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Full-Text Articles in Statistics and Probability
Deep Learning Analysis Of Limit Order Book, Xin Xu
Deep Learning Analysis Of Limit Order Book, Xin Xu
Arts & Sciences Electronic Theses and Dissertations
In this paper, we build a deep neural network for modeling spatial structure in limit order book and make prediction for future best ask or best bid price based on ideas of (Sirignano 2016). We propose an intuitive data processing method to approximate the data is non-available for us based only on level I data that is more widely available. The model is based on the idea that there is local dependence for best ask or best bid price and sizes of related orders. First we use logistic regression to prove that this approach is reasonable. To show the advantages …
Nonparametric Estimation Of Time Series Volatility Model Estimation, Teng Tu
Nonparametric Estimation Of Time Series Volatility Model Estimation, Teng Tu
Arts & Sciences Electronic Theses and Dissertations
In this article we consider two estimation methods of a non-parametric volatility model with autoregressive error of order two. The first estimation method based on the two- lag difference. To get a better result, we consider the second approach based on the general quadratic forms. For illustration, we provided several data sets from different simulation models to support the procedures of both two methods, and prove that the second approach can make a better estimation.
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
Arts & Sciences Electronic Theses and Dissertations
The general goal of this thesis is to investigate and examine some issues about post-selection inference which arises from the setting where statistical inference is carried out after a datadriven model selection step. In this setting, the classical inference theory which requires a fixed priori model becomes invalid since the selected model is a result of random event. Hence, a common practice in applied research which ignores the model selection and builds up confidence interval will result in misleading or even false conclusion. In this thesis, specifically, we first discusses some examples to show how the classical inference theory loses …