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随机波动率模型的 GMM 估计：蒙特卡洛研究AI 解读

We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting matrix for estimation,…

含随机波动率与跳跃的连续时间套利定价模型AI 解读

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…

含非零初值、局部漂移与趋势的近单位根模型：最小二乘估计与检验的渐近分布AI 解读

This paper considers the distribution of the Dickey-Fuller test in a model with non-zero initial value and drift and trend. We show how stochastic integral representations for the limiting distribution can be derived either from the local to unity approach with local drift and trend or from the continuous record asymptotic results of Sørensen [29]. We also show how the stochastic integral representations can be utilized as the basis for finding the corresponding…

随机微分方程系统的连续记录渐近理论AI 解读

This paper considers estimation based on a set of T + 1 discrete observations, y (0), y ( h ), y (2 h ),…, y ( Th ) = y ( N ), where h is the sampling frequency and N is the span of the data. In contrast to the standard approach of driving N to infinity for a fixed sampling frequency, the current paper follows Phillips [35,36] and Perron [29] and examines the “dual” asymptotics implied by letting h tend to zero while the span N remains fixed. We suggest a way of explicitly…