A Continuous-Time Arbitrage-Pricing Model With Stochastic Volatility and Jumps
利用美国股票市场数据，构建并检验了一个连续时间资产定价模型，该模型假设股票收益由包含随机跳跃的泊松过程和随机波动率的布朗运动等共同因子驱动，并通过对均值收益施加过度识别约束来识别风险中性概率分布，从而为衍生品定价提供支持。
We formulate and test a continuous-time asset-pricing model using U.S. equity market data. We assume that stock returns are driven by common factors including random jump-size Poisson processes and Brownian motions with stochastic volatility. The model places overidentifying restrictions on the mean returns, allowing one to identify risk-neutral probability distributions useful in pricing derivative securities. We test for the restrictions and decompose moments of the asset returns into the contributions made by different factors. Our econometric methods take full account of time aggregation.