This study attempts to construct an econometric model using China's natural disaster losses and macro-industry development data from 1980 to 2017 to explore the macroeconomic fluctuations caused by natural disasters. The structural vector autoregressive (SVAR) and the seemingly unrelated regression (SUR) models are employed in estimating the impact of natural disasters on China's macroeconomy and how the disasters specifically affect the three sectors of the economy: primary, secondary, and tertiary. This study concludes that even though natural disasters in China do not significantly affect the overall real GDP, they have adverse impacts on the production in the primary industry, causing a sudden reduction in the means of production in the market and directly affecting various industries, but the impact on the secondary and tertiary industries is weak. This study also shows that the effect of natural disasters on the primary sector reduced significantly following industry restructuring after China's accession to the World Trade Organization (WTO). The impact of natural disasters on the primary industry could be reduced by adjusting the industrial structure to deal with macroeconomic shocks caused by natural disasters in order to promote macroeconomic stability of both regional and national economies. Finally, national aid policy should focus on the primary industry since that sector is significantly affected by natural disasters shocks.
This paper compares the weather insurance, weather index insurance and index futures and focuses on why China needs to develop weather indexes and adopt and trade weather index futures. It further discusses how to construct the indexes and futures and how to price them. Different from the Heating Degree Days (HDDs) and Cooling Degree Days (CDDs) used at Chicago Mercantile Exchange (CME), it develops the Extremely Heating Days (EHDs) and Extremely Cooling Days (ECDs) to derive relevant temperature-based weather index futures. Recently China has started using weather index insurance to cover farmers’ risk. Through comparisons of weather index futures with index insurance, this study shows the necessity and importance of using the weather index futures to better protect farmers and better develop China’s financial markets.
Precisely estimation of Cat-loss is not only the foundation of risk analysis, but also the premise of product design and the practice of insurance compensation. The law of large numbers generally assumes risks have normal distributions, which reduces the accuracy of damage assessment and influences the pricing of cat-insurance due to negligence of the extreme value at both sides of the distribution. Data of more than 100 million Yuan of earthquake disaster loss from 1969-2014 presents the characteristics of right skewed peak, excess kurtosis and heavy tail. Furthermore, the comparison of QQ plot, parameter estimation as well as test of model parameters between N-distribution, E-distribution, W-distribution and P-distribution shows that the generalized Pareto distribution fits the earthquake loss perfectly, and significantly improves the estimate precision.
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