On oil-US exchange rate volatility relationships: An intraday analysis (original) (raw)

Exchange rates and oil prices: A multivariate stochastic volatility analysis

The Quarterly Review of Economics and Finance, 2012

This paper uses the multivariate stochastic volatility (MSV) and the multivariate GARCH (MGARCH) models to investigate the volatility interactions between the oil market and the foreign exchange (FX) market, in an attempt to extract information intertwined in the two for better volatility forecast. Our analysis takes into account structural breaks in the data. We find that when the markets are relatively calm (before the 2008 crisis), both oil and FX markets respond to shocks simultaneously and therefore no interaction is detected in daily data. However, during turbulent time, there is bi-directional volatility interaction between the two. In other words, innovations that hit one market also have some impact on the other at a later date and thus using such a dependence significantly improves the forecasting power of volatility models. The MSV models outperform others in fitting the data and forecasting exchange rate volatility. However, the MGARCH models do better job in forecasting oil volatility.

Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects

Resources Policy, 2020

This paper investigates the dynamic correlation and risk transmission between the oil market and the U.S. stock market, using the respective implied volatility indices published by the Chicago Board Options Exchange. The results indicate that, first, there is a significant positive time-varying correlation between oil and stock implied volatility returns. Second, during the global financial crisis, the correlation between oil and stock markets increases significantly. Third, there is a significant bidirectional implied volatility spillover between the oil and stock markets. Insights gleaned from the findings in this study could project energy and monetary policy implications. Monetary and/or energy policy changes could impact the predicted linkage mechanism between these two markets, which can be further leveraged to forecast the market's future volatility.

Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China

Energies, 2019

The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK ...

Effects of Conditional Oil Volatility on Exchange Rate and Stock Markets Returns

International Journal of Energy Economics and Policy

The underlying volatility at a given time is called conditional volatility at this particular time and is modeled by various ARMA-GARCH conditional variance equations (GARCH, EGARCH, GJR, APARCH, IGARCH). How important are oil price fluctuations and oil price volatility in foreign exchange markets and stock markets? What is the nature of the relationship between these three markets? What are the political implications if volatility, using appropriate models to determine, turns out to be important? We evaluate these questions empirically, using the specification of Narayan and Narayan (2010). This specification, in our paper, deals with the determination of volatility appropriate models, based on information criteria, of the ARMA-ARCH family conditional volatility of oil returns using daily data for each country independently (i), and revolve around an analysis of the effect of the volatility of black gold price on the returns of the other two markets in Oil Importing Developed Count...

A closer look into the global determinants of oil price volatility

Energy Economics, 2021

In this paper we investigate global determinants of oil price volatility by employing a time-varying parameter vector autoregressive (TVP-VAR) model. We focus on realised volatility and consider the impact from a set of potential determinants including oil supply, oil demand, oil inventory, financial market uncertainty, financial interbank stress, as well as, financial trends in different currencies. We investigate the impact of these factors on realised volatility utilising monthly data over the period 1990:1-2019:5. Findings show that all factors can be conducive to higher levels of realised oil price volatility particularly in the short run. What can further be noticed, is that the magnitude of the corresponding impulse response functions may differ across time and this could largely be attributed to specific intervals of financial crises and economic recessions. Nevertheless, we show that shocks originating to the financial markets tend to be more important for oil price volatility. Our findings are closely linked to the implications regarding the financialisation of the oil market.

Volatility Spillovers: Evidence On U.S. Oil Product Markets

Journal of Applied Business Research (JABR), 2012

This paper provides evidence on the lead, the contemporaneous and the lagged transmission mechanism of extreme shocks across energy products. Our findings reveal a weak leadership of crude oil in guiding hedgers against risk that is specific to natural gas whose changes show a weak reliance on changes in crude oil. Moreover, our findings are consistent with the competitive use of energy products. It follows that substitutability characterizes the relationship between heating oil and natural gas when extreme standardized shocks are considered.

The connectedness between crude oil and financial markets: Evidence from implied volatility indices

Journal of Commodity Markets, 2016

In this paper we exploit newly introduced implied volatility indexes to investigate the directional risk transfer from oil to US equities, Euro/Dollar exchange rates, precious metals and agricultural commodities. We find significant volatility transmission from oil to equities but little transmission to agricultural commodities. The total pairwise directional connectedness to equities is around 20.4%, while it is only 1.6%, 1.0% and 2.0% to wheat, corn, and soybeans respectively. The risk spillover from oil to precious metals and Euro/Dollar foreign exchange rates is moderate. For instance, the oil market uncertainty spills 11.0%, 11.1% and 8.9% to gold, silver and Euro/Dollar exchange rate respectively. The volatility crossover from all of these markets to oil is tiny, implying that oil is the main driver of its association with these markets. Finally, we provide evidence that the transmission from oil to other markets has increased since the collapse of oil prices in July 2014.

Volatility Spillover Between Oil Prices, Us Dollar Exchange Rates and International Agricultural Commodities Prices

2017

Crude Oil prices are thought to have direct and indirect effect through the exchange rate on the international agricultural commodities prices. The aim of this paper is to examine the interdependence relationship between crude oil futures prices, US dollar exchange rate, and international agricultural commodities prices, including corn (maize), sorghum, wheat, sugar, coconut oil, fishmeal, olive oil, palm oil, groundnut oil, groundnuts, rapeseed oil, soybean meal, soybean oil, soybeans, and sunflower prices. Using autoregressive (AR) model with an exponential generalized autoregressive conditional heteroskedasticity (EGARCH), namely AR-EGARCH model, we describe mean and variance equation in EGARCH model and then extract GARCH variance time series to investigate the volatility spillover from crude oil returns and US dollar exchange rate to the international agricultural commodities returns. To this end, the vector auto-regression (VAR) and vector error correction model (VECM) Granger...

Hourly Oil Price Volatility: The Role of COVID-19

Energy RESEARCH LETTERS, 2020

In this paper, we study the evolution of hourly oil price volatility. Using multiple measures of oil price volatility, we conclude that volatility increased following the onset of COVID-19. After controlling for conventional predictors of oil price volatility, we show that COVID-19 cases and deaths led to an increase in daily oil price volatility by between 8% and 22%. Our results pass a battery of robustness tests.

An introduction to oil market volatility analysis 1

Modelling and forecasting crude oil price volatility is crucial in many financial and investment applications. The main purpose of this paper is to review and assess the current state of oil market volatility knowledge. It highlights the properties and characteristics of the oil price volatility that models seek to capture, and discuss the different modelling approaches to oil price volatility. Asymmetric response to price change, persistence and mean reversion, structural breaks, and possible market spillover of volatility are discussed. To complement the discussion, West Texas Intermediate futures price data are used to illustrate these properties using non-parametric and conditional modelling methods. The generalised autoregressive conditional heteroskedasticity-type models usually applied in the oil price volatility literature are also explored. We additionally examine the exogenous factors that may influence volatility in the oil markets.