Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers.     

Oil price and Gulf Corporation Council stock indices: New evidence from time-varying copula models

Using a time-varying GJR copula approach, we determine the conditional dependence of the GCC stock indices on oil price between 2007 and 2016. We show how to improve the forecasting accuracy of the co-movement of energy and stock prices in an equally weighted portfolio. Contrary to prior findings, we demonstrate that due to the different co-movements across the GCC stock indices, portfolios of oil assets and several GCC stocks are less likely to be affected by systemic risk. The different co-movements across several stock indices over time provide different entry and exit points for stock investors. This approach is in line with the ‘buy low/sell high’ adage.     

Extreme risk spillovers between crude palm oil prices and exchange rates

This study is the first attempt to examine the extreme risk spillovers between Malaysian crude palm oil (CPO) and foreign exchange currencies of the three largest CPO importers: India, the European Union and China throughout the global financial crisis. Using daily data of three currencies, CPO spot and futures from 2000 to 2018, our results show: First, before the crisis, the unexpected change in foreign exchange rates is the primary driver of risk spillover to the CPO market. Second, during the crisis, the extreme movement of CPO spot returns is dominant in the Malaysian exchange rates relative to the euro. Third, after the crisis, the spillover flows from the CPO market to the foreign exchange market. Overall, our findings show the importance of CPO pricing dynamics in mitigating foreign exchange risk over the crisis period. This paper contributes to the extant literature by recognizing the effect of risk spillover on the targeted foreign exchange rate for portfolio allocation.     

The effects of oil price shocks on inflation in the G7 countries

This study examines the effects of three types of oil price shocks on inflation in the G7 countries with a new method of isolating oil price shocks. Based on monthly data from January 1997 to January 2019, we find that each oil price shock has the largest effect on U.S. inflation among the G7 countries and each country’s response to oil price shocks is different. Moreover, a rolling-window analysis shows that supply shocks, demand shocks and risk shocks have dynamic effects on inflation. The effect of supply shocks on inflation is strong before the financial crisis, but weakens during the crisis. However, the effect of demand shocks increases sharply in this time. The effect of risk shocks mainly occurs during the financial crisis and the European debt crisis. In addition, this study uses two ways to verify the robustness of the results. Our empirical results have important implications for policymakers and manufacturers, since the results provide a good explanation for the response of inflation in the G7 countries to the oil price shocks from different sources.     

What determines the long-term correlation between oil prices and exchange rates?

In this study, we obtain the long-term correlation between oil prices and exchange rates by employing the dynamic conditional correlation-mixed data sampling (DCC-MIDAS) model. We then identify the factors that influence the long-term correlation using panel data analysis. We find that the long-run correlations between oil prices and exchange rates are negative for all oil-exchange rate markets except Japan. We also find that both inflation and term spread have negative effects, while the risk-free interest rate has a positive effect on the long-term correlation between oil prices and exchange rates. Importantly, the empirical results show that an increase in inflation will significantly damage the real value of the currency itself.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.     

The non-linear impact of oil price on the oil demand

ABSTRACT

A panel smooth transition regression model was adopted to analyse the non-linear impact of oil prices on oil demand. Data for 42 countries was obtained from the International Energy Agency for the time period spanning from January 1990 to June 2017. The results indicate that a threshold value does exist. Furthermore, when the oil price was lower than this threshold value, a positive relationship between oil price and oil demand was observed. When the price of oil was higher than the threshold value, however, a negative relationship between price and demand was found.     

A study of the efficiency of the Chinese clean energy stock market and its correlation with the crude oil market based on an asymmetric multifractal scaling behavior analysis

We examine the multifractal scaling behavior and market efficiency of China’s clean energy stock indexes using an asymmetric multifractal detrended fluctuation analysis (A-MFDFA) and then investigate the tail correlation between this index and the crude oil market via an asymmetric multifractal detrended cross-correlation analysis (A-MFDCCA). First, we reveal that the overall, upward and downward trends of the clean energy stock indexes all have significant multifractal characteristics. The clean energy stock market is far from efficient regardless of whether the fluctuations are small or large. In addition, both upward and downward fluctuations exhibit considerable asymmetry. The significant gap between the downward and overall trends indicates that the downward trend following small-scale fluctuations implies weaker efficiency for investors. Furthermore,based on the sliding market deficiency measure (MDM),we find that the change in efficiency in the three trends significantly depends on the length of the window. In the short term, there is no significant efficiency difference among these three trends; however, in the long term, the asymmetry in the upward and downward trends has gradually increased,especially after December 2018. The results demonstrate that bear markets can offer considerably more opportunities for obtaining excess profits. Finally, we reveal that the cross-correlation between the trends of crude oil prices and low-carbon indexes exhibits significant multifractal characteristics. When the crude oil market is in a bull market or the low-carbon energy market is in a bear market, especially in a larger-scale fluctuation, investors should pay attention to the long-term influence of the counterparty market and carry out a hedging operation to avoid risks.     

Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.     

Forward-looking agents and inflation in an oil-producing country: Evidence from Iran

A monetary model of inflation was estimated on the oil-producing country of Iran for the period 1984:1–2016:4. It was found that expectations are formed rationally and that agents are forward-looking and adjust their behavior based on changes in government expenditure. Consequently, it was found that higher fiscal variables result in lower price levels over the long run. A higher oil price leads to a lower price in the country but to a higher money supply and interest rate over the long run. Furthermore, a higher domestic interest rate results in a higher price level, while the reverse is true for a higher foreign interest rate. Another cause of inflation in Iran is the foreign price level. It was found that over the short run a higher growth of the real government expenditure results in a lower inflation rate in the country but a positive change in the foreign interest rate brings in a higher inflation rate.