Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices.

The study provides comparative risk analyses of Australia’s three Victorian dairy regions. Historical data were used to identify business risk and financial viability. Multivariate distributions were fitted to the historical price, production, and input costs using copula models, capturing non-linear dependence among the variables. Monte Carlo simulation methods were then used to generate cash flows for a decade. Factors that influenced profitability the most were identified using sensitivity analysis. The dairies in the Northern region have faced water reductions, whereas those of Gippsland and South West have more positive indicators. Our analysis summarizes long-term risks and net farm profits by utilizing survey data in a probabilistic manner.

Adverse weather-related risk is a main source of crop production loss and a big concern for agricultural insurers and reinsurers. In response, weather risk hedging may be valuable, however, due to basis risk it has been largely unsuccessful to date. This research proposes the Lévy subordinated hierarchical Archimedean copula model in modelling the spatial dependence of weather risk to reduce basis risk. The analysis shows that the Lévy subordinated hierarchical Archimedean copula model can improve the hedging performance through more accurate modelling of the dependence structure of weather risks and is more efficient in hedging extreme downside weather risk, compared to the benchmark copula models. Further, the results reveal that more effective hedging may be achieved as the spatial aggregation level increases. This research demonstrates that hedging weather risk is an important risk management method, and the approach outlined in this paper may be useful to insurers and reinsurers in the case of agriculture, as well as for other related risks in the property and casualty sector.

Government support uncertainty, scarce yield information, and the inherent risk in bioeconomic phenomena are some of the deterrents faced by investors in the nascent cellulosic biofuel industry. A financial probabilistic model was developed to contrast the economic feasibility of producing cellulosic biofuels from energy cane and sweet sorghum using three technologies: hydrolysis, pyrolysis, and gasification. Hydrolysis and pyrolysis proved feasible (showed possibilities of a positive net present value) without government support and conditioned to stochastic feedstock yields and biofuel prices. Gasification was feasible with government support. Improved feedstock and biofuel productivity would considerably raise the feasibility probabilities for hydrolysis and pyrolysis without government support.

In order to guarantee the success of the nascent cellulose-based biofuel industry, it is crucial to identify the most economically relevant components of the biofuel production path. To this aim, an original stochastic financial model is developed to estimate the impact that different feedstock production and biofuel conversion parameters have on the probability of economic success. Estimation of the model was carried out using Monte Carlo simulation techniques along with parametric maximum likelihood estimation procedures. Results indicate that operational efficiency strategies should concentrate on improving feedstock yields and extending the feedstock growing season.

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. We examine the ability of the log-periodic power law model (LPPL-model) to accurately predict the end dates of speculative bubbles on financial markets through modeling of asset price dynamics on a selection of historical bubbles. The method is based on a nonlinear least squares estimation that yields predictions of when the bubble will change regime. Previous studies have only presented results where the predictions turn out to be successful. This study is the first to highlight both the potential and the limitations of the LPPL-model. We find evidence that supports the characteristic patterns as proposed by the LPPL-framework leading up to the change in regime; asset prices during bubble periods seem to oscillate around a faster-than-exponential growth. In most cases the estimation yields accurate predictions, although we conclude that the predictions are quite dependent on the point in time at which they are conducted. We also find that the end of a speculative bubble seems to be influenced by both endogenous speculative growth and exogenous factors. For this reason we propose a new way of interpreting the predictions of the model, where the end dates should be interpreted as the start of a time period where the asset prices are especially sensitive to exogenous events. We propose that negative news during this time period results in a regime shift and the bursting of the bubble. Thus, the model has the ability to predict sensitivity to exogenous events ex ante.

Economists have been blamed for their inability to forecast and address crises. This article attributes this inability to intertwined factors: the lack of a coherent definition of crises, the reference-class problem, the lack of imagination regarding the nature of future crises and sample-selection biases. Specifically, economists tend to adapt their views on crises to recent episodes, and omit averted and potential crises. Threshold-based definitions of crises run the risk of being ad hoc. Using historical examples, this article highlights some epistemological shortcomings of the current approach.