Regime switching model of US crude oil and stock market prices: 1859 to 2013

Balcılar, Mehmet; Gupta, Rangan; Miller, Stephen M.

doi:10.1016/j.eneco.2015.01.026

Cited by 144 publications

(83 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the period from 1995 to 2011, they find that oil futures prices predict the S&P 500 sub-group index but not vice versa. Further, Balcilar et al (2015) use a Markov-switching model with two regimes, a low and high volatility regime, in a VECM setting. They examine the impact of oil price shocks on the S&P 500 index for monthly data from 1859 to 2013.…”

Section: Theoretical Considerations and Literature Reviewmentioning

confidence: 99%

The impact of oil shocks on exchange rates: A Markov-switching approach

2016

View full text Add to dashboard Cite

This paper uses Markov-switching models to investigate the impact of oil shocks on real exchange rates for a sample of oil exporting and oil importing countries. This is an important topic to study because an oil shock can affect a country's terms of trade which can affect its competitiveness. We detect significant exchange rate appreciation pressures in oil exporting economies after oil demand shocks. We find limited evidence that oil supply shocks affect exchange rates. Global economic demand shocks affect exchange rates in both oil exporting and importing countries, though there is no systematic pattern of appreciating and depreciating real exchange rates. The results lend support to the presence of regime switching for the effects of oil shocks on real exchange rates.JEL Classification: F31, G15, Q43

show abstract

Section: Theoretical Considerations and Literature Reviewmentioning

confidence: 99%

The impact of oil shocks on exchange rates: A Markov-switching approach

2016

View full text Add to dashboard Cite

show abstract

“…16 The details of the MCMC implementation is omitted to save space. We refer the interested reader to Balcilar et al (2015) as our implementation is analogues to the MCMC procedure used there. 17 Further details can be found in Balcilar et al (2015).…”

Section: Linear Vecmmentioning

confidence: 99%

“…This may be explained by not having too high oil prices on average to force factor substitution. In contrast to the two real economic variables, both RIR and OIL are not correcting to the equilibrium in the long run in this linear model, 15 We modify the MCMC method of Balcilar et al (2015) and introduce a rejection sampling scheme where the draws that violate the restriction that second regime has greater variance than the first regime for all equations are rejected. 16 The details of the MCMC implementation is omitted to save space.…”

Section: Linear Vecmmentioning

confidence: 99%

“…Two approaches arose in the literature as a solution to the history-dependence problem of the IRFs in the MS-VECM. For this article, we use the approach of Ehrmann et al (2003) for RDIRF and combine it with the Bayesian MCMC integration (Balcilar et al, 2015).…”

Section: Impulse Response Analysis For the Ms-vecmmentioning

confidence: 99%

“…It may not be possible to compute the full vector of likelihoods for each regime in each period with the ML and EM methods in certain cases. To avoid this problem, the MCMC works only with one sample path for the regimes rather than a weighted average of sample paths over all regimes (for more information, see Balcilar et al, 2015). 15 We perform the MCMC integration with 50 000 posterior draws with a 20 000 burn-in draws.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Interactions between real economic and financial sides of the US economy in a regime-switching environment

2015

Self Cite

View full text Add to dashboard Cite

This objective of this study is to examine the linkages between real (economic) and financial variables in the United States in a regime-switching environment that accounts explicitly for high volatility in the stock market and high stress in financial markets. Since the linearity test shows that the linear model should be rejected, we employ the Markov-switching VECM to examine the same objective using the Bayesian Markov-chain Monte Carlo method. The regime-dependent impulse response function (RDIRF) highlights the increasing importance of the financial sector of the economy during stress periods. The responses and their fluctuations are significantly greater in the high-volatility regime than in the low-volatility regime.

show abstract

Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data

Nonejad¹

2020

Journal of Forecasting

View full text Add to dashboard Cite

Apart from the percentage change in the price of crude oil, there is a growing tradition of using various nonlinear transformations of the price of crude oil to forecast real gross domestic product growth rates, equity returns, inflation and other macroeconomic variables. This study attempts to quantify the additional potential predictive power afforded by crude oil price volatility relative to widely used crude oil price-based variables for more than 300 US macroeconomic time series at the monthly and the quarterly sampling frequency. We observe that regressions employing crude oil price realized volatility and crude oil price realized semivolatilities tend to afford a more consistent pattern of out-of-sample prediction gains relative to competitors using well-known crude oil price measures and the autoregressive benchmark at the quarterly and monthly sampling frequency. While it is somewhat harder to find evidence of finite-sample predictive gains relative to the benchmark, the evidence is stronger with respect to population-level predictability 1 quarter (1 month) ahead for the model with crude oil price realized semivolatilities across the considered data and models. Furthermore, point (density) forecasts employing crude oil price realized volatility tend to be more accurate than corresponding forecasts produced under the crude oil price-based predictive regressions in a horse race.

show abstract

Regime switching model of US crude oil and stock market prices: 1859 to 2013

Abstract: Abstract:This paper examines the relationship between US crude oil and stock market prices, using a Markov-Switching vector error-correction model and a monthly data set from 1859 to 2013.

Cited by 144 publications

References 85 publications

The impact of oil shocks on exchange rates: A Markov-switching approach

The impact of oil shocks on exchange rates: A Markov-switching approach

Interactions between real economic and financial sides of the US economy in a regime-switching environment

Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data

Contact Info

Product

Resources

About