ance relied heavily on the traditional reinsurance market to protect its agricul- tural portfolio from inordinate losses. As a result of a 70 percent increase in the retrocession rates of 2001, Agroasemex’s search for new alternatives led it to analyze the comparative efficiency of the weather derivatives market. As the World Bank study states, this weather derivative transaction was the first of its kind in the developing world. While designing the contract there were taken into account the facts that
there are two agricultural production cycles in Mexico: spring-summer and autumn-winter and that the former is primarily a rain-fed production cycle, while the latter is generally irrigated. In addition, The crops and weather risks were selected given their relative importance in the portfolio, the consistency of the numerical analysis between negative deviations in the agricultural port- folio and the protection provided by the proposed weather derivative structure, and the availability of consistent and high-quality historical weather data. As far as the financial structure of the contract is concerned, there were
studied the negative deviations within the portfolio and the protection offered within the contract. Therefore, there were selected the following crops: beans, corn, tobacco, chickpeas and sorghum. Afterwards, there was established the relationship between weather in-
dices and the expected indemnities of the Agroasemex agricultural portfolio. In order to accomplish that, a severity index was created for each crop in the portfolio, measured as the ratio between the indemnities and total liabilities. Once the severity index was calculated for each crop, the next step was to find a mathematical relationship between the SI and the weather index most relevant to the crop. Agroasemex performed linear least square regres- sions for each crop severity index to establish the SI–weather-index relation- ship:
Y = β1 + β2X + u
Where Y=Indemnities/Total Liability X=FCDD (Factores Climaticos Dañinos Diarios), periods—that repre-
sent the index that captures the critical weather risk of each crop in the portfo- lio.
u is a normally distributed noise term; and the estimators for the linear
gradient and intercept were calculated using a least squares regression method.
For example, in order to determine the exposure of the beans crop to the low temperatures there are considered the number of days when the minimum temperatures is below a certain established limit during the cold season. Therefore, there were taken into account the historical data collected within five Mexican weather stations and two U.S. airport stations. For the tobacco was chosen an index (DDD-12) taking into account the
fact that the low temperatures represent the greatest risk 138
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161