标题： 使用气候情景和风险模型预测和缓解农业价格波动 显示英文标题

标题： Predicting and Mitigating Agricultural Price Volatility Using Climate Scenarios and Risk Models

摘要： 农业价格波动对可持续金融、规划和政策构成挑战，这由市场动态和气象因素（如温度和降水）驱动。 在印度，最低支持价格（MSP）制度起到隐性作物保险的作用，使农民免受价格下跌的影响而无需支付保费。 我们分析了气候对大豆（中央邦）、水稻（阿萨姆邦）和棉花（古吉拉特邦）价格波动的影响。 使用欧洲哥白尼气候变化服务的ERA5-Land再分析数据，我们分析了历史气候模式，并评估了两种情景：SSP2.4.5（中等情景）和SSP5.8.5（严重情景）。 我们的研究结果表明，天气条件强烈影响价格波动，并且将气象数据整合到波动率模型中可以增强风险对冲能力。 使用指数广义自回归条件异方差（EGARCH）模型，我们估计了条件价格波动，并确定了天气与价格波动之间的交叉相关性。 认识到MSP等同于欧式看跌期权，我们应用布莱克-舒尔斯模型来估计其隐含保费，量化其财政成本。 我们提出了一种新的基于市场的风险对冲机制，即政府购买相当于MSP的保险，利用布莱克-舒尔斯模型进行准确的保费估算。 我们的结果强调了气象数据在农业风险建模中的重要性，支持针对性的保险措施，并加强农业金融的韧性。 这一气候信息驱动的金融框架增强了风险分担，稳定了价格，并在日益增长的气候不确定性下为可持续农业政策提供了依据。 显示英文摘要

摘要： Agricultural price volatility challenges sustainable finance, planning, and policy, driven by market dynamics and meteorological factors such as temperature and precipitation. In India, the Minimum Support Price (MSP) system acts as implicit crop insurance, shielding farmers from price drops without premium payments. We analyze the impact of climate on price volatility for soybean (Madhya Pradesh), rice (Assam), and cotton (Gujarat). Using ERA5-Land reanalysis data from the Copernicus Climate Change Service, we analyze historical climate patterns and evaluate two scenarios: SSP2.4.5 (moderate case) and SSP5.8.5 (severe case). Our findings show that weather conditions strongly influence price fluctuations and that integrating meteorological data into volatility models enhances risk-hedging. Using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, we estimate conditional price volatility and identify cross-correlations between weather and price volatility movements. Recognizing MSP's equivalence to a European put option, we apply the Black-Scholes model to estimate its implicit premium, quantifying its fiscal cost. We propose this novel market-based risk-hedging mechanism wherein the government purchases insurance equivalent to MSP, leveraging Black-Scholes for accurate premium estimation. Our results underscore the importance of meteorological data in agricultural risk modeling, supporting targeted insurance and strengthening resilience in agricultural finance. This climate-informed financial framework enhances risk-sharing, stabilizes prices, and informs sustainable agricultural policy under growing climate uncertainty.