Hedging and speculative pressures and the transition of the spot-futures relationship in energy and metal markets

This paper examines the impact of hedging and speculative pressures on the transition of the spot-futures relationship in metal and energy markets. We build a Markov regime switching (MRS) model where hedging and speculative pressures affect the transition probabilities between a stronger and weaker spot-futures relationship. It is found that hedging pressure increases the likelihood of transition, i.e. destabilises the existing spot-futures relationship, while speculative pressure reduces it, i.e. stabilises the relationship, in the copper, crude oil and natural gas markets, but this effect is relatively weak in the silver and heating oil markets. We also examine whether these findings generate practical benefits by testing the hedging effectiveness of the minimum variance hedge ratios (MVH) derived from the MRS models with hedging and speculative pressures. A relatively strong reduction of the portfolio variance, hedger's utility and value at risk (VaR) is observed in the energy markets

Climate risk and green investments : New evidence

The academic literature on green energy equity markets has increased extensively over the last decade due to growing concerns about climate change and the substantial flow of investments into alternative energy markets. This study contributes by investigating the effect of climate risk on the return and volatility of green energy assets. This is one of the first papers to assess such effects using the recently developed climate policy uncertainty index as an indicator of climate risk. In particular, we seek to answer the following research questions. Firstly, does rising climate risk lead to a significant increase in green energy asset returns? Secondly, does climate risk affect the volatility of green energy assets negatively? Employing various models, we provide statistical evidence in favour of our hypotheses. Rising climate risk seems to encourage investment in alternative energy, which leads to an upward demand for green energy, which in turn increases the prices of green energy investments and decreases their volatility levels. Our analysis further shows that when climate risk increases, the correlation between crude oil and green energy returns decreases. Furthermore, green energy assets are more effective than gold for hedging oil market risk, without ignoring the hedging ability of technology stock investment.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree

This study examines the volatility and correlation and their relationships among the euro/US dollar exchange rates, the S&P500 equity indices, and the prices of WTI crude oil and the precious metals (gold, silver, and platinum) over the period 2005 to 2012. Our model links the univariate volatilities with the correlations via a hidden stochastic decision tree. The ensuing Hidden Markov Decision Tree (HMDT) model is in fact an extension of the Hidden Markov Model (HMM) introduced by Jordan et al. (1997). The architecture of this model is the opposite that of the classical deterministic approach based on a binary decision tree and, it allows a probabilistic vision of the relationship between univariate volatility and correlation. Our results are categorized into three groups, namely (1) exchange rates and oil, (2) S&P500 indices, and (3) precious metals. A switching dynamics is seen to characterize the volatilities, while, in the case of the correlations, the series switch from one regime to another, this movement touching a peak during the period of the Subprime crisis in the US, and again during the days following the Tohoku earthquake in Japan. Our findings show that the relationships between volatility and correlation are dependent upon the nature of the series considered, sometimes corresponding to those found in econometric studies, according to which correlation increases in bear markets, at other times differing from them

Essays on the empirical analysis of energy risk

Energy markets have become increasingly sophisticated, requiring modelling techniques of analogous calibre. This thesis deals with models of changing regime for the petroleum complex. Modelling the conditional distribution of energy prices as a regime switching process is motivated by the market-specific characteristics of oil: different market conditions, such as backwardation and contango, involve different dynamics. The first empirical part examines the very short-end of the futures curve volatility. To address in a realistic way the potential diverse response of oil volatility to fundamentals across high and low volatility regimes, augmented regime volatility models are employed. Results indicate that volatility can be decomposed to a highly persistent conditional volatility process and a relatively short-lived non-stationary process. Apart from evaluating the size of price risk, risk managers must also design a framework for mitigating their exposures. This is the focus of the second empirical part which estimates dynamic hedge ratios. Linking the concept of disequilibrium with that of uncertainty across high and low volatility regimes, a state-dependent error correction model with timevarying second moments is introduced. Finally, the third empirical part, examines the information content of the dependence structure between correlated petroleum futures curves. Term structure is decomposed into level, slope and curvature shocks. Introducing a multiregime framework, these factors are utilised to study inter-commodity and inter-market spreads. Results suggest markedly different state-dependent speeds of mean reversion and volatility/correlation dynamics across regimes. Overall, the employed models provide superior forecasting performance and indicate that state-dependent dynamics may provide significant benefits to market participants. The findings of this thesis have important implications for energy market trading and risk management, as well as energy market operations, such as refining and budget planning, by providing valuable information on the oil price volatility dynamics and the ability to predict risk

Futures contracts as hedges on equity investments

Πτυχιακή εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.In this work we try to identify, assess and evaluate the hedging performance of derivative contracts on equity portfolios that are available in the financial markets. We specifically focus on the use of future contracts, such as gold and oil futures, as hedgers on equity indices. We first present in brief theory and basics of equity investments and financial derivatives. We further focus on the concept of hedging and the uses and characteristics of future contracts. The thesis continues with a literature review on how the optimal hedge ratio is defined and how it can be estimated with the implementation of econometric models. We then employ multivariate GARCH BEKK models in order to estimate the dynamic conditional variance of the assets returns and evaluate the performance of their hedge ratios. Finally, we discuss the results and conclude with investment proposals

Impact of the COVID-19 pandemic on return and risk transmission between oil and precious metals: Evidence from DCC-GARCH model

It is frequently discussed in the literature that the correlation between low-correlation assets under ordinary market conditions may increase during crisis periods. To contribute to the ongoing debates, this paper empirically examines risk transmission between oil and precious metal markets induced by the COVID-19 pandemic using the DCC-GARCH model. The findings reveal evidence of a significant risk transmission between oil prices and precious metal prices, particularly during the onset of the COVID-19 pandemic. The findings point out that the negative relationship between oil and all precious metals returns in the pre-COVID-19 period has changed with the effect of the pandemic. In this process, it is revealed that the negative relationship between oil and gold has strengthened, but the negative relationship between oil and silver has weakened. In addition, the correlations between oil and platinum and palladium turn positive. The empirical findings imply that investors and portfolio managers seeking portfolio diversification and hedging opportunities in a high-risk environment such as the COVID-19 pandemic should consider gold and silver assets for investment

Information transmission and hedging effectiveness for the pairs crude oil-gold and crude oil-Bitcoin during the COVID-19 outbreak

This study uses hourly data to analyse the return and volatility transmission of oil-gold and oil-Bitcoin pairs during the preCOVID-19 and COVID-19 periods. The results show that the return transmissions vary across the two periods for both pairs. There is a unidirectional volatility spill-over from gold to oil in the preCOVID-19 period, and from oil to gold during the COVID-19 period. There is a significant volatility spill-over from Bitcoin to oil during the pre-COVID-19 period, whereas no evidence of volatility spill-over between oil and Bitcoin is shown during the COVID-19 period. Based on optimal weights, investors should increase their investments in, (a) gold for a portfolio of oil-gold, and (b) Bitcoin for a portfolio of oil-Bitcoin during the COVID-19 period. All hedge ratios are higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. The results of hedging effectiveness reveal that the risk-adjusted returns can be improved by constructing a portfolio of oil-gold and oil-Bitcoin during both sample periods. Further results reveal that gold is a strong safe haven and a hedge for the oil market, while Bitcoin serves as a diversifier for the oil market during the COVID-19 period

Commodity price volatility, stock market performance and economic growth: evidence from BRICS countries

Abstracts in English, Afrikaans and ZuluThe study investigated the nexus between commodity price volatility, stock market performance, and economic growth in the emerging economies of Brazil, Russia, India, China, and South Africa (the BRICS) predicated on two hypotheses. First, the study hypothesised that in modern integrated financial systems, commodity price volatility predisposes stock market performance to be non-linearly related to economic growth. The second hypothesis was that financial crises are an inescapable feature of modern financial systems. The study used daily data on stock indices and selected commodity prices as well as monthly data on national output proxies and stock indices. The study analysed data for non-linearities, fractality, and entropy behaviour using the spectral causality approach, univariate GARCH, EGARCH, FIGARCH, DCC-GARCH, and Markov Regime Switching (MRS) – GARCH. The four main findings were: first, spectral causality tests signalled dynamic non-linearities in the relationship between the three commodity futures prices and the BRICS stock indices. Second, the predominantly non-linear relationship between commodity prices and stock prices was reflected in the nexus between the national output proxies and the indices of the five main commodity classes. Third, spectral causality analysis revealed that the causal structures between commodity prices and national output proxies were non-linear and dynamic. Fourth, the Nyblom parameter stability tests revealed evidence of structural breaks in the data that was analysed. The DCC-GARCH model uncovered strong evidence of contagion, spillovers, and interdependence. The study added to the body of knowledge in three ways. First, micro and macro levels of commodity price changes were linked with corresponding stock market performance indicator changes. Second, unlike earlier studies on the commodity price – stock market performance – economic growth nexus, the study employed spectral causality analysis, single - regime GARCH analysis, Dynamic Conditional Correlation (DCC) – GARCH and a two-step Markov – Regime – Switching – GARCH as a unified analytical approach. Third, spectral causality graphs depicting relationships between stock indices and national output proxies revealed benign business cycle effects, thus, contributing to broadening the scope of business cycle theoryBusiness ManagementPhD. (Management Studies

A Hybrid Markov Switching Garch Model Approach For Improving Volatility Dynamics

Time series analysis has long attracted the attention of researchers in a variety of fields. Past two decades, time series have been analyzed using linear models, which have a number of advantages. However, the question of whether there are other methods that can help understand and predict actual data than linear models have been presented. The historical time series data show nonlinearity, evidence of structural changes, and are extremely volatile. In this case, linear models are incapable of explaining volatility and predicting future values. The GARCH family models explain volatility and forecasting very well for nonlinear time series data but collapse when structural breaks and market turbulence are present. This research aims to incorporate a new nonlinear time series model comprised of the nonlinear conditional mean model, ExpAR, and the nonlinear conditional variance model, MSGARCH. This hybrid model was developed to capture nonlinearity in both the mean and variance equations during structural changes and extreme market conditions. As a result, it can be a valuable method for fitting, illustrating, and capturing downside risk in nonlinear time series data. Moreover, it can offer some enhancement in both fitting and explaining volatility dynamics compared to the benchmark model. Since the launch of the proposed model, similar time series data has been generated from the proposed model and the benchmark model. Then the generated data are fitted into these models. Later, the real-world time series data were fitted into these models and their performance was compared

Geopolitical risk and renewable energy asset prices : Implications for sustainable development

This study intends to investigate the impact of geopolitical uncertainty, proxied by the geopolitical risk (GPR) index, on the volatility of renewable energy exchange traded funds (ETFs). Employing a two-state Markov regime switching model reveals that an upturn in the GPR index increases (reduces) the likelihood of being in the low (high) volatility regime. This finding could be attributed to the fact that when the geopolitical risk increases, users of crude oil, which is highly sensitive to such risk, tend to consider clean energy as a substitute for traditional energy sources. This causes a growth in the equity prices of new energy firms, further leading to a drop in the levels of volatility. Additionally, the results of generalized autoregressive conditional heteroscedasticity (GARCH) models also confirm that higher GPR implies lower risk for these green assets. The outcomes have implications to policymakers and investors participating in clean energy markets.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed