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A Two-stage Estimation Approach of Continuous-time Series Models with Jumps

机译:带跳跃的连续时间序列模型的两阶段估计方法

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摘要

In modern financial economics continuous time-series diffusion models are more convenient to deal with than discrete time models when they are used to depict important economic variables such as stock prices, exchange rates and interest rates. A two-stage estimation approach is proposed to deal with the continuous-time models with jumps, in which the initial settings are quite resilient. This paper presents an example: first, the realized volatility theory is applied to the jumps and diffusion parameters of the model; then it uses forward Kolmogorov equation of actual price distribution at steady state to estimate the drift parameters. The model relies little on initial settings and optimization algorithms. The empirical results show that the estimations are stable and reliable. Hence the model is easier to extend to the estimation of complex set continuous time series.
机译:在现代金融经济学中,连续时间序列扩散模型用于描述重要的经济变量(如股票价格,汇率和利率)时,比离散时间模型更易于处理。提出了一种两阶段估计的方法来处理具有跳跃的连续时间模型,其中初始设置是相当有弹性的。本文以一个例子为例:首先,将已实现的波动性理论应用于模型的跳跃和扩散参数;然后使用稳态下实际价格分布的前Kolmogorov方程估算漂移参数。该模型几乎不依赖于初始设置和优化算法。实证结果表明,该估计是稳定可靠的。因此,该模型更易于扩展到复杂集合连续时间序列的估计。

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