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Potential Applications of Function Data Analysis in High-frequency Financial Research

机译:函数数据分析在高频金融研究中的潜在应用

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High-Frequency and intraday financial data in general, have been an important focus of research in finance, econometrics and statistics for over two decades. There are mainly two streams of studies in this area: 1) time series properties at tick by tick or fixed high frequency resolution, 2) inference based on diffusion processes. The distinction is not sharp. The focus of the statistical analysis has been on building of time series models at fine resolution, possibly at non-evenly spaced times, or on estimating quantities, i.e. the noise covariance’s, implied by the continuous time financial theory.
机译:通常,高频和日内财务数据在过去的二十多年中一直是金融,计量经济学和统计研究的重要重点。该领域的研究主要有两个方面:1)逐滴答或固定高频分辨率的时间序列属性; 2)基于扩散过程的推论。区别并不明显。统计分析的重点一直放在建立精细的时间序列模型上(可能是在非均匀间隔的时间),或估计连续时间金融理论所隐含的数量,即噪声协方差。

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