Comparing the Hedging Effectiveness of Weather Derivatives Based on Remotely Sensed Vegetation Health Indices and Meteorological Indices

Abstract

Weather derivatives are considered a promising agricultural risk management tool. Station-based meteorological indices typically provide the data underlying these instruments. However, the main shortcoming of these weather derivatives is an imperfect correlation between the weather index and the yield of the insured crop, called basis risk. This paper considers three available remotely sensed vegetation health (VH) indices, namely, the vegetation condition index (VCI), the temperature condition index (TCI), and the vegetation health index (VHI), as indices for weather derivatives in a German case study. We investigated the correlation and period of highest correlation with winter wheat yield. Moreover, we analyzed whether the use of remotely sensed VH indices for weather derivatives can reduce basis risk and thus improve the performance of weather derivatives. The two commonly used meteorological indices, precipitation and temperature sums, were employed as benchmarks. Quantile regression and index value simulation were used for the design and pricing of the weather derivatives. The analysis for the selected farms and corresponding counties in northeastern Germany revealed that, on average, the VHI resulted in the highest correlation with winter wheat yield, and VHI-based weather derivatives were also superior in terms of the hedging effectiveness. The total periods of the highest correlations ranged from the beginning of April to the end of July. VHI- and VCI-based weather derivatives led to statistically significant reductions of basis risk, compared to the benchmarks. Our results indicate that the VHI-based weather derivatives can be useful alternatives to meteorological indices, especially in regions with sparser weather station networks.

Do remotely-sensed vegetation health indices explain credit risk in agricultural microfinance?

“…However, we find the VCI to outperform the VHI for the branch office that is located in the western part of the study area. A varying performance of vegetation health indices across regions is in line with the literature (Bokusheva et al, 2016;Kogan et al, 2003;Möllmann et al, 2018) and could be related to the quality of the remotely-sensed data, local species (Kogan et al, 2003) or the precision of identifying the areas that were cultivated with rice by the clients of the ABM. Results of the quantile regressions show that the explanatory power is higher in the upper quantiles of the distribution of the number of installments that were overdue between one and 14 days.…”

“…The credit risk reduction potential of the precipitation index is found to be even smaller, ranging from 0.2 % to 0.3 % per index point. The great difference between the results using vegetation health indices and weather station indices might refer to a higher basis risk inherent in weather station based indices (Möllmann et al, 2018). Furthermore, Table 3 shows the results for the loan-and sociodemographic variables.…”

“…While for Antsirabe and Mahitsy, the TCI outperforms the VCI, we find a higher explanatory power of the VCI in Betafo (Table 4). A varying performance of the different vegetation health indices across different regions has already been found by Bokusheva et al (2016) and Möllmann et al (2018). This variability might be related to the quality of the remotely-sensed data, local species (Kogan, Gitelson, Zakarin, Spivak, & Lebed, 2003) or the precision of identifying the areas that were cultivated with rice by the clients of the ABM.…”

An Actuarial Pricing Method for Air Quality Index Options

“…Many researchers have studied the use of financial means to manage weather risks [17][18][19]. These include insurance, and weather derivatives based on futures, options, and other financial instruments.…”

“…If the AAQI at expiration is 2.3 (higher than the strike index), the company can execute the options contract. According to Equation (18), the payoff per share is 900 CNY based on P(t)=m[D(T) -K] =3000 × (2.3 -2.0) =900. After subtracting the premium payment of 221.03 CNY per share, the actual net income is 678.97 CNY, and 1500 shares earns is 1,018,455 CNY.…”