In this study, we pursue two main innovations. First, evaluate the predictive value of climate policy uncertainty (CPU) for oil market volatility. Second, demonstrate how an investor can exploit information contents CPU to gain higher returns. We find that increased values heighten crude risk, while forecast gains are achieved in a model accommodates CPU. further show observing offers portfolio...

This study explores the impact of oil depletion on the energetic efficiency of oil extraction and refining in California. These changes are measured using energy return ratios (such as the energy return on investment, or EROI). I construct a time-varying first-order process model of energy inputs and outputs of oil extraction. The model includes factors such as oil quality, reservoir depth, enh...

Studies of the energy return on investment (EROI) for oil production generally rely on aggregated statistics for large regions or countries. In order to better understand the drivers of the energy productivity of oil production, we use a novel approach that applies a detailed field-level engineering model of oil and gas production to estimate energy requirements of drilling, producing, processi...

a r t i c l e i n f o JEL classification: Q43 F3 G14 G15 Keywords: Stock market returns Oil price fluctuations Gregory–Hansen co-integration test Toda–Yamamoto Granger non-causality test The main focus of this study is to examine how oil price fluctuations influence the performance of stock markets. This study used the causality approach developed by Toda and Yamamoto (1995) to explore the caus...

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and onemonth forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCCMGARCH) model of Bollerslev [1990], Vector Autoregressive Moving Average – GARCH (VARMAGARCH) m...

In this paper we use “Clustering Method” to understand whether stock market volatility can be predicted at all, and if so, when it can be predicted. The exercise has been performed for the Indian stock market on daily data for two years. For our analysis we map number of clusters against number of variables. We then test for efficiency of clustering. Our contention is that, given a fixed number...

This paper investigates returns and volatility transmission between SPGCE (S&P Global Clean Energy), SPGO Oil), two non-renewable energy commodities (natural gas crude oil), three products of oil distillation (heating oil, gasoline, propane). We estimate a VAR(1) asymmetric BEKK-MGARCH(1,1) using daily U.S. data from March 1, 2010, to February 25, 2020. The empirical findings reveal vast hetero...