Macroscopic properties of equity markets affect the performance of active equity strategies but many are not adequately captured by conventional models of financial mathematics and econometrics. Using the CRSP Database of the US equity market, we study empirically several macroscopic properties defined in terms of market capitalizations and returns, and highlight a list of stylized facts and open questions motivated in part by stochastic portfolio theory. Additionally, we present a systematic backtest of the diversity-weighted portfolio under various configurations and study its performance in relation to macroscopic quantities. All of our results can be replicated using codes made available on our GitHub repository.
{"title":"Macroscopic properties of equity markets: stylized facts and portfolio performance","authors":"Steven Campbell, Qien Song, Ting-Kam Leonard Wong","doi":"arxiv-2409.10859","DOIUrl":"https://doi.org/arxiv-2409.10859","url":null,"abstract":"Macroscopic properties of equity markets affect the performance of active\u0000equity strategies but many are not adequately captured by conventional models\u0000of financial mathematics and econometrics. Using the CRSP Database of the US\u0000equity market, we study empirically several macroscopic properties defined in\u0000terms of market capitalizations and returns, and highlight a list of stylized\u0000facts and open questions motivated in part by stochastic portfolio theory.\u0000Additionally, we present a systematic backtest of the diversity-weighted\u0000portfolio under various configurations and study its performance in relation to\u0000macroscopic quantities. All of our results can be replicated using codes made\u0000available on our GitHub repository.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"212 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268157","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}
Wasim Ahmad, Mohammad Arshad Rahman, Suruchi Shrimali, Preeti Roy
This article develops multiple novel climate risk measures (or variables) based on the television news coverage by Bloomberg, CNBC, and Fox Business, and examines how they affect the systematic and idiosyncratic risks of clean energy firms in the United States (US). The measures are built on climate related keywords and cover the volume of coverage, type of coverage (climate crisis, renewable energy, and government and human initiatives), and media sentiments. We show that an increase in the aggregate measure of climate risk, as indicated by coverage volume, reduces idiosyncratic risk while increasing systematic risk. When climate risk is segregated, we find that systematic risk is positively affected by the textit{physical risk} of climate crises and textit{transition risk} from government and human initiatives, but no such impact is evident for idiosyncratic risk. Additionally, we observe an asymmetry in risk behavior: negative sentiments tend to increase idiosyncratic risk and decrease systematic risk, while positive sentiments have no significant impact. This asymmetry persists even when considering print media variables, climate policy uncertainty, and analysis based on the COVID-19 period.
{"title":"Tuning into Climate Risks: Extracting Innovation from TV News for Clean Energy Firms","authors":"Wasim Ahmad, Mohammad Arshad Rahman, Suruchi Shrimali, Preeti Roy","doi":"arxiv-2409.08701","DOIUrl":"https://doi.org/arxiv-2409.08701","url":null,"abstract":"This article develops multiple novel climate risk measures (or variables)\u0000based on the television news coverage by Bloomberg, CNBC, and Fox Business, and\u0000examines how they affect the systematic and idiosyncratic risks of clean energy\u0000firms in the United States (US). The measures are built on climate related\u0000keywords and cover the volume of coverage, type of coverage (climate crisis,\u0000renewable energy, and government and human initiatives), and media sentiments.\u0000We show that an increase in the aggregate measure of climate risk, as indicated\u0000by coverage volume, reduces idiosyncratic risk while increasing systematic\u0000risk. When climate risk is segregated, we find that systematic risk is\u0000positively affected by the textit{physical risk} of climate crises and\u0000textit{transition risk} from government and human initiatives, but no such\u0000impact is evident for idiosyncratic risk. Additionally, we observe an asymmetry\u0000in risk behavior: negative sentiments tend to increase idiosyncratic risk and\u0000decrease systematic risk, while positive sentiments have no significant impact.\u0000This asymmetry persists even when considering print media variables, climate\u0000policy uncertainty, and analysis based on the COVID-19 period.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250013","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}
This paper examines the influence of low-frequency macroeconomic variables on the high-frequency returns of copper futures and the long-term correlation with the S&P 500 index, employing GARCH-MIDAS and DCC-MIDAS modeling frameworks. The estimated results of GARCH-MIDAS show that realized volatility (RV), level of interest rates (IR), industrial production (IP) and producer price index (PPI), volatility of Slope, PPI, consumer sentiment index (CSI), and dollar index (DI) have significant impacts on Copper futures returns, among which PPI is the most efficient macroeconomic variable. From comparison among DCC-GARCH and DCC-MIDAS model, the added MIDAS filter of PPI improves the model fitness and have better performance than RV in effecting the long-run relationship between Copper futures and S&P 500.
{"title":"On the macroeconomic fundamentals of long-term volatilities and dynamic correlations in COMEX copper futures","authors":"Zian Wang, Xinshu Li","doi":"arxiv-2409.08355","DOIUrl":"https://doi.org/arxiv-2409.08355","url":null,"abstract":"This paper examines the influence of low-frequency macroeconomic variables on\u0000the high-frequency returns of copper futures and the long-term correlation with\u0000the S&P 500 index, employing GARCH-MIDAS and DCC-MIDAS modeling frameworks. The\u0000estimated results of GARCH-MIDAS show that realized volatility (RV), level of\u0000interest rates (IR), industrial production (IP) and producer price index (PPI),\u0000volatility of Slope, PPI, consumer sentiment index (CSI), and dollar index (DI)\u0000have significant impacts on Copper futures returns, among which PPI is the most\u0000efficient macroeconomic variable. From comparison among DCC-GARCH and DCC-MIDAS\u0000model, the added MIDAS filter of PPI improves the model fitness and have better\u0000performance than RV in effecting the long-run relationship between Copper\u0000futures and S&P 500.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250017","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}
The Fractional Stochastic Regularity Model (FSRM) is an extension of Black-Scholes model describing the multifractal nature of prices. It is based on a multifractional process with a random Hurst exponent $H_t$, driven by a fractional Ornstein-Uhlenbeck (fOU) process. When the regularity parameter $H_t$ is equal to $1/2$, the efficient market hypothesis holds, but when $H_tneq 1/2$ past price returns contain some information on a future trend or mean-reversion of the log-price process. In this paper, we investigate some properties of the fOU process and, thanks to information theory and Shannon's entropy, we determine theoretically the serial information of the regularity process $H_t$ of the FSRM, giving some insight into one's ability to forecast future price increments and to build statistical arbitrages with this model.
分数随机正则模型（FSRM）是布莱克-斯科尔斯（Black-Scholes）模型的扩展，描述了价格的多分性。它基于一个具有随机赫斯特指数 $H_t$ 的多分形过程，由分形奥恩斯坦-乌伦贝克（fOU）过程驱动。当规律性参数$H_t$等于1/2$时，有效市场假说成立，但当$H_t/neq为1/2$时，过去的价格回报包含了对数价格过程未来趋势或均值反转的一些信息。在本文中，我们研究了 fOU 过程的一些特性，并借助信息论和香农熵，从理论上确定了 FSRM 的正则过程 $H_t$ 的序列信息，从而对预测未来价格增量和利用该模型建立统计套利的能力有了一定的了解。
{"title":"Market information of the fractional stochastic regularity model","authors":"Daniele Angelini, Matthieu Garcin","doi":"arxiv-2409.07159","DOIUrl":"https://doi.org/arxiv-2409.07159","url":null,"abstract":"The Fractional Stochastic Regularity Model (FSRM) is an extension of\u0000Black-Scholes model describing the multifractal nature of prices. It is based\u0000on a multifractional process with a random Hurst exponent $H_t$, driven by a\u0000fractional Ornstein-Uhlenbeck (fOU) process. When the regularity parameter\u0000$H_t$ is equal to $1/2$, the efficient market hypothesis holds, but when\u0000$H_tneq 1/2$ past price returns contain some information on a future trend or\u0000mean-reversion of the log-price process. In this paper, we investigate some\u0000properties of the fOU process and, thanks to information theory and Shannon's\u0000entropy, we determine theoretically the serial information of the regularity\u0000process $H_t$ of the FSRM, giving some insight into one's ability to forecast\u0000future price increments and to build statistical arbitrages with this model.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190908","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}
Conformal field theories with central charge $cle1$ on random surfaces have been extensively studied in the past. Here, this discussion is extended from their equilibrium distribution to their critical dynamics. This is motivated by the conjecture that these models describe the time evolution of certain social networks that are self-driven to a critical point. The time evolution of the surface area is identified as a Cox Ingersol Ross process. Planar surfaces shrink, while higher genus surfaces grow until the cosmological constant stops their growth. Three different equilibrium states are distingushed, dominated by (i) small planar surfaces, (ii) large surfaces with high but finite genus, and (iii) foamy surfaces, whose genus diverges. Time variations of the order parameter are analyzed and are found to have generalized hyperbolic distributions. In state (i), those have power law tails with a tail index close to 4. Analogies between the time evolution of the order parameter and a multifractal random walk are also pointed out.
{"title":"Critical Dynamics of Random Surfaces","authors":"Christof Schmidhuber","doi":"arxiv-2409.05547","DOIUrl":"https://doi.org/arxiv-2409.05547","url":null,"abstract":"Conformal field theories with central charge $cle1$ on random surfaces have\u0000been extensively studied in the past. Here, this discussion is extended from\u0000their equilibrium distribution to their critical dynamics. This is motivated by\u0000the conjecture that these models describe the time evolution of certain social\u0000networks that are self-driven to a critical point. The time evolution of the\u0000surface area is identified as a Cox Ingersol Ross process. Planar surfaces\u0000shrink, while higher genus surfaces grow until the cosmological constant stops\u0000their growth. Three different equilibrium states are distingushed, dominated by\u0000(i) small planar surfaces, (ii) large surfaces with high but finite genus, and\u0000(iii) foamy surfaces, whose genus diverges. Time variations of the order\u0000parameter are analyzed and are found to have generalized hyperbolic\u0000distributions. In state (i), those have power law tails with a tail index close\u0000to 4. Analogies between the time evolution of the order parameter and a\u0000multifractal random walk are also pointed out.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190911","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}
Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani
In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends.
{"title":"Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement","authors":"Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani","doi":"arxiv-2409.08297","DOIUrl":"https://doi.org/arxiv-2409.08297","url":null,"abstract":"In recent years, financial analysts have been trying to develop models to\u0000predict the movement of a stock price index. The task becomes challenging in\u0000vague economic, social, and political situations like in Pakistan. In this\u0000study, we employed efficient models of machine learning such as long short-term\u0000memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi\u0000Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic,\u0000social, political, and administrative indicators from February 2004 to December\u00002020. The comparative results of LSTM and QLSTM predicted values of the KSE 100\u0000index with the actual values suggested QLSTM a potential technique to predict\u0000stock market trends.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250015","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}
The Foreign Exchange market is a significant market for speculators, characterized by substantial transaction volumes and high volatility. Accurately predicting the directional movement of currency pairs is essential for formulating a sound financial investment strategy. This paper conducts a comparative analysis of various machine learning models for predicting the daily directional movement of the EUR/USD currency pair in the Foreign Exchange market. The analysis includes both decorrelated and non-decorrelated feature sets using Principal Component Analysis. Additionally, this study explores meta-estimators, which involve stacking multiple estimators as input for another estimator, aiming to achieve improved predictive performance. Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day ahead forecasts, coupled with an annual return of 32.48% for the year 2022.
{"title":"Predicting Foreign Exchange EUR/USD direction using machine learning","authors":"Kevin Cedric Guyard, Michel Deriaz","doi":"arxiv-2409.04471","DOIUrl":"https://doi.org/arxiv-2409.04471","url":null,"abstract":"The Foreign Exchange market is a significant market for speculators,\u0000characterized by substantial transaction volumes and high volatility.\u0000Accurately predicting the directional movement of currency pairs is essential\u0000for formulating a sound financial investment strategy. This paper conducts a\u0000comparative analysis of various machine learning models for predicting the\u0000daily directional movement of the EUR/USD currency pair in the Foreign Exchange\u0000market. The analysis includes both decorrelated and non-decorrelated feature\u0000sets using Principal Component Analysis. Additionally, this study explores\u0000meta-estimators, which involve stacking multiple estimators as input for\u0000another estimator, aiming to achieve improved predictive performance.\u0000Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day\u0000ahead forecasts, coupled with an annual return of 32.48% for the year 2022.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190913","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}
This study aims to map the scientific production on the European Union Emissions Trading System (EU ETS) market from 2004 to 2024. By analyzing research articles collected from the Scopus database, this bibliometric review provides a comprehensive overview of the academic landscape surrounding the EU ETS. Utilizing the Bibliometrix package in R, we conducted an in-depth analysis of publication trends, key research themes, influential authors, and prominent journals in the field. Our findings reveal significant growth in scholarly interest, with notable peaks corresponding to major policy updates and economic events. The analysis highlights the most cited works and collaborative networks, offering insights into the evolution of research topics over the past two decades. This review serves as a valuable resource for researchers and policymakers, providing a detailed understanding of the academic contributions to the EU ETS market and identifying emerging trends and gaps in the literature. Through this bibliometric approach, we offer a nuanced perspective on the development and impact of the EU ETS in the context of global carbon markets and climate policy.
{"title":"Evolving Dynamics: Bibliometric Insights into the Economics of the EU ETS Market","authors":"Cristiano Salvagnin","doi":"arxiv-2409.01739","DOIUrl":"https://doi.org/arxiv-2409.01739","url":null,"abstract":"This study aims to map the scientific production on the European Union\u0000Emissions Trading System (EU ETS) market from 2004 to 2024. By analyzing\u0000research articles collected from the Scopus database, this bibliometric review\u0000provides a comprehensive overview of the academic landscape surrounding the EU\u0000ETS. Utilizing the Bibliometrix package in R, we conducted an in-depth analysis\u0000of publication trends, key research themes, influential authors, and prominent\u0000journals in the field. Our findings reveal significant growth in scholarly\u0000interest, with notable peaks corresponding to major policy updates and economic\u0000events. The analysis highlights the most cited works and collaborative\u0000networks, offering insights into the evolution of research topics over the past\u0000two decades. This review serves as a valuable resource for researchers and\u0000policymakers, providing a detailed understanding of the academic contributions\u0000to the EU ETS market and identifying emerging trends and gaps in the\u0000literature. Through this bibliometric approach, we offer a nuanced perspective\u0000on the development and impact of the EU ETS in the context of global carbon\u0000markets and climate policy.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190934","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}
The Kullback-Leibler cluster entropy $mathcal{D_{C}}[P | Q] $ is evaluated for the empirical and model probability distributions $P$ and $Q$ of the clusters formed in the realized volatility time series of five assets (SP&500, NASDAQ, DJIA, DAX, FTSEMIB). The Kullback-Leibler functional $mathcal{D_{C}}[P | Q] $ provides complementary perspectives about the stochastic volatility process compared to the Shannon functional $mathcal{S_{C}}[P]$. While $mathcal{D_{C}}[P | Q] $ is maximum at the short time scales, $mathcal{S_{C}}[P]$ is maximum at the large time scales leading to complementary optimization criteria tracing back respectively to the maximum and minimum relative entropy evolution principles. The realized volatility is modelled as a time-dependent fractional stochastic process characterized by power-law decaying distributions with positive correlation ($H>1/2$). As a case study, a multiperiod portfolio built on diversity indexes derived from the Kullback-Leibler entropy measure of the realized volatility. The portfolio is robust and exhibits better performances over the horizon periods. A comparison with the portfolio built either according to the uniform distribution or in the framework of the Markowitz theory is also reported.
{"title":"Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity","authors":"L. Ponta, A. Carbone","doi":"arxiv-2409.10543","DOIUrl":"https://doi.org/arxiv-2409.10543","url":null,"abstract":"The Kullback-Leibler cluster entropy $mathcal{D_{C}}[P | Q] $ is evaluated\u0000for the empirical and model probability distributions $P$ and $Q$ of the\u0000clusters formed in the realized volatility time series of five assets (SP&500,\u0000NASDAQ, DJIA, DAX, FTSEMIB). The Kullback-Leibler functional $mathcal{D_{C}}[P\u0000| Q] $ provides complementary perspectives about the stochastic volatility\u0000process compared to the Shannon functional $mathcal{S_{C}}[P]$. While\u0000$mathcal{D_{C}}[P | Q] $ is maximum at the short time scales,\u0000$mathcal{S_{C}}[P]$ is maximum at the large time scales leading to\u0000complementary optimization criteria tracing back respectively to the maximum\u0000and minimum relative entropy evolution principles. The realized volatility is\u0000modelled as a time-dependent fractional stochastic process characterized by\u0000power-law decaying distributions with positive correlation ($H>1/2$). As a case\u0000study, a multiperiod portfolio built on diversity indexes derived from the\u0000Kullback-Leibler entropy measure of the realized volatility. The portfolio is\u0000robust and exhibits better performances over the horizon periods. A comparison\u0000with the portfolio built either according to the uniform distribution or in the\u0000framework of the Markowitz theory is also reported.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250016","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}
Peilun He, Gareth W. Peters, Nino Kordzakhiac, Pavel V. Shevchenko
The Nelson-Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope, and curvature dynamics of the yield curves. In this study, we present a novel state-space functional regression model that incorporates a dynamic Nelson-Siegel model and functional regression formulations applied to multi-economy setting. This framework offers distinct advantages in explaining the relative spreads in yields between a reference economy and a response economy. To address the inherent challenges of model calibration, a kernel principal component analysis is employed to transform the representation of functional regression into a finite-dimensional, tractable estimation problem. A comprehensive empirical analysis is conducted to assess the efficacy of the functional regression approach, including an in-sample performance comparison with the dynamic Nelson-Siegel model. We conducted the stress testing analysis of yield curves term-structure within a dual economy framework. The bond ladder portfolio was examined through a case study focused on spread modelling using historical data for US Treasury and UK bonds.
{"title":"State-Space Dynamic Functional Regression for Multicurve Fixed Income Spread Analysis and Stress Testing","authors":"Peilun He, Gareth W. Peters, Nino Kordzakhiac, Pavel V. Shevchenko","doi":"arxiv-2409.00348","DOIUrl":"https://doi.org/arxiv-2409.00348","url":null,"abstract":"The Nelson-Siegel model is widely used in fixed income markets to produce\u0000yield curve dynamics. The multiple time-dependent parameter model conveniently\u0000addresses the level, slope, and curvature dynamics of the yield curves. In this\u0000study, we present a novel state-space functional regression model that\u0000incorporates a dynamic Nelson-Siegel model and functional regression\u0000formulations applied to multi-economy setting. This framework offers distinct\u0000advantages in explaining the relative spreads in yields between a reference\u0000economy and a response economy. To address the inherent challenges of model\u0000calibration, a kernel principal component analysis is employed to transform the\u0000representation of functional regression into a finite-dimensional, tractable\u0000estimation problem. A comprehensive empirical analysis is conducted to assess\u0000the efficacy of the functional regression approach, including an in-sample\u0000performance comparison with the dynamic Nelson-Siegel model. We conducted the\u0000stress testing analysis of yield curves term-structure within a dual economy\u0000framework. The bond ladder portfolio was examined through a case study focused\u0000on spread modelling using historical data for US Treasury and UK bonds.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190912","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}
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