arXiv - QuantFin - Statistical Finance最新文献_第5页

The Theory of Intrinsic Time: A Primer 内在时间理论：入门指南

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-11 DOI: arxiv-2406.07354

James B. Glattfelder, Richard B. Olsen

{"title":"The Theory of Intrinsic Time: A Primer","authors":"James B. Glattfelder, Richard B. Olsen","doi":"arxiv-2406.07354","DOIUrl":"https://doi.org/arxiv-2406.07354","url":null,"abstract":"The concept of time mostly plays a subordinate role in finance and economics.\u0000The assumption is that time flows continuously and that time series data should\u0000be analyzed at regular, equidistant intervals. Nonetheless, already nearly 60\u0000years ago, the concept of an event-based measure of time was first introduced.\u0000This paper expands on this theme by discussing the paradigm of intrinsic time,\u0000its origins, history, and modern applications. Departing from traditional,\u0000continuous measures of time, intrinsic time proposes an event-based,\u0000algorithmic framework that captures the dynamic and fluctuating nature of\u0000real-world phenomena more accurately. Unsuspected implications arise in general\u0000for complex systems and specifically for financial markets. For instance, novel\u0000structures and regularities are revealed, otherwise obscured by any analysis\u0000utilizing equidistant time intervals. Of particular interest is the emergence\u0000of a multiplicity of scaling laws, a hallmark signature of an underlying\u0000organizational principle in complex systems. Moreover, a central insight from\u0000this novel paradigm is the realization that universal time does not exist;\u0000instead, time is observer-dependent, shaped by the intrinsic activity unfolding\u0000within complex systems. This research opens up new avenues for economic\u0000modeling and forecasting, paving the way for a deeper understanding of the\u0000invisible forces that guide the evolution and emergence of market dynamics and\u0000financial systems. An exciting and rich landscape of possibilities emerges\u0000within the paradigm of intrinsic time.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dissecting Multifractal detrended cross-correlation analysis 剖析多分形去趋势交叉相关分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-09 DOI: arxiv-2406.19406

Borko Stosic, Tatijana Stosic

引用次数: 0

Investigating the price determinants of the European Emission Trading System: a non-parametric approach 调查欧洲排放交易体系的价格决定因素：一种非参数方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-07 DOI: arxiv-2406.05094

Cristiano Salvagnin, Aldo Glielmo, Maria Elena De Giuli, Antonietta Mira

{"title":"Investigating the price determinants of the European Emission Trading System: a non-parametric approach","authors":"Cristiano Salvagnin, Aldo Glielmo, Maria Elena De Giuli, Antonietta Mira","doi":"arxiv-2406.05094","DOIUrl":"https://doi.org/arxiv-2406.05094","url":null,"abstract":"The European carbon market plays a pivotal role in the European Union's\u0000ambitious target of achieving carbon neutrality by 2050. Understanding the\u0000intricacies of factors influencing European Union Emission Trading System (EU\u0000ETS) market prices is paramount for effective policy making and strategy\u0000implementation. We propose the use of the Information Imbalance, a recently\u0000introduced non-parametric measure quantifying the degree to which a set of\u0000variables is informative with respect to another one, to study the\u0000relationships among macroeconomic, economic, uncertainty, and energy variables\u0000concerning EU ETS prices. Our analysis shows that in Phase 3 commodity related\u0000variables such as the ERIX index are the most informative to explain the\u0000behaviour of the EU ETS market price. Transitioning to Phase 4, financial\u0000fluctuations take centre stage, with the uncertainty in the EUR/CHF exchange\u0000rate emerging as a crucial determinant. These results reflect the disruptive\u0000impacts of the COVID-19 pandemic and the energy crisis in reshaping the\u0000importance of the different variables. Beyond variable analysis, we also\u0000propose to leverage the Information Imbalance to address the problem of\u0000mixed-frequency forecasting, and we identify the weekly time scale as the most\u0000informative for predicting the EU ETS price. Finally, we show how the\u0000Information Imbalance can be effectively combined with Gaussian Process\u0000regression for efficient nowcasting and forecasting using very small sets of\u0000highly informative predictors.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal distribution of clusters of investors and their application in prediction with expert advice 投资者集群的时间分布及其在专家建议预测中的应用

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-04 DOI: arxiv-2406.19403

Wojciech Wisniewski, Yuri Kalnishkan, David Lindsay, Siân Lindsay

{"title":"Temporal distribution of clusters of investors and their application in prediction with expert advice","authors":"Wojciech Wisniewski, Yuri Kalnishkan, David Lindsay, Siân Lindsay","doi":"arxiv-2406.19403","DOIUrl":"https://doi.org/arxiv-2406.19403","url":null,"abstract":"Financial organisations such as brokers face a significant challenge in\u0000servicing the investment needs of thousands of their traders worldwide. This\u0000task is further compounded since individual traders will have their own risk\u0000appetite and investment goals. Traders may look to capture short-term trends in\u0000the market which last only seconds to minutes, or they may have longer-term\u0000views which last several days to months. To reduce the complexity of this task,\u0000client trades can be clustered. By examining such clusters, we would likely\u0000observe many traders following common patterns of investment, but how do these\u0000patterns vary through time? Knowledge regarding the temporal distributions of\u0000such clusters may help financial institutions manage the overall portfolio of\u0000risk that accumulates from underlying trader positions. This study contributes\u0000to the field by demonstrating that the distribution of clusters derived from\u0000the real-world trades of 20k Foreign Exchange (FX) traders (from 2015 to 2017)\u0000is described in accordance with Ewens' Sampling Distribution. Further, we show\u0000that the Aggregating Algorithm (AA), an on-line prediction with expert advice\u0000algorithm, can be applied to the aforementioned real-world data in order to\u0000improve the returns of portfolios of trader risk. However we found that the AA\u0000'struggles' when presented with too many trader ``experts'', especially when\u0000there are many trades with similar overall patterns. To help overcome this\u0000challenge, we have applied and compared the use of Statistically Validated\u0000Networks (SVN) with a hierarchical clustering approach on a subset of the data,\u0000demonstrating that both approaches can be used to significantly improve results\u0000of the AA in terms of profitability and smoothness of returns.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Methods for Pricing Financial Derivatives 金融衍生品定价的机器学习方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-01 DOI: arxiv-2406.00459

Lei Fan, Justin Sirignano

{"title":"Machine Learning Methods for Pricing Financial Derivatives","authors":"Lei Fan, Justin Sirignano","doi":"arxiv-2406.00459","DOIUrl":"https://doi.org/arxiv-2406.00459","url":null,"abstract":"Stochastic differential equation (SDE) models are the foundation for pricing\u0000and hedging financial derivatives. The drift and volatility functions in SDE\u0000models are typically chosen to be algebraic functions with a small number (less\u0000than 5) parameters which can be calibrated to market data. A more flexible\u0000approach is to use neural networks to model the drift and volatility functions,\u0000which provides more degrees-of-freedom to match observed market data. Training\u0000of models requires optimizing over an SDE, which is computationally\u0000challenging. For European options, we develop a fast stochastic gradient\u0000descent (SGD) algorithm for training the neural network-SDE model. Our SGD\u0000algorithm uses two independent SDE paths to obtain an unbiased estimate of the\u0000direction of steepest descent. For American options, we optimize over the\u0000corresponding Kolmogorov partial differential equation (PDE). The neural\u0000network appears as coefficient functions in the PDE. Models are trained on\u0000large datasets (many contracts), requiring either large simulations (many Monte\u0000Carlo samples for the stock price paths) or large numbers of PDEs (a PDE must\u0000be solved for each contract). Numerical results are presented for real market\u0000data including S&P 500 index options, S&P 100 index options, and single-stock\u0000American options. The neural-network-based SDE models are compared against the\u0000Black-Scholes model, the Dupire's local volatility model, and the Heston model.\u0000Models are evaluated in terms of how accurate they are at pricing out-of-sample\u0000financial derivatives, which is a core task in derivative pricing at financial\u0000institutions.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141258807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Extreme Events in the Stock Market: A Topological Data Analysis 识别股市极端事件：拓扑数据分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-25 DOI: arxiv-2405.16052

Anish Rai, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman, Sushovan Majhi

{"title":"Identifying Extreme Events in the Stock Market: A Topological Data Analysis","authors":"Anish Rai, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman, Sushovan Majhi","doi":"arxiv-2405.16052","DOIUrl":"https://doi.org/arxiv-2405.16052","url":null,"abstract":"This paper employs Topological Data Analysis (TDA) to detect extreme events\u0000(EEs) in the stock market at a continental level. Previous approaches, which\u0000analyzed stock indices separately, could not detect EEs for multiple time\u0000series in one go. TDA provides a robust framework for such analysis and\u0000identifies the EEs during the crashes for different indices. The TDA analysis\u0000shows that $L^1$, $L^2$ norms and Wasserstein distance ($W_D$) of the world\u0000leading indices rise abruptly during the crashes, surpassing a threshold of\u0000$mu+4*sigma$ where $mu$ and $sigma$ are the mean and the standard deviation\u0000of norm or $W_D$, respectively. Our study identified the stock index crashes of\u0000the 2008 financial crisis and the COVID-19 pandemic across continents as EEs.\u0000Given that different sectors in an index behave differently, a sector-wise\u0000analysis was conducted during the COVID-19 pandemic for the Indian stock\u0000market. The sector-wise results show that after the occurrence of EE, we have\u0000observed strong crashes surpassing $mu+2*sigma$ for an extended period for\u0000the banking sector. While for the pharmaceutical sector, no significant spikes\u0000were noted. Hence, TDA also proves successful in identifying the duration of\u0000shocks after the occurrence of EEs. This also indicates that the Banking sector\u0000continued to face stress and remained volatile even after the crash. This study\u0000gives us the applicability of TDA as a powerful analytical tool to study EEs in\u0000various fields.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An empirical study of market risk factors for Bitcoin 比特币市场风险因素实证研究

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-24 DOI: arxiv-2406.19401

Shubham Singh

引用次数: 0

Decision Trees for Intuitive Intraday Trading Strategies 直观日内交易策略的决策树

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-22 DOI: arxiv-2405.13959

Prajwal Naga, Dinesh Balivada, Sharath Chandra Nirmala, Poornoday Tiruveedi

引用次数: 0

Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach 预测金融机构服务中的客户目标：数据驱动的 LSTM 方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-22 DOI: arxiv-2406.19399

Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo, Rui Silva

引用次数: 0

A K-means Algorithm for Financial Market Risk Forecasting 用于金融市场风险预测的 K-means 算法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-21 DOI: arxiv-2405.13076

Jinxin Xu, Kaixian Xu, Yue Wang, Qinyan Shen, Ruisi Li

引用次数: 0