Simulation and hedging oil price with geometric Brownian Motion and single-step binomial price model

This paper[1] uses the Geometric Brownian Motion (GBM) to model the behaviour of crude oil price in a Monte Carlo simulation framework. The performance of the GBM method is compared with the naïve strategy using different forecast evaluation techniques. The results from the forecasting accuracy statistics suggest that the GBM outperforms the naïve model and can act as a proxy for modelling movement of oil prices. We also test the empirical viability of using a call option contract to hedge oil price declines. The results from the simulations reveal that the single-step binomial price model can be effective in hedging oil price volatility. The findings from this paper will be of interest to the government of Nigeria that views the price of oil as one of the key variables in the national budget. JEL Classification Numbers: E64; C22; Q30 Keywords: Oil price volatility; Geometric Brownian Motion; Monte Carlo Simulation; Single-Step Binomial Price Model [1] Acknowledgement: We wish to thank the two anonymous reviewers for their insightful comments and kind considerations. Memos to: Azeez Abiola Oyedele, School of Business and Enterprise, University of the West of Scotland, Paisley Campus, Paisley PA1 2BE, Scotland, Email: [email protected]

Volatility term structures in commodity markets

In this study, we comprehensively examine the volatility term structures in commodity markets. We model state‐dependent spillovers in principal components (PCs) of the volatility term structures of different commodities, as well as that of the equity market. We detect strong economic links and a substantial interconnectedness of the volatility term structures of commodities. Accounting for intra‐commodity‐market spillovers significantly improves out‐of‐sample forecasts of the components of the volatility term structure. Spillovers following macroeconomic news announcements account for a large proportion of this forecast power. There thus seems to be substantial information transmission between different commodity markets

Oil price volatility and new evidence from news and Twitter

In this paper, we develop semantic-based sentiment indices through relevant news and Twitter feeds for oil market using a state-of-the-art natural language processing technique. We investigate the predictability of crude oil price volatility using the novel sentiment indices through a hybrid structure consisting of generalized autoregressive conditional heteroskedasticity and bidirectional long short-term memory models. Findings show that media sentiment considerably enhances forecasting quality and the proposed framework outperforms existing benchmark models. More importantly, we compare the predictive power of news stories with Twitter feeds and document the superiority of the news sentiment index over the counterpart. This is an important contribution as this paper is the first study that compares the impact of regular press with that of social media, as an alternative informative medium, on oil market dynamics

The effects of investor emotions sentiments on crude oil returns: A time and frequency dynamics analysis

In this paper, we use wavelet coherence analysis to find that sentiment has a significant effect on crude oil returns that lasts over various investment horizons. While oil returns are positively associated with the sentiments of optimism and trust, it is negatively linked to fear and anger. These relations are more pronounced over the medium and the long term. Additionally, we find that short-term oil returns are relatively more sentiment-sensitive during turbulent periods than in normal conditions. These results highlight the importance of sentiment and investor psychology in the crude oil market

The impact of uncertainty shocks on the volatility of commodity prices

In this paper, we empirically examine the impact of uncertainty shocks on the volatility of commodity prices. Using alternative measures of economic uncertainty for the U.S. we estimate their effects on commodity price volatility by employing both VAR and OLS regression models. We find that the unobservable economic uncertainty measures of Jurado et al. (2015) have a significant and long-lasting positive impact on the volatility of commodity prices. Our results indicate that a positive shock in both macroeconomic and financial uncertainty leads to a persistent increase in the volatility of the broad commodity market index and of the individual commodity prices, with the macroeconomic effect being more significant. The impact is stronger in energy commodities compared to the agricultural and metals markets. In addition, our findings show that the measure of unpredictability of the macroeconomic environment has the most significant impact on the commodity price volatility when compared to the observable measures of economic uncertainty that have a rather small and transitory effect. Finally, we show that uncertainty in the macroeconomy is significantly reduced after the occurrence of large commodity market volatility episodes

Evaluating the Short Run Effects of U.S. Crude Oil Inventory Levels on WTI Crude Oil Price from 1993 – 2013

The focus of this research was to investigate the short-term influence of U.S. crude oil inventories on WTI crude oil prices from 1993 to 2013. This study is important for policy makers who wish to reduce the persistent and growing price volatility of crude oil and its related products as well as businesses such as airline companies who wish to make annual budgetary sales decisions. Using OLS multiple regression, cointegration, VECM and Ex-post forecast techniques; we provide evidence of an inelastic relationship in which a 1% increase in U.S. crude oil inventories is associated with 0.46% decrease in WTI crude oil prices; however this was only valid for 22% of WTI crude oil price variation. We also find that past data on U.S. crude oil inventories could be used to predict future WTI crude oil prices movement. Contrary to literature, the results of the VECM analysis indicate there is no short-run relationship between both variables over the trajectory

Multiple regression model for cotton price returns: Analysis of the impact of weather, oil price return, and China’s economy

Objectives The study aims at determining the relationship between cotton price return and oil price return, China’s economy, and weather condition, particularly the monsoon season. Furthermore, the study attempts to conduct a multiple regression model to estimate the cotton price return based on oil price return, difference of precipitation in China, India, USA, China’s interest rate, China’s import, and monsoon. Summary A multiple regression model is conducted for 263 samples of cotton spot price and independent variables: oil spot price, China’s import, China’s interest rate, precipitation in USA, China, India, and monsoon period, which are all recorded as monthly data from February of 1994 to December of 2015. The assumption pre-tests for multiple regression model are conducted. Based on the assumption test result, the input set of data can be considered valid for conducting multiple regression model. Conclusions The empirical results reaffirm the negative relationship between cotton price return and two other variables: China’s interest rate, and the change in China’s import level and positive relationship between cotton price return and oil price return. The model offers inconclusive conclusion for the relationship of cotton price return and monsoon period

The role of time-varying price elasticities in accounting for volatility changes in the crude oil market

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The interaction between oil price shocks, currency volatility and stock market prices: evidence from South Africa

Crude oil is an essential and strategic commodity in modern economies. Therefore, energy price fluctuations have the potential of affecting the economic welfare of a country. For instance, they have the potential to undermine the government’s attainment of its economic growth targets (National Treasury, 2016:2). The South African Reserve Bank (SARB) also considers oil price movements to be one of the major threats to currency volatility and the continued attainment of its inflation targets of about (3-6, per cent), as evidenced by numerous recent statements by its monetary policy committee (SARB, 2016:5-13). This study used co-integration tests to investigate the interaction between oil price shocks, exchange rates and stock market prices in South Africa over the period 1 January 2011 to 1 April 2018. The study employed the Johansen co-integration test. The results found no long run co-integration between oil prices, exchange rate and stock market prices. Therefore, this study adopted the VAR model for causality tests. Using the VAR model, this study found the existence of a unidirectional causality between stock prices and oil prices, with stock prices leading the oil prices changes. The all share index, resources and financials index were found to be significant variables to explain oil prices. This result is consistent with the business cycle view, which states that oil price fluctuations are mainly driven by demand factors. Furthermore, strong world output growth trends especially in emerging markets, could give rise to an upward surge in oil prices. The study also found that there is a weak correlation between stock price and exchange rate in South Africa. This is consistent with the asset approach. The findings of this study add to the already largely debated theories that seek to explain the relationship between the oil prices, exchange rates and stock market prices. The recommendation of this research is that, policy makers, researchers and investment bankers or fund managers who have interest or trade these financial instruments, may have to consider the role of stock market prices in the various sectors of the economy in their models for forecasting the path of the oil prices and the Rand/US Dollar exchange rate trend