An Ordinal Pattern Approach to Detect and to Model Leverage Effects and Dependence Structures Between Financial Time Series

Sequential Break-Point Detection in Stationary Time Series: An Application to Monitoring Economic Indicators

Monitoring economic conditions and financial stability with an early warning system serves as a prevention mechanism for unexpected economic events. In this paper, we investigate the statistical performance of sequential break-point detectors for stationary time series regression models with extensive simulation experiments. We employ an online sequential scheme for monitoring economic indicators from the European as well as the American financial markets that span the period during the 2008 financial crisis. Our results show that the performance of these tests applied to stationary time serie…

A multivariate model for financial indices and an algorithm for detection of jumps in the volatility

We consider a mean-reverting stochastic volatility model which satisfies some relevant stylized facts of financial markets. We introduce an algorithm for the detection of peaks in the volatility profile, that we apply to the time series of Dow Jones Industrial Average and Financial Times Stock Exchange 100 in the period 1984-2013. Based on empirical results, we propose a bivariate version of the model, for which we find an explicit expression for the decay over time of cross-asset correlations between absolute returns. We compare our theoretical predictions with empirical estimates on the same…

Towards Artificial Intelligence Enabled Financial Crime Detection

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to…

Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks

A major concern when dealing with financial time series involving a wide variety ofmarket risk factors is the presence of anomalies. These induce a miscalibration of the models used toquantify and manage risk, resulting in potential erroneous risk measures. We propose an approachthat aims to improve anomaly detection in financial time series, overcoming most of the inherentdifficulties. Valuable features are extracted from the time series by compressing and reconstructingthe data through principal component analysis. We then define an anomaly score using a feedforwardneural network. A time ser…

Domain Specific Concept Drift Detectors for Predicting Financial Time Series

Concept drift detectors allow learning systems to maintain good accuracy on non-stationary data streams. Financial time series are an instance of non-stationary data streams whose concept drifts (market phases) are so important to affect investment decisions worldwide. This paper studies how concept drift detectors behave when applied to financial time series. General results are: a) concept drift detectors usually improve the runtime over continuous learning, b) their computational cost is usually a fraction of the learning and prediction steps of even basic learners, c) it is important to st…

基于非参数累积量方法的多变量金融数据异常值检测

Non-parametric cumulants approach for outlier detection of multivariate financial data

本文提出基于最大化Cumulant Generating Function (CGF)投影的多变量数据异常值检测算法，证明其为标准主成分技术的扩展，并在人工与真实金融数据上验证有效性，最终应用于金融危机早期检测。

Change-point Analysis in Financial Networks

A major impact of globalization has been the information flow across the financial markets rendering them vulnerable to financial contagion. Research has focused on network analysis techniques to understand the extent and nature of such information flow. It is now an established fact that a stock market crash in one country can have a serious impact on other markets across the globe. It follows that such crashes or critical regimes will affect the network dynamics of the global financial markets. In this paper, we use sequential change point detection in dynamic networks to detect changes in t…

为什么拓扑数据分析能检测金融泡沫？

Why Topological Data Analysis Detects Financial Bubbles?

本文基于LPPLS模型给出启发式论证，解释TDA为何能检测金融时间序列中的临界转变早期预警信号，并以比特币正负泡沫为例进行验证。

TimeTrail：通过时间相关性分析揭示金融欺诈模式

TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysis

本研究提出金融欺诈检测技术TimeTrail，通过时间数据丰富化、动态相关性分析、可解释可视化三阶段，挖掘欺诈的时间关联模式，在准确性和可解释性上优于传统方法，具备实际应用价值。