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The purpose of this study is to evaluate the energy and exergy prices and carbon emission equivalents of the jet kerosene (Jet A-1) fuel considering 12 months data for an air craft used in the air transport sector in Turkey.

In the selection of the energy resources, one of the most important factors besides the need is the price of the energy resources. To use and save the energy resources efficiently, the prices should be evaluated in terms of exergy too. In this context, the exergy prices and carbon emission equivalents of the jet kerosene fuel have been examined.

According to analysis results, after January 2020, a steady decline in energy prices has been obtained until April 2020. In this regard, directly proportional changes have been obtained in exergy prices. The minimum exergy price of the fuel is calculated as 74.36 US cents/kWh for April 2020, while the maximum exergy price of the fuel is calculated as 150.02 US cents/kWh for September 2019. The minimum exergy price based carbon emission equivalents for the jet kerosene fuel is determined as 1,099.98 US cents/kg for April 2020, while the maximum exergy price based carbon emission equivalents for the jet kerosene fuel is found to be 2,219.29 US cents/kg for September 2019.

The new contribution has been made to the open literature by examining the energy and exergy prices of the jet kerosene fuel. In addition, the carbon emission equivalents of the jet kerosene fuel have been determined not only energy but also exergy methods.

The purpose of this study is to examine oil price shocks on US shale oil supply and energy security during the period 2000q1–2020q4.

In this study, the Shannon–Wiener index was used to calculate energy security, and then a structural vector autoregression (VAR) was applied to measure the effect of oil price shocks.

The results of the variance decomposition indicate that oil prices account for about 20% of changes in US shale oil production, while it explains only about 3% of changes in energy security. Finally, historical decomposition confirms the results of impulse response functions.

The novelty of this study is that so far, no study has examined the effect of oil price shock on shale oil production and energy security in the USA using the structural VAR model. This study also used the latest Shannon–Wiener index as a measure of energy security in the USA. The reason for selecting this index is that, in addition to considering the share of the total consumption of each primary energy, the share of energy imports from each country as well as the political risk of energy exporting countries to the USA are also included.

This paper aims to investigate the cyclical associations among energy prices and key macroeconomic variables for the USA.

To this end, the recently developed Hamilton filter (HF) and the oft-used Hodrick–Prescott filter (HPF) are used. The two methods produce starkly different results regarding the relationships between energy prices on the one hand and output and employment on the other.

While the HF suggests that energy prices are acyclical, the HPF suggests they are procyclical. However, the associations between energy prices and inflation are robust across the two methods, indicating that energy prices are strongly correlated with – and lead – the consumer price index (CPI). Furthermore, unlike the results produced by the HPF, those produced by the HF are robust across seasonally adjusted and unadjusted data.

Given the inherent seasonality in energy prices and the differences in the underlying processes that generate macroeconomic and energy prices, the results obtained from the HPF filter should be interpreted with caution.

To the best of the authors’ knowledge, this is the first study that uses the recently developed HF to examine the associations between the cyclical behaviors of three key macroeconomic variables in the USA – the industrial production index, the CPI, and total nonfarm employment – and the prices of natural gas, crude oil, gasoline, diesel, and heating oil. Second, this study presents a comparison of the results produced by the two filtering techniques. Third, recognizing that energy prices are characterized by seasonality, this study tests the robustness of the results produced by the two filters across seasonally adjusted and unadjusted data.

This paper examines the impact of macroeconomic and risk factors on the profitability and volatility of professional momentum portfolios for the US, the UK, Japan and Germany, for the period 1998–2018. Many of the factors employed, such as energy price changes and economic policy uncertainty, have been largely neglected in the relevant literature.

Regression analysis, VECTOR AUTOREGRESSION (VAR), Panel-VAR, Variance Decomposition Analysis

The results indicate that, since the financial crises in the US and the EU, energy prices and economic-policy uncertainty have become important return determinants, along with market-related uncertainty that seems to have a stable impact over time, especially for the U.S. and U.K. portfolios.

Economic policy uncertainty significantly affects contemporaneous momentum returns in the US, UK and Japan, mainly between 2007 and 2018, while market-related uncertainty affects all markets during all subperiods. In addition, the variance of market-related uncertainty (VIX) explains a large percentage of the variance in the momentum returns for the US, UK and Germany.

The main implication of the findings for portfolio managers is that a manager may increase (decrease) exposure to the momentum factor during optimistic (pessimistic) periods and during periods of rising energy prices (high economic policy and market-related uncertainty).

The paper examines the impact of factors, such as energy prices and economic policy uncertainty, which have been largely neglected in the relevant literature on the possible drivers of the momentum strategies. It employs professional portfolios that are often used in practice as benchmark indexes.

Estimates the cost of meeting the Kyoto Protocol with an energy‐economic optimization model. Special focus is on the Russian and Ukrainian and the potential implications of the US decision to withdraw from the Protocol. Finds that the carbon permit price can be expected to drop substantially due to US withdrawal. In fact, the aggregated emission target could be met in the absence of US participation. However, Russia and the Ukraine could be the dominant sellers of emission permits and they could increase the permit price. Clearly no climate benefits would result from trading emission permits that do not correspond to real reductions in CO₂ emissions. EU countries, Japan and Canada are not likely to be supportive of paying billions of dollars that do not result in emission reductions. One way of dealing with the Russian and Ukrainian surplus is to negotiate more stringent targets for subsequent commitment periods early, and to allow banking. The model suggests that, under these conditions, early action and banking do take place.

The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative analysis of the constructed index along with pre-existing World Bank and International Monetary Fund indices on energy.

This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.

This paper finds that an energy price shock has a detrimental impact on the macroeconomic indicators of India in the long run. This study also finds that the constructed index acts as a relatively more sensitive index in comparison to the International Monetary Fund and World Bank indices, which is bespoke to a developing economy case. This sensitivity is ascribed to dynamic weighting for a different basket of energy components, which are more pertinent to an Indian context.

The novelty of this research lies in the construction of a new index and its comparison to the existing ones. This study justifies why a developing economy would require a different measure of energy as opposed to the existing indices.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

Purpose – The purpose of this chapter is to investigate the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements.

Methodology/Approach – We use a biorhythm approach characterized by the seasonal affective disorder (SAD) variable to study the impact of emotion on energy markets. Normal times and periods of extreme price movements are approximated by OLS and quantile estimations, respectively.

Findings – We use European energy forward prices of oil, gas, coal, and electricity. European equity future index (Dow Jones Euro Stoxx 50) and euro/dollar US exchange rate are used as control variables for economic and financial environment. Estimating OLS and quantile regressions, we find that seasonal patterns have a significant impact during extreme volatility periods only. Further investigations reveal that the SAD effect is significant during periods of price decrease, but insignificant during price increase times. The out-of-sample predictive ability properties show that our “SAD model” outperforms significantly the pure “macroeconomic” one.

Originality/Value of chapter – This topic is novel in energy finance since I use psychological background theory to understand energy price dynamics. I illustrate the relevance of our approach by comparing the out-of-sample predictive ability of our model against macroeconomic one. My results could be considered to improve energy porfolio allocation.

The purpose of the study is to evaluate the impact of China's current coal‐to‐liquids (CTL) activities on its coal and oil markets from 2005 to 2025.

A partial equilibrium multi‐equation model of China's oil and coal markets is developed based on data obtained from the existing literature. The impact of CTL technologies on China's oil and coal markets is evaluated using computer simulations by solving the model under scenarios with and without CTL production.

The simulation results show that on average, the planned CTL activities will decrease crude oil prices by 5.73 percent and China's oil imports by 6.09 percent and increase China's domestic oil supply by 9.26 percent over the 20 year period. Also, China's demand for oil will increase by 0.35 percent on average, suggesting that CTL production will slightly stimulate China's demand for oil because of the drop in oil prices. China's demand for coal will also increase by 1.02 percent because of the additional demand for coal created by CTL production. Surprisingly, both coal prices and China's coal supply will decline by 0.51 percent while the demand for coal and coal supply of the rest of the world will be reduced by 1.63 percent and 0.28 percent, respectively.

The paper is the first study on the implication of CTL conversion from an economist's point of view. It applies an economic model to quantify the impacts of such technology on overall energy prices and supplies.