This paper examines the empirical relationship between economic indexes (namely the industrial production and the consumer confidence) and their volatility by using United States data over the periods 1919–2023 and 1960–2023, respectively. To address this topic, several GARCH-family models including volatility feedback and the asymmetric effects are estimated. We find that: (i) the industrial production index exhibits asymmetric effects, with significant fitting performance over the standard GARCH model; (ii) the inclusion of t-distributed innovations in the error term improves the fitting performance of the competing models against their counterparts with a normal distribution assumption; (iii) both models tested for the consumer confidence index exhibit similar performance to fit with the data; (iv) by excluding the months during and after the COVID-19 pandemics and the Russia-Ukraine conflict, it leads to the same results and conclusions for both economic indexes.
Download of the paper: https://www.tandfonline.com/doi/full/10.1080/00036846.2024.2393460?src=#abstract
This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID-19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with t-distributed innovations outperforms the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the t-distributed innovations remain the appropriate model for the COVID-19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short-term predictive ability—such as the conditional and the observed-date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models.
Download of the paper: https://onlinelibrary.wiley.com/doi/10.1002/fut.22469?af=R
This paper proposes a novel unobserved component model with a COVID-19 structural break in the trend growth rate to model output gaps. Using historical real GDP data for the Euro Area between 1995Q1 and 2022Q1, we test our framework against a battery of competing models, including a standard unobserved components model, a correlated model with a second-order Markov process, a Hodrick-Prescott filter and an augmented version of it. To examine the impact on the fitting performance, we test the inclusion and exclusion of pandemic quarters and we also extend the estimation to a country-level detail. We find that: (i) our suggested model outperforms the competing ones; (ii) when excluding pandemic quarters, the standard unobserved component model outperforms their counterparts; (iii) our model yields the best fitting performance for most of the Euro Area countries and (iv) the Hodrick-Prescott filter model has the poorest fitting performance.
Keywords: Euro Area, COVID-19, HP filter, Output gaps, Trend-cycle decomposition, Unobserved component models.
Download of the paper: https://www.sciencedirect.com/science/article/abs/pii/S0313592623000851
This paper presents a novel approach for structuring dependence between electricity and natural gas prices in the context of energy transition: a copula of mean-reverting and jump-diffusion processes. Based on historical day-ahead prices of the Nord Pool electricity market and the Henry Hub natural gas market, a stochastic model is estimated via the maximum likelihood approach and considering the dependency structure between the innovations of these two-dimensional returns. Given the role of natural gas in the global policy for energy transition, different copula functions are fit to electricity and natural gas returns. Overall, we find that: (i) using an out-of-sample forecasting exercise, we show that it is important to consider both mean-reversion and jumps; (ii) modeling correlation between the returns of electricity and natural gas prices, assuring nonlinear dependencies are satisfied, leads us to the adoption of Gumbel and Student-t copulas; and (iii) without government incentive schemes in renewable electricity projects, the usual maximization of the risk-return trade-off tends to avoid a high exposure to electricity assets.
Keywords: Copula's functions, Electricity Prices, Energy transition, Jump-Diffusion, Mean Reversion.
Download of the paper: https://www.sciencedirect.com/science/article/abs/pii/S0264999321002601