Linear Functions ◽  
Impulse Response Functions ◽  
Vector Autoregressions ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Varying Coefficients ◽  
Macroeconomic Analysis ◽  
Long Run ◽  
Time Varying Coefficients ◽  
Structural Vector

Structural vector autoregressions (SVARs) represent a prominent class of time series models used for macroeconomic analysis. The model consists of a set of multivariate linear autoregressive equations characterizing the joint dynamics of economic variables. The residuals of these equations are combinations of the underlying structural economic shocks, assumed to be orthogonal to each other. Using a minimal set of restrictions, these relations can be estimated—the so-called shock identification—and the variables can be expressed as linear functions of current and past structural shocks. The coefficients of these equations, called impulse response functions, represent the dynamic response of model variables to shocks. Several ways of identifying structural shocks have been proposed in the literature: short-run restrictions, long-run restrictions, and sign restrictions, to mention a few. SVAR models have been extensively employed to study the transmission mechanisms of macroeconomic shocks and test economic theories. Special attention has been paid to monetary and fiscal policy shocks as well as other nonpolicy shocks like technology and financial shocks. In recent years, many advances have been made both in terms of theory and empirical strategies. Several works have contributed to extend the standard model in order to incorporate new features like large information sets, nonlinearities, and time-varying coefficients. New strategies to identify structural shocks have been designed, and new methods to do inference have been introduced.

Study of Short-Run Grain Movements on the Inland Waterway System

2005 ◽  
Vol 1909 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Mark A. Thoma ◽  
Wesley W. Wilson
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Important Variable ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
Planning Models ◽  
Long Run ◽  
Short Run ◽  
Variance Decompositions

Time series techniques—particularly impulse–response functions and variance decompositions—are used to characterize the short-run relationships between 17 variables in a vector autoregressive model designed to trace the short-run interconnections among variables affecting lockages on the Mississippi and Illinois Rivers. The model contains five categories of variables: lockages, barge rates, grain bids, rail rates, and rail deliveries. Variance decompositions are constructed that identify barge rates as the most important variable affecting lockages at both short and long horizons. Barge rates are, in turn, explained largely by lockages and rail rates, indicating two-way feedback or bidirectional causality between lockages and barge rates. Impulse–response functions are also examined. The variance decompositions indicate that barge rates are important in explaining lockages, and the impulse–response functions show how lockages and other variables respond to such shocks. In general, there is a substitution away from barge transportation and toward rail transportation when barge rates increase. The results are useful for illuminating the causal relationships among variables in the model and for understanding behavioral relationships present in the data and can be used to guide short- and long-run planning models. For example, many planning models assume that barge traffic does not respond significantly to changes in barge rates; however, results obtained here imply that barge traffic and rail deliveries do respond to such changes. This potentially important implication illustrates the usefulness of the time series techniques used.

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