Giacomo Sbrana

Random Switching Exponential Smoothing and Inventory Forecasting

Exponential smoothing models represent an important prediction tool both in business and in macroeconomics. This paper provides the analytical forecasting properties of the random coefficient exponential smoothing model in the “multiple source of error” framework. The random coefficient state-space representation allows for switching between simple exponential smoothing and local linear trend. Therefore it enables controlling, in a flexible manner, the random changing dynamic behavior of the time series. The paper establishes the algebraic mapping between the state-space parameters and the implied reduced form ARIMA parameters. In addition, it shows that the parametric mapping allows overcoming the difficulties that are likely to emerge in estimating directly the random coefficient state-space model. Finally, it presents an empirical application comparing the forecast accuracy of the suggested model vis-a-vis other benchmark models, both in the ARIMA and in the exponential smoothing class. Using time series relative to wholesalers inventories in the USA, the out-of-sample results show that the reduced form of the random coefficient exponential smoothing model tends to be superior to its competitors.

Feasible generalized least squares estimation of multivariate GARCH(1, 1) models

We provide a feasible generalized least squares estimator for (unrestricted) multivariate GARCH(1, 1) models. We show that the estimator is consistent and asymptotically normally distributed under mild assumptions. Unlike the (quasi) maximum likelihood method, the feasible GLS is considerably fast to implement and does not require any complex optimization routine.

Some Financial Implications of Global Warming: an Empirical Assessment

Concurrent with the rapid development of the market for catastrophe (cat) bonds, a steady decline in their risk premia has been observed. Whether the latter trend is consistent with the evolution of natural disasters risk is an open question. Indeed, a large share of outstanding risk capital in the cat bonds market appears to be exposed to some climate change-related risk as, for instance, hurricane risk, which global warming is expected to enhance. This paper addresses the above issue by assessing the global warming evidence, its implications for the natural environment and the drivers of cat bonds risk premia. We find that radiative forcing, i.e. the net insolation absorbed by the Earth, drives the warming trend in temperature anomalies and the trend evolution of natural phenomena, such as ENSO and Atlantic hurricanes, enhancing their disruptive effects. Hence, in the light of the ongoing contributions of human activity to radiative forcing, i.e., greenhouse gases emissions, natural disasters risk appears to be on a raising trend. Yet, the latter does not appear to have been accurately priced in the cat bonds market so far. In fact, while we find that the falling trend in cat bonds multiples is accounted by the expansionary monetary stance pursued by the Fed, we do also find evidence of significant undervaluation of natural disasters risk.