Margherita Grasso

Modelling time-varying conditional correlations in the volatility of Tapis oil spot and forward returns

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and one-month 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 (CCC–MGARCH) model of Bollerslev (1990), Vector Autoregressive Moving Average–GARCH (VARMA–GARCH) model of Ling and McAleer (2003), VARMA–Asymmetric GARCH (VARMA–AGARCH) model of Hoti et al. (2002), and the Dynamic Conditional Correlation (DCC) model of Engle (2002). The dynamic correlations are extremely useful in determining whether the spot and forward returns are substitutes or complements, which can be used to hedge against contingencies. Both the univariate ARCH and GARCH estimates are significant for spot and forward returns, whereas the estimates of the asymmetric effect at the univariate level are not statistically significant for either spot or forward returns. Standard diagnostic tests show that the AR(1)–GARCH(1, 1) and AR(1)–GJR(1, 1) specifications are statistically adequate for both the conditional mean and the conditional variance. The multivariate estimates for the VAR(1)–GARCH(1, 1) and VAR(1)–AGARCH(1, 1) models show that the ARCH and GARCH effects for spot (forward) returns are significant in the conditional volatility model for spot (forward) returns. Moreover, there are significant interdependences in the conditional volatilities between the spot and forward markets. The multivariate asymmetric effects are significant for both spot and forward returns. Overall the multivariate VAR(1)–AGARCH(1, 1) dominates its symmetric counterpart. The calculated constant conditional correlations between the conditional volatilities of spot and forward returns using CCC–GARCH(1, 1), VAR(1)–GARCH(1, 1) and VAR(1)–AGARCH(1, 1) are very close to 0.93. Virtually identical results are obtained when the three constant conditional correlation models are extended to include two lags in both the ARCH and GARCH components. Finally, the estimates of the two DCC parameters are statistically significant, which makes it clear that the assumption of constant conditional correlation is not supported empirically. This is highlighted by the dynamic conditional correlations between spot and forward returns, for which its sample mean is virtually identical to the computed constant conditional correlation, regardless of whether a DCC–GARCH(1, 1) or a DCC–GARCH(2, 2) is used. For these models, the dynamic conditional correlations are in the range (0.417, 0.993) and (0.446, 0.993), signifying medium to extreme interdependence. Therefore, the dynamic volatilities in the returns in Tapis oil spot and forward markets are generally interdependent over time. These findings suggest that a sensible hedging strategy would consider spot and forward markets as being characterized by different degrees of substitutability.

The Health Effects of Climate Change: A Survey of Recent Quantitative Research

In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. In particular, a large amount of research on the effects of climate changes on human health has addressed two fundamental questions. First, can historical data be of some help in revealing how short-run or long-run climate variations affect the occurrence of infectious diseases? Second, is it possible to build more accurate quantitative models which are capable of predicting the future effects of different climate conditions on the transmissibility of particularly dangerous infectious diseases? The primary goal of this paper is to review the most relevant contributions which have directly tackled those questions, both with respect to the effects of climate changes on the diffusion of non-infectious and infectious diseases, with malaria as a case study. Specific attention will be drawn on the methodological aspects of each study, which will be classified according to the type of quantitative model considered, namely time series models, panel data and spatial models, and non-statistical approaches. Since many different disciplines and approaches are involved, a broader view is necessary in order to provide a better understanding of the interactions between climate and health. In this respect, our paper also presents a critical summary of the recent literature related to more general aspects of the impacts of climate changes on human health, such as: the economics of climate change; how to manage the health effects of climate change; the establishment of Early Warning Systems for infectious diseases.

Three-Regime Threshold Error Correction Models and the Law of One Price: The Case of European Electricity Markets

In this paper threshold error correction models (TVECMs) and min-max (MM) models are applied to examine the integration of European electricity markets. The relationships across German, Dutch, British and French forward prices are assessed allowing for the possibility that the convergence in prices may not always be operational. Indeed, interdependences may occur only when the spread in prices between two markets makes it profitable to invest in cross-border contracts. As a main result, allowing for non-linear adjustment dynamics improves the accuracy of the model.

Time Varying Parameters Bayesian Forecasting of Electricity Demand: The Italian Case

Electricity demand is modeled as a time-varying parameters (TVP) vector autoegression with or without imposing cointegration. The paper applies Bayesian strategies where all or a part of the parameters are allowed to vary, and compares their forecasts performances with alternative time series models, namely a seasonal ARIMA (SARIMA) specification and a vector error correction model (VECM). Considering Italian data, the appropriate diagnostic tests and estimation results are in favour of non-stability of the parameters. However, the forecasts abilities of the models do not show significant differencies when measured by RMSE and MAE, and compared trough the Diebold Mariano statistic. On the other hand, forecast intervals of Bayesian models show higher empirical coverage rates.