Management of climate risks in agriculture–will weather derivatives permeate?

It is a matter of common knowledge that weather represents the major source of uncertainty in crop production. It is to be expected that weather fluctuations will increase in the future due to climate change. Traditionally, farmers tried to protect themselves against weather-related yield variations by buying insurances. More recently, there has been a discussion regarding the use of weather derivatives to safeguard against volumetric risks. Although weather derivatives display advantages over traditional insurances, there is only a relatively small market for these products in agriculture. This is partly attributed to the fact that it is unclear whether and to what extent weather derivatives are a useful instrument of risk management in agriculture. This study applies real yield and weather data from Northeast Germany in order to quantify the risk-reducing effect that can be achieved in wheat production by using precipitation options. To do so stochastic simulation is used. The hedging effectiveness is controlled by the contract design (index, strike level, tick size). However, the local basis risk and the geographical basis risk remain with the farmer. We separate both causes of basis risk and reveal the extent of each. This enables conclusions regarding the design of weather derivatives; thus the question dealt with here is relevant both for farmers and for potential sellers of weather derivatives.

Measuring Non-Catastrophic Weather Risks for Businesses

While many published articles touch on the problem of using weather derivatives as tools for non-catastrophic weather-risk management, few studies have looked at the problem of appropriate risk measurement. This paper aims to present and evaluate all available methods used to identify and estimate the impact of non-catastrophic weather upon commercial enterprises. Correctly defining these parameters fundamentally affects building weather cover. Analysis of already existing methods of weather-risk measurement for businesses, as presented in the literature, has shown a few disadvantages. This paper proposes an improved approach to weather risk measurement – one based on an extended econometric model. We have empirically tested all the methods proposed herein and present our conclusions. The Geneva Papers (2009) 34, 425–439. doi:10.1057/gpp.2009.16

Weather Index Insurance and Climate Change: Opportunities and Challenges in Lower Income Countries

Weather index insurance underwrites a weather risk, typically highly correlated with agricultural production losses, as a proxy for economic loss and is gaining popularity in lower income countries. This instrument, although subject to basis risk and high start-up costs, should reduce costs over traditional agricultural insurance. Multilateral institutions have suggested that weather index insurance could enhance the ability of stakeholders in lower income countries to adapt to climate change. While weather index insurance could have several benefits in this context (e.g. providing a safety net to vulnerable households and price signals regarding the weather risk), climate change impacts increase the price of insurance due to increasing weather risk. Uncertainty about the extent of regional impacts compounds pricing difficulties. Policy recommendations for insurance market development include funding risk assessments, start-up costs and the extreme layer of risk. General premium subsidies are cautioned against as they may actually slow household adaptation. The Geneva Papers (2009) 34, 401–424. doi:10.1057/gpp.2009.11

Terrorism risk, resilience and volatility: A comparison of terrorism patterns in three Southeast Asian countries

Objective This article explores patterns of terrorist activity over the period from 2000 through 2010 across three target countries: Indonesia, the Philippines and Thailand. Methods We use self-exciting point process models to create interpretable and replicable metrics for three key terrorism concepts: risk, resilience and volatility, as defined in the context of terrorist activity. Results Analysis of the data shows significant and important differences in the risk, volatility and resilience metrics over time across the three countries. For the three countries analysed, we show that risk varied on a scale from 0.005 to 1.61 “expected terrorist attacks per day”, volatility ranged from 0.820 to 0.994 “additional attacks caused by each attack”, and resilience, as measured by the number of days until risk subsides to a pre-attack level, ranged from 19 to 39 days. We find that of the three countries, Indonesia had the lowest average risk and volatility, and the highest level of resilience, indicative of the relatively sporadic nature of terrorist activity in Indonesia. The high terrorism risk and low resilience in the Philippines was a function of the more intense, less clustered pattern of terrorism than what was evident in Indonesia. Conclusions Mathematical models hold great promise for creating replicable, reliable and interpretable “metrics” to key terrorism concepts such as risk, resilience and volatility