Extreme weather and demand for index insurance in rural India

The Future of Water: A Collection of Essays on “Disruptive” Technologies that may Transform the Water Sector in the Next 10 Years

“…An example of one of the early applications of such an idea was in Peru where the central banks were insured from floods, through a parametric index linked to the El Nino conditions (Khalil, Kwon, Lall, Miranda, & Skees, 2007;Skees, Hartell, & Murphy, 2007). Similar products have been developed and applied for drought and also to securitize water market option contracts and utility finances, including their use as ex ante or forecast insurance, that pays out potentially even before an event occurs in many different settings and countries (Brown & Carriquiry, 2007;Carriquiry & Osgood, 2012;Chantarat, Barrett, Mude, & Turvey, 2007;Goes & Skees, 2003;Zeff & Characklis, 2013) (Bjerge & Trifkovic, 2018;Maestro, Bielza, & Garrido, 2016). The Caribbean Risk Facility developed by the World Bank provides an example of a regional risk pooling and indexing approach.…”

An Actuarial Pricing Method for Air Quality Index Options

“…Insurance is widely used in agriculture to protect farmers against crop losses. Due to ethical problems such as adverse selection (i.e., due to information asymmetry) in traditional agricultural insurance [20], recent research has mainly focused on weather index insurance [21,22]. However, insurance is mostly used for catastrophic events, whereas derivatives provide a more flexible risk management tool for non-catastrophic weather risks [23,24].…”

“…If we excluded Y t from the AQI variable that accounts for random perturbation, the augmented Dickey-Fuller unit root test revealed a stationary sequence, with t = −17.157, versus a critical value of −3.968 at p < 0.01, and there was a first-order autocorrelation. Therefore, we established the AR (1) autoregression model (i.e., q = 1 in Equation (6)), which can be expressed as: The regression in Equation (22) shows that the residual has obvious clustering of volatility. A conditional heteroscedasticity test for the AR (1) model shows the results as shown in Table 4.…”

Memory, Risk Aversion, and Nonlife Insurance Consumption: Evidence from Emerging and Developing Markets

“…The first study is Kunreuther et al (1978) who argue that property owners' more likely buy disaster insurance if their homes have been damaged in the recent past. The second one is Bjerge and Trifkovic (2018) who have found that the past rainfall has positive impact on demand of index link weather insurance in India. Despite, partially similar findings, these studies have provided little guidance to firms and policymakers on which specific variable they should focus for improving disaster insurance supply.…”

“…The previously available literature on the subject, in the context of developing countries has focused mainly on weather insurance demand, crop insurance, government policies, and economic vulnerability of population living in rural and coastal areas (Mirza 2003;Toya and Skidmore 2007;Ghesquiere and Mahul 2007;Levine et al 2000;Liu and Neilson 2006;Linnerooth-Bayer et al 2009;Elango and Jones 2011;Hellin et al 2017;Sawada and Takasaki 2017;Bjerge and Trifkovic 2018;Yuzva et al 2018;Klomp and Hoogezand 2018). In the context, Linnerooth-Bayer et al (2009) have provided a comprehensive discussion over the need of disaster insurance markets in developing countries.…”