Asymmetric multifractal cross-correlations and time varying features between Latin-American stock market indices and crude oil market

2017 ◽  
Vol 104 ◽  
pp. 121-128 ◽  
Author(s):  
Gabriel Gajardo ◽  
Werner Kristjanpoller
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Latin American ◽  
Time Varying ◽  
Oil Market ◽  
Cross Correlations

scholarly journals Oil Price Forecasting Using a Time-Varying Approach

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Crude Oil ◽  
Time Window ◽  
Oil Prices ◽  
Oil Price ◽  
Price Forecasting ◽  
Time Varying ◽  
Crude Oil Price ◽  
Trend Prediction ◽  
Oil Market ◽  
Polynomial Decomposition

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

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.

New results on the predictive value of crude oil for US stock returns

2018 ◽  
Vol 35 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Matt Brigida
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
State Space ◽  
Risk Premia ◽  
Time Varying ◽  
Content Type ◽  
Varying Coefficients ◽  
The Us ◽  
The Relationship ◽  
Predictive Relationship

Purpose The purpose of this study is to clarify the nature of the predictive relationship between crude oil and the US stock market, with particular attention to whether this relationship is driven by time-varying risk premia. Design/methodology/approach The authors formulate the predictive regression as a state-space model and estimate the time-varying coefficients via the Kalman filter and prediction-error decomposition. Findings The authors find that the nature of the predictive relationship between crude oil and the US stock market changed in the latter half of 2008. After mid-2008, the predictive relationship switched signs and exhibited characteristics which make it much more likely that the predictive relationship is due to time-varying risk premia rather than a market inefficiency. Originality/value The authors apply a state-space approach to modeling the predictive relationship. This allows one to watch the evolution of the predictive relationship over time. In particular, the authors identify a dramatic shift in the relationship around August 2008. Prior research has not been able to identify shifts in the relationship.

Sign in / Sign up

Export Citation Format

Share Document