Andrea Bastianin

Modelling Asymmetric Dependence Using Copula Functions: An application to Value-at-Risk in the Energy Sector

In this paper I have used copula functions to forecast the Value-at-Risk (VaR) of an equally weighted portfolio comprising a small cap stock index and a large cap stock index for the oil and gas industry. The following empirical questions have been analyzed: (i) are there nonnormalities in the marginals? (ii) are there nonnormalities in the dependence structure? (iii) is it worth modelling these nonnormalities in risk- management applications? (iv) do complicated models perform better than simple models? As for questions (i) and (ii) I have shown that the data do deviate from the null of normality at the univariate, as well as at the multivariate level. When considering the dependence structure of the data I have found that asymmetries show up in their unconditional distribution, as well as in their unconditional copula. The VaR forecasting exercise has shown that models based on Normal marginals and/or with symmetric dependence structure fail to deliver accurate VaR forecasts. These findings confirm the importance of nonnormalities and asymmetries both in-sample and out-of-sample.

Biofuels and Food Prices: Searching for the Causal Link

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-ofsample 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.

Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers

Call centers’ managers are interested in obtaining accurate point and distributional forecasts of call arrivals in order to achieve an optimal balance between service quality and operating costs. We present a strategy for selecting forecast models of call arrivals which is based on three pillars: (i) flexibility of the loss function; (ii) statistical evaluation of forecast accuracy; and (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of daily call arrivals. Although we focus mainly on point forecasts, we also analyze density forecast evaluation. We show that second-moment modeling is important for both point and density forecasting and that the simple seasonal random walk model is always outperformed by more general specifications. Our results suggest that call center managers should invest in the use of forecast models which describe both first and second moments of call arrivals.

How Does Stock Market Volatility React to Oil Shocks

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.

Food versus Fuel: Causality and Predictability in Distribution

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.

Oil Price Forecast Evaluation with Flexible Loss Functions

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.

Investments and Financial Flows Induced by Climate Mitigation Policies

In this paper we use the hybrid integrated model WITCH to quantify and analyze the investments and financial flows stimulated by a climate policy to stabilize Greenhouse Gases concentrations at 550ppm CO2-eq at the end of the century. We focus on investments to decarbonize the power sector and on investments in knowledge creation. We examine the financial flows associated with the carbon market and the implications for the international trade of oil. Criticalities in investment requirements will emerge when coal power plants with carbon capture and sequestration and nuclear power plants are deployed around 2020-2040, both in high and low income regions. Investments in energy related R&D increase sharply and might cause stress in the short term. However, the transition to a low-carbon world, although costly, appears to be manageable from a financial point of view. In particular, R&D financial needs can easily be accommodated using revenues from the carbon market, which is expected to eventually become more important than the oil market in terms of traded value.

Evaluating regulatory reform of network industries: a survey of empirical models based on categorical proxies

Proxies for regulatory reforms based on categorical variables are increasingly used in empirical evaluation models. We surveyed 63 studies that rely on such indices to analyze the effects of entry liberalization, privatization, unbundling, and independent regulation of the electricity, natural gas, and telecommunications sectors. We highlight methodological issues related to the use of these proxies. Next, taking stock of the literature, we provide practical advice for the design of the empirical strategy and discuss the selection of control and instrumental variables to attenuate endogeneity problems undermining identification of the effects of regulatory reforms.

The Socio–Economic Impact of a Breakthrough in the Particle Accelerators’ Technology: A Research Agenda

Preliminary evidence on the long–run trajectory of the accelerator industry suggests that it may be close to have reached the maturity phase of its cycle. If this is the case, how can we measure the benefits an uncertain breakthrough in acceleration technology? Who are the main stakeholders interested by such a breakthrough? We identify these subjects and sketch some avenues for answering these questions. We thus present a model for the social Cost-Benefit Analysis (CBA) of research infrastructures and illustrate the results of its implementation for assessing the benefits of accelerators in basic science and hadrontherapy. Lastly, we move from the social CBA of single research infrastructures to the modeling a major change in the accelerator technology and hence in the industry. A research agenda on the potential impacts of a technological breakthrough is presented.

Economic impacts of El Niño southern oscillation: evidence from the Colombian coffee market

El Nino Southern Oscillation (ENSO) is a naturally occurring phenomenon that affects weather around the world. Past ENSO episodes have had severe impacts on the economy of Colombia. We study the influence of ENSO on Colombian coffee production, exports, and price. Our structural econometric specification is consistent with an economic model of the market for Colombian coffee which, in the short run, is characterized by a downward-sloping demand curve and by a vertical supply curve. We show that El Nino (i.e., positive shocks to ENSO) is beneficial for Colombian production and exports and decreases the real price of Colombian coffee. On the contrary, La Nina (i.e., negative shocks to ENSO) depresses Colombian coffee production and exports and increases price. However, the overall impact of ENSO shocks is small. Both in the short run and in the long run, shocks to international demand for Colombian coffee are more relevant than supply-side shocks in Colombia in explaining the dynamics of the price of Colombian coffee. Our results suggest that a given coffee price shock can have beneficial, detrimental, or negligible effects on the Colombian economy, depending on its underlying cause. As a consequence, policy responses to coffee price shocks should be designed by looking at the causes of the shocks.