机器学习中的序贯假设检验与原油价格跳跃大小检测

Q3 Mathematics

Applied Mathematical Finance Pub Date : 2020-04-19 DOI:10.1080/1350486X.2020.1859943

Michael Roberts, I. Sengupta

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引用次数: 12

摘要

摘要本文提出了一种序贯假设检验，用于检测lsamvy过程中跳跃大小的分布。给出了相应对数似然比的无穷小发生器，并对其进行了分析。用超解和子解计算了无穷小发生器的界。这在原油价格数据集的各种分类问题中是可实现的。机器和深度学习算法用于从数据集中提取特定的确定性成分，并实现确定性成分来改进Barndorff-Nielsen & Shephard模型，这是一种用于衍生品和商品市场分析的常用随机模型。

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Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection

ABSTRACT In this paper, we present a sequential hypothesis test for the detection of the distribution of jump size in Lévy processes. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analysed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the data set, and the deterministic component is implemented to improve the Barndorff-Nielsen & Shephard model, a commonly used stochastic model for derivative and commodity market analysis.

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来源期刊

Applied Mathematical Finance Economics, Econometrics and Finance-Finance

CiteScore

2.30

自引率

0.00%

发文量

期刊介绍： The journal encourages the confident use of applied mathematics and mathematical modelling in finance. The journal publishes papers on the following: •modelling of financial and economic primitives (interest rates, asset prices etc); •modelling market behaviour; •modelling market imperfections; •pricing of financial derivative securities; •hedging strategies; •numerical methods; •financial engineering.