Weather-based estimation of wildfire risk

Catastrophic wildfires in California have become more frequent in past decades, while insured losses per event have been rising substantially. On average, California ranks the highest among states in the U.S. in the number of fires as well as the number of acres burned each year. The study of catastrophic wildfire models plays an important role in the prevention and mitigation of such disasters. Accurate forecasts of potential large fires assist fire managers in preparing resources and strategic planning for fire suppression. Furthermore, fire forecasting can a priori inform insurers on potential financial losses due to large fires. This paper describes a probabilistic model for predicting wildland fire risks using the two-stage Heckman procedure. Using 37 years of spatial and temporal information on weather and fire records in Southern California, this model measures the probability of a fire occurring and estimates the expected size of the fire on a given day and location, offering a technique to predict and forecast wildfire occurrences based on weather information that is readily available at low cost.biased sampling, forest fires, fire occurrence probabilities, fire weather

ASSESSMENT OF MARKET RISK IN HOG PRODUCTION USING VALUE-AT-RISK AND EXTREME VALUE THEORY

The objective of this paper is to investigate the performance of different VaR models in the context of risk assessment in hog production. Potential pitfalls of traditional VaR models are pinpointed and proposals to solve them are analyzed. After a brief description these methods are used to calculate the VaR of the hog finishing margin under German market conditions. In particular we apply Extreme Value Theory (EVT) to our data and compare the results with historical simulation (HS) and the variance-covariance method (VCM). Hill's estimator is used to determine the tail index of the extreme distribution of the gross margin in hog finishing and farrow production. A bootstrap method proposed by Danielsson et al. (1999) is adopted to choose the optimal sample fraction for the tail estimator. It turns out that EVT, VCM, and HS lead to different VaR forecasts if the return distributions are fat tailed and the forecast horizon is long.Livestock Production/Industries, Risk and Uncertainty,

Investment Reluctance: Irreversibility or Imperfect Capital Markets? Evidence from German Farm Panel Data

Investment behavior at the firm level is characterized by lumpy adjustments and frequent periods of inactivity. Low investment rates are particularly puzzling in transition economies where an urgent need of modernization exists. The literature offers two explanations for. Firstly, neo-institutional finance theory focuses on the impacts of imperfect capital markets on investment decisions showing that the limited availability of financial funds may confine firms investments. Secondly, real options theory asserts that the interaction of irreversibility, uncertainty and flexibility may also result in investment reluctance. In this paper we suggest a generalized model that combines imperfect capital markets and real options effects. We also offer an econometric implementation that has the structure of a generalized tobit model. This model is applied to German farm panel data. We demonstrate that ignoring real options effects may lead to erroneous results when estimating the impact of imperfect capital markets on investment decisions.investment decision, irreversibility, uncertainty, q-model, capital market imperfections, generalized tobit model, transition, Financial Economics, D81, D92, O12,

Can crop yield risk be globally diversified?

In 2007 and 2008 world food markets observed a significant price boom. Crop failures simultaneously occurring in some of the world’s major production regions have been quoted as one factor among others for the price boom. Against this background, we analyse the stochasticity of crop yields in major production areas. The analysis is exemplified for wheat, which is one of the most important crops worldwide. Particular attention is given to the stochastic dependence of yields in different regions. Thereby we address the question of whether local fluctuations of yields can be smoothed by international agricultural trade, i.e. by global diversification. The analysis is based on the copula approach, which requires less restrictive assumptions compared with linear correlations. The use of copulas allows for a more reliable estimation of extreme yield shortfalls, which are of particular interest in this application. Our calculations reveal that a production shortfall, such as in 2007, is not a once in a lifetime event. Instead, from a statistical point of view, similar production conditions will occur every 15 years.crop yield risk, fully nested hierarchical Archimedean copulas (FNAC), price boom

Modeling and Hedging Rain Risk

In this article we price a precipitation option based on empirical weather data from Germany using different pricing methods, among them Burn Analysis, Index Value Simulation and Daily Simulation. For that purpose we develop a daily precipitation model. Moreover, a de-correlation analysis is proposed to assess the spatial basis risk that is inherent to rainfall derivatives. The models are applied to precipitation data in Brandenburg, Germany. Based on simplifying assumptions of the production function, we quantify and compare the risk exposure of grain producers with and without rainfall insurance. It turns out that a considerable risk remains with producers who are remotely located from the weather station. Another finding is that significant differences may occur between the pricing methods. We identify the strengths and weaknesses of the pricing methods and give some recommendations for their applications. Our results are relevant for producers as well as for potential sellers of weather derivatives.Risk and Uncertainty,

Financial constraints in economic transition: Empirical evidence from Ukrainian large farms

This paper addresses the question of financial constraints in Ukrainian agriculture in transition. The main objective is to reveal the evidence of the both phenomena, soft budget constraints and credit rationing, investigating investment behaviour of large farms in Ukraine. Our empirical analysis is based on unbalanced panel data containing 529 agricultural enterprises from three Ukrainian regions between 2001 and 2005. Estimates of the Euler investment equation for several sub-samples reveal a dissimilar level of financial constraints. We confirm the presence of the soft financial environment (soft budget constraints) for the Ukrainian large farms being in an unconstrained financial regime. The farms belong to this regime if they receive credits after being unprofitable in two consecutive years. The other farms defined a priori as being in an constrained financial regime face evidence of credit rationing. With regard to the empirical results, we derive macroeconomic implications of financial constraints in the agriculture of Ukraine.transition agriculture, investment, soft budget constraints, credit rationing, Ukraine, Agricultural Finance,

Meteorological forecasts and the pricing of weather derivatives

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results.Weather forecasting, weather risk, price forecasting, nancial markets, temperature futures, CME

Optimal Design of Weather Bonds

Replaced with revised version of paper 06/17/08.weather risk, weather bonds, reinsurance, securitisation, Agricultural Finance, Risk and Uncertainty,

Systemic Weather Risk and Crop Insurance: The Case of China

The supply of affordable crop insurance is hampered by the existence of systemic weather risk which results in large risk premiums. In this article, we assess the systemic nature of weather risk for 17 agricultural production regions in China and explore the possibility of spatial diversification of this risk. We simulate the buffer load of hypothetical temperature-based insurance and investigate the relation between the size of the buffer load and the size of the trading area of the insurance. The analysis makes use of a hierarchical Archimedean copula approach (HAC) which allows flexible modeling of the joint loss distribution and reveals the dependence structure of losses in different insured regions. Our results show a significant decrease of the required risk loading when the insured area expands. Nevertheless, a considerable part of undiversifiable risk remains with the insurer. We find that the spatial diversification effect depends on the type of the weather index and the strike level of the insurance. Our findings are relevant for insurers and insurance regulators as they shed light on the viability of private crop insurance in China.crop insurance, systemic weather risk, hierarchical Archimedean copulas

On the Systemic Nature of Weather Risk

Systemic weather risk is a major obstacle for the formation of private (nonsubsidized) crop insurance. This paper explores the possibility of spatial diversification of insurance by estimating the joint occurrence of unfavorable weather conditions in different locations. For that purpose copula methods are employed that allow an adequate description of stochastic dependencies between multivariate random variables. The estimation procedure is applied to weather data in Germany. Our results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a national scale. Thus the possibility to reduce risk exposure by increasing the trading area of the insurance is limited. Irrespective of their economic implications our results pinpoint the necessity of a proper statistical modeling of the dependence structure of multivariate random variables. The usual approach of measuring stochastic dependence with linear correlation coefficients turned out to be questionable in the context of weather insurance as it may overestimate diversification effects considerably.weather risk, crop insurance, copula, Risk and Uncertainty, C14, Q19,