How does stock market volatility react to oil shocks?

We study the impact of oil price shocks on the U.S. stock market volatility. We jointly analyze three different structural oil market shocks (i.e., aggregate demand, oil supply, and oil-specific demand shocks) and stock market volatility using a structural vector autoregressive model. 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 unexpected changes in aggregate and oil-specific demand, whereas the impact of supply-side shocks is negligible

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; (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 moments modeling is important both for 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

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.Climate Change, Mitigation, Carbon Finance, Emission Trading, Energy Investments

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.Oil Price, WTI Spot and Futures Prices, Forecasting, Econometric Models

What drives the European carbon market? Macroeconomic factors and forecasts

Putting a price on carbon -- with taxes or developing carbon markets -- is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the EU Emissions Trading Scheme (EU ETS). We aim to uncover supply- and demand-side forces that can contribute to improving the prediction accuracy of models at short- and medium-term horizons. We show that a simple Bayesian Vector Autoregressive (BVAR) model, augmented with either one or two factors capturing a set of predictors affecting the price of carbon, provides substantial accuracy gains over a wide set of benchmark forecasts, including survey expectations and forecasts made available by data providers. We extend the study to verified emissions and demonstrate that, in this case, adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR model. We rely on emissions and price forecasts to build market monitoring tools that track demand and price pressure in the EU ETS market. Our results are relevant for policymakers and market practitioners interested in monitoring the carbon market dynamics.Comment: The Supplementary Material is available upon request to the author

Revised survival analysis-based models in medical device innovation field

Scholars have shown that innovation and R&D affect both the business cycle and long-run economic growth (Basu et al.[1]; Comin and Gertler[2]). A statistical analysis of cross-country adoption of medical technology data, whose focus is on linear particle accelerators used as radiation treatment devices for patients with cancer, is presented. We exploit a unique database collecting information on some worldwide radiotherapy centres and concerning the exact year of medical devices adoption, in order to compare the late-innovation functions of different groups of countries and to detect the basic economic, social and geographical features impacting on the early technological innovation opportunity. From a statistical point of view, a contribution to the study of technological medical innovations can be provided through the survival analysis-based models. Survival analysis resorts to both non-parametric and semi-parametric tools, such as the survival function (e.g. Kaplan and Meier[5]), which gives the probability of surviving beyond a certain event time t, and the Cox regression model (Cox[3]; Cox and Oakes[4]), which fulfills predictive purposes by detecting both the individual baseline hazard and that associated with the presence of specific factors impacting on the event occurrence. Typically, the event of interest takes a negative connotation since denoting a failure (e.g., length of time before a patient die after a disease). The fact that the survival and the cumulative distribution of a random variable are intertwined proves useful to interpret survival analysis results from an economic standpoint. Our proposal is to extend the survival analysis approach to the context of the innovative medical device adoption and its eventual diffusion within the worldwide countries, here representing the statistical units of interest. In such a perspective, a new perception of the main survival analysis tools is then provided. The event of interest is recognized in the initial adoption of a specific technology, becoming an indicator of medical technology innovation. On the contrary, the survival function is interpreted as an indicator of the delay in the technological innovation adoption since measuring the probability of introducing a novel medical device beyond a specific event time t. Given these features, the survival function is named late-innovation function. In the same manner, also the Cox regression model is framed into an opposite scenario where the baseline hazard has no longer the meaning of risk but rather the meaning of early technological innovation opportunity, if no factors impacting on the initial technology adoption are taken into account. Analogously, the hazard function built on specific economic, social or geographical variables allows to detect their effects on the early technological innovation opportunity

Causality and predictability in distribution : the ethanol–food price relation revisited

This paper examines the relationship between biofuels, field crops and cattle prices in the U.S. from a new perspective. We focus on predictability in distribution by asking whether ethanol returns can be used to forecast different parts of field crops and cattle 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 or cattle returns distribution

Energy efficiency in Europe: trends, convergence and policy effectiveness

This paper analyses energy efficiency in the EU, both in terms of reductions in energy intensity and in terms of physical indicators, looking at the differences among sectors and among Member States. We test econometrically the existence of convergence in energy intensity across Europe. We find a sensible catching–up of less performing countries, particularly in the agricultural and in the industrial sectors. Against this background, we analyse the role played by energy policies in EU Member States and we identify the most effective classes of policies and measures by means of a panel analysis of the EU-15 and Norway. It turns out that, in the residential sector, energy efficiency is particularly affected by heating regulations, by subsidies as well as tax reductions; in the transport sector, effective policies are tax reductions, incentives to eliminate old and polluting cars, car sharing, commuter plan and traffic management; in the industrial sector, mandatory technology standards, financing at low interest rate, information activities, education and outreach proved to be effective.energy intensity, energy efficiency, convergence, European energy policy

The empirics of regulatory reforms proxied by categorical variables: recent findings and methodological issues

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