Search

We assess the degree of connectedness among 16 metals that are critical for the production of clean energy technologies. These commodities are the constituents of the Energy Transition Metals (ETMs) price index maintained by the International Monetary Fund and comprise base, precious, and minor metals. We rely on Vector Autoregressive models and generalized forecast error variance decomposition to quantify spillovers among ETMs returns and volatilities. By calculating both static and dynamic measures of connectedness, we gain insight into the patterns of shock transmission between ETMs. Our static analysis reveals that base and precious metals are net shock transmitters, while minor and most battery metals are net receivers. By splitting the analysis into three groups, we find that almost half of the connectedness originates within each group, whereas the other half is due to cross-group spillovers. Moreover, we find that the system-wide connectedness of returns is positively correlated with proxies of economic activity, whereas volatility connectedness seems to be more related to global economic policy uncertainty.

We present a weekly structural Vector Autoregressive (VAR) model of the US crude oil market. Exploiting weekly data we can explain short-run crude oil price dynamics, including those related with the COVID-19 pandemic and with the Russia’s invasion of Ukraine. The model is set identified with a Bayesian approach that allows to impose restrictions directly on structural parameters of interest, such as supply and demand elasticises. Our model incorporates both the futures-spot price spread to capture shocks to the real price of crude oil driven by changes in expectations and US inventories to describe price fluctuations due to unexpected of variations of above-ground stocks. Including the futures-spot price spread is key for accounting for feedback effects from the financial to the physical market for crude oil and for identifying a new structural shock that we label expectational shock. This shock plays a crucial role when describing the series of events that have led to the spike in the price of crude oil recorded in the aftermath of Russia’s invasion of Ukraine.

Over the period 2015-2050 the consumption of natural gas of European OECD countries is expected to grow more than the consumption of any other energy source. Although these countries are interconnected and in most cases share a common currency, their wholesale national gas markets are highly heterogeneous. We study the determinants of cross-country convergence of natural gas prices for industrial consumers in fourteen European countries. Our empirical analysis is based on the notions of pairwise, relative and σ-convergence. We show that there is evidence of pairwise price convergence and that some key characteristics of gas markets, such as the maturity of trading hubs and the degree of interconnection, are positively associated with the existence of a convergence process. This result carries over to the notion of σ-convergence and is robust to a number of changes in the implementation of the statistical tests. The analysis of relative convergence points to the existence of price-growth convergence, while price-level convergence is not supported by the data. Lastly, we assess the the short-run implications of price convergence focusing on the speed of reversion to equilibrium after a system-wide shocks hits the cointegrating relation.

Call centers' managers are interested in obtaining accurate forecasts of call arrivals because these are a key input in staffing and scheduling decisions. Therefore their ability to achieve an optimal balance between service quality and operating costs ultimately hinges on forecast accuracy. We present a strategy to model selection in call centers which is based on three pillars: (i) a flexible loss function; (ii) statistical evaluation of forecast accuracy; (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of call arrivals. We show that second moment modeling is important when forecasting call arrivals. From the point of view of a call center manager, our results indicate that outsourcing the development of a forecasting model is worth its cost, since the simple Seasonal Random Walk model is always outperformed by other, relatively more sophisticated, specifications.

Some regulatory reforms do not change just a specific signal that can be represented by a quantitative continuous variable, such as a tax rate, a price cap, or an emission threshold. The standard theory of reform in applied welfare economics (going back to contributions by e.g. Ramsey, Samuelson and Guesnerie) asks the question: What is the marginal effect on social welfare of changing a policy signal? However, reforms such as privatization, unbundling or liberalization of network industries are often described by ‘packages’ shifting a policy framework. It is increasingly frequent in the empirical evaluation of such reforms to use categorical variables, often in polytomous form, for instance describing unbundling steps (vertical integration, accounting, functional, legal, ownership separation) on a discrete numerical scale, such as those proposed by the OECD and other international bodies. We review recent econometric literature evaluating regulatory reforms using such variables (40 papers) and we discuss some methodological issues arising in this context.

We develop a structural econometric model to study the impacts of El Niño Southern Oscillation (ENSO) on Colombian coffee production, exports and price. Our empirical specification is consistent with an economic model of the coffee market that, in the short-run, is characterized by a downward-sloping demand curve and by a vertical supply curve. This allows the study of the effects of unpredictable innovations to ENSO on the Colombian coffee price, while controlling for shocks arising from both the supply and the demand-side of the market. We show that El Niño events (i.e. positive shocks to ENSO) might be beneficial for production and exports and tend to decrease the price of coffee. On the contrary, La Niña conditions (i.e. negative shocks to ENSO) depress coffee production and exports and increase price. However, the overall impact of ENSO shocks is small. In the short-run, ENSO shocks explain 2% of the fluctuations of coffee production and 0.2% of the variability of the price of coffee. In the long-run, these percentages rise to 8% and 6%, respectively. Both in the short-run and in the long-run, demand-side shocks are more relevant than supply-side shocks in explaining the dynamics of the price of coffee.

In this paper we study how monetary policy, economic uncertainty and economic policy uncertainty impact on the dynamics of gross capital inflows in the US. Particular attention is paid to the mixed frequency-nature of the economic time series involved in the analysis. A MIDAS-SVAR model is presented and estimated over the period 1988-2013. While no relation is found when using standard quarterly data, exploiting the variability present in the series within the quarter shows that the effect of a monetary policy shock is greater the longer the time lag between the month of the shock and the end of the quarter. In general, the effects of economic and policy uncertainty on US capital inflows are negative and significant. Finally, the effect of the three shocks is different when distinguishing between financial and bank capital in ows from one side, and FDI from the other.

The security of energy supply is a key geopolitical factor in the relationship between the European Union and the southern neighborhood countries of the Middle East and North Africa region. We study the response of eight Mediterranean economies to exogenous oil supply shocks. We focus on the effects on economic activity - as measured by real Gross Value Added - for the whole economy, as well as for selected industries. We show that there are clear patterns characterizing the response of different economies to an unexpected reduction in global oil production. The main determinants of these patterns are the degree of energy intensity and energy dependence of the country, as well as the composition of its Gross Value Added.

We study the impact of oil price shocks on U.S. stock market volatility. We derive three different structural oil shock variables (i.e. aggregate demand, oil-supply, and oil-demand shocks) and relate them to stock market volatility, using bivariate structural VAR models, one for each oil price shock. Identification is achieved by assuming that the price of crude oil reacts to stock market volatility only with delay. This implies that innovations to the price of crude oil are not strictly exogenous, but predetermined with respect to the stock market. We show that volatility responds significantly to oil price shocks caused by sudden changes in aggregate and oil-specific demand, while the impact of supply-side shocks is negligible.

We study the effects of crude oil price shocks on the stock market volatility of the G7 economies. We rely on a structural VAR model to identify the causes underlying the oil price shocks and gauge the differential impact that oil supply and oil demand innovations have on financial volatility. We show that stock market volatility does not respond to oil supply shocks. On the contrary, demand shocks impact significantly on the variability of the G7 stock markets.

According to the Rockets and Feathers hypothesis (RFH), the transmission mechanism of positive and negative changes in the price of crude oil to the price of gasoline is asymmetric. Although there have been many contributions documenting that downstream prices are more reactive to increases than to decreases in upstream prices, little is known about the forecasting performance of econometric models incorporating asymmetric price transmission from crude oil to gasoline. In this paper we fill this gap by comparing point, sign and probability forecasts from a variety of Asymmetric-ECM (A-ECM) and Threshold Autoregressive ECM (TAR-ECM) specifications against a standard ECM. Forecasts from A-ECM and TAR-ECM subsume the RFH, while the ECM implies symmetric price transmission from crude oil to gasoline. We quantify the forecast accuracy gains due to incorporating the RFH in predictive models for the prices of gasoline and diesel. We show that the RFH is useless for point forecasting, while it can be exploited to produce more accurate sign and probability forecasts. Finally, we highlight that the forecasting performance of the estimated models is time-varying.

We analyze the relationship between the prices of ethanol, agricultural commodities and livestock in Nebraska, the U.S. second largest ethanol producer. The paper focuses on long-run relations and Granger causality linkages between ethanol and the other commodities. The analysis takes possible structural breaks into account and uses a set of techniques that allow to draw inferences about the existence of long-run relations and of short-run in-sample Granger causality and out-of-sample predictive ability. Even after taking breaks into account, evidence that the price of ethanol drives the price dynamics of the other commodities is extremely weak. It is concluded that, on the basis of a formal, comprehensive and rigorous causality analysis we do not find evidence in favour of the Food versus Fuel debate.

This paper examines the relationship between biofuels and commodity food prices in the U.S. from a new perspective. While a large body of literature has tried to explain the linkages between sample means and volatilities associated with ethanol and agricultural price returns, little is known about their whole distributions. We focus on predictability in distribution by asking whether ethanol returns can be used to forecast different parts of field crops returns distribution, or vice versa. Density forecasts are constructed using Conditional Autoregressive Expectile models estimated with Asymmetric Least Squares. Forecast evaluation relies on quantile-weighed scoring rules, which identify regions of the distribution of interest to the analyst. Results show that both the centre and the left tail of the ethanol returns distribution can be predicted by using field crops returns. On the contrary, there is no evidence that ethanol can be used to forecast any region of the field crops distribution.

The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (mixed models). Our empirical findings suggest that, irrespective of the shape of the loss function, the class of financial models is to be preferred to time series models. Both financial and time series models are better than mixed and structural models. Results of the Diebold and Mariano test are not conclusive, for the loss differential seems to be statistically insignificant in the large majority of cases. Although the random walk model is not statistically outperformed by any of the alternative models, the empirical findings seem to suggest that theoretically well-grounded financial models are valid instruments for producing accurate forecasts of the WTI spot price.