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Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

Forecasting and adequately measuring equity returns volatility is crucial for portfolio selection and trading strategies. Implied volatility is often considered to be informationally superior to the realized volatility. When available, implied volatility is largely used by practitioners and investors to forecast future volatility. To this extent we want to identify the best approach to track equity returns implied volatility using parametric and ANN approaches. Using daily equity prices and stock market indices traded on major international Exchanges we estimate time varying volatility using the E-GARCH approach, the Heston model and a novel ANN framework to replicate the corresponding implied volatility. Overall the ANN approach results the most accurate to track the equity returns implied volatility.

The paper investigates the effects of oil price shocks on stock market volatility in Europe by focusing on three measures of volatility, i.e. the conditional, the realized and the implied volatility. The findings suggest that supply-side shocks and oil specific demand shocks do not affect volatility, whereas, oil price changes due to aggregate demand shocks lead to a reduction in stock market volatility. More specifically, the aggregate demand oil price shocks have a significant explanatory power on both current- and forward-looking volatilities. The results are qualitatively similar for the aggregate stock market volatility and the industrial sectors' volatilities. Finally, a robustness exercise using short- and long-run volatility models supports the findings.

This paper examines the link between climate risk, energy consumption, and financial market performance in a sample of emerging countries over the period 2000–2024. The objective is to model the dynamic interactions between these three dimensions, in order to understand the extent to which energy dependence and exposure to climate risks influence the stability and resilience of emerging financial markets. We use a panel data covering a representative group of emerging countries to examine the nexus among climate risk, energy consumption, and stock market performance. The estimated models are based on a panel VAR to capture endogenous dynamic effects, on DCC‐GARCH model to analyze volatility and conditional correlations, on panel cointegration tests for long‐term relationships, and on structural break models to integrate exogenous shocks (2008 financial crisis, COVID‐19, war in Ukraine). The results show that climate risk negatively affects stock market performance in emerging countries. The dependence on fossil fuels increases financial vulnerability to climate shocks. Moreover, the increased use of renewable energy mitigates this impact and strengthens the resilience of financial markets. Finally, the intensity of the relationship varies depending on the degree of financial and energy development of emerging countries.

This research analyzed crude oil price volatility and the Nigerian economy for the period 1990:Q1-2023:Q4. The independent variables were crude oil price, exchange rate and oil revenue while the dependent variables were GDP, government revenue, foreign exchange reserve and income level (per capita income). Four models were formulated. Data were analyzed using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH). The results revealed strong evidence of volatility clustering in crude oil prices; the availability of new crude oil prices increased conditional volatility by a high magnitude. There was a significant negative effect of crude oil price volatility on the growth of the Nigerian economy while crude oil price volatility increased Nigeria’s foreign reserve, government revenue and income level. In addition, the exchange rate and oil revenue significantly increased the crude oil price volatility – an economic growth nexus in Nigeria. The study concluded that crude oil price hurts the Nigerian economy. However, crude oil price volatility exerted a positive effect on government revenue, foreign exchange reserve and income level in Nigeria during the period of the study. It was recommended that the government should devise a strategy to deviate the economy away from oil dependency to make the economy less vulnerable to oil price shocks.

Purpose This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations. Design/methodology/approach The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable. Findings The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model. Originality/value The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

In many competitive electricity markets around the world, the dynamic behavior of hourly electricity prices is subject to significant uncertainty and volatility due to electricity demand, availability of generation sources, fuel costs, and power plant availability. This work is devoted to describing and comparing the dynamics of electricity prices for some markets in Europe, selecting the five countries representing the largest economies in Western Europe (France, Germany, Italy, Spain, and the United Kingdom). Additionally, Denmark is included in the study to assess whether the size of the country is a determinant of price behavior. The six datasets of hourly price series, which exhibits a strong daily seasonality, are modelled using the most relevant well-known statistical models for time series analysis: ARIMA models and different versions of GARCH models. The comparison of the estimated models’ parameters, the analysis of outliers’ rate of appearance and the evaluation of out-of-sample one-day-ahead forecast let us draw some insightful similarities and dissimilarities between the analyzed countries.

Technology choice allows for substitution of production across states of nature and depends on state-dependent risk aversion. In equilibrium, endogenous technology choice can counter a persistent negative productivity shock with an increase in investment. An increase in risk aversion intensifies transformation across states, which directly leads to higher investment volatility. In our model and the data, the conditional volatility of investment correlates negatively with the price-dividend ratio and predicts excess stock market returns. In addition, the same mechanism generates predictability of consumption growth and produces fluctuations in the risk-free rate. This paper was accepted by Gustavo Manso, finance.