In this paper, we introduce a doubly doubly robust estimator for the average and heterogeneous treatment effect for left-truncated-right-censored (LTRC) survival data. In causal inference for survival functions in LTRC survival data, two missing data issues are noteworthy: one is the missing data of counterfactuals for causal inference, and the other is the missing data due to truncation and censoring. Based on previous research on non-parametric deep learning estimation in survival analysis, this paper proposes an algorithm to obtain an efficient estimate of the average and heterogeneous causal effect. We simulate the data and compare our methods with the marginal hazard ratio estimation, the naive plug-in estimation, and the doubly robust causal with Cox Proportional Hazard estimation and illustrate the advantages and disadvantages of the model application.
{"title":"Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator","authors":"Guanghui Pan","doi":"arxiv-2409.01412","DOIUrl":"https://doi.org/arxiv-2409.01412","url":null,"abstract":"In this paper, we introduce a doubly doubly robust estimator for the average\u0000and heterogeneous treatment effect for left-truncated-right-censored (LTRC)\u0000survival data. In causal inference for survival functions in LTRC survival\u0000data, two missing data issues are noteworthy: one is the missing data of\u0000counterfactuals for causal inference, and the other is the missing data due to\u0000truncation and censoring. Based on previous research on non-parametric deep\u0000learning estimation in survival analysis, this paper proposes an algorithm to\u0000obtain an efficient estimate of the average and heterogeneous causal effect. We\u0000simulate the data and compare our methods with the marginal hazard ratio\u0000estimation, the naive plug-in estimation, and the doubly robust causal with Cox\u0000Proportional Hazard estimation and illustrate the advantages and disadvantages\u0000of the model application.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"2012 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192977","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}
Strategic shrouding of taxes by profit-maximizing firms can impair the effectiveness of corrective taxes. This paper explores tax shrouding and its consequences after the introduction of a digital sin tax designed to discourage harmful overconsumption of online sports betting in Germany. In response to the tax reform, most firms strategically shroud the tax, i.e., exclude tax surcharges from posted prices. Using an extensive novel panel data set on online betting odds, I causally estimate the effect of the tax on consumer betting prices. Consumers bear, on average, 76% of the tax burden. There is considerable and long-lasting heterogeneity in effects conditional on shrouding practices. Firms that shroud taxes can pass 90% of the tax onto consumers, while the pass-through rate is 16% for firms that directly post tax-inclusive prices. To understand the results' underlying mechanisms and policy implications, I propose an optimal corrective taxation model where oligopolistic firms compete on base prices and can shroud additive taxes. Tax shrouding is only attainable in equilibrium if (some) consumers underreact to shrouded attributes. According to the theoretical predictions, the empirically identified heterogeneity suggests that strategic tax shrouding significantly attenuates the positive corrective welfare effects of the tax. The results prompt regulating shrouding practices in the context of corrective taxation.
{"title":"Shrouded Sin Taxes","authors":"Johannes Kasinger","doi":"arxiv-2409.01493","DOIUrl":"https://doi.org/arxiv-2409.01493","url":null,"abstract":"Strategic shrouding of taxes by profit-maximizing firms can impair the\u0000effectiveness of corrective taxes. This paper explores tax shrouding and its\u0000consequences after the introduction of a digital sin tax designed to discourage\u0000harmful overconsumption of online sports betting in Germany. In response to the\u0000tax reform, most firms strategically shroud the tax, i.e., exclude tax\u0000surcharges from posted prices. Using an extensive novel panel data set on\u0000online betting odds, I causally estimate the effect of the tax on consumer\u0000betting prices. Consumers bear, on average, 76% of the tax burden. There is\u0000considerable and long-lasting heterogeneity in effects conditional on shrouding\u0000practices. Firms that shroud taxes can pass 90% of the tax onto consumers,\u0000while the pass-through rate is 16% for firms that directly post tax-inclusive\u0000prices. To understand the results' underlying mechanisms and policy\u0000implications, I propose an optimal corrective taxation model where\u0000oligopolistic firms compete on base prices and can shroud additive taxes. Tax\u0000shrouding is only attainable in equilibrium if (some) consumers underreact to\u0000shrouded attributes. According to the theoretical predictions, the empirically\u0000identified heterogeneity suggests that strategic tax shrouding significantly\u0000attenuates the positive corrective welfare effects of the tax. The results\u0000prompt regulating shrouding practices in the context of corrective taxation.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192976","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}
When it comes to structural estimation of risk preferences from data on choices, random utility models have long been one of the standard research tools in economics. A recent literature has challenged these models, pointing out some concerning monotonicity and, thus, identification problems. In this paper, we take a second look and point out that some of the criticism - while extremely valid - may have gone too far, demanding monotonicity of choice probabilities in decisions where it is not so clear whether it should be imposed. We introduce a new class of random utility models based on carefully constructed generalized risk premia which always satisfy our relaxed monotonicity criteria. Moreover, we show that some of the models used in applied research like the certainty-equivalent-based random utility model for CARA utility actually lie in this class of monotonic stochastic choice models. We conclude that not all random utility models are bad.
{"title":"Stochastic Monotonicity and Random Utility Models: The Good and The Ugly","authors":"Henk Keffert, Nikolaus Schweizer","doi":"arxiv-2409.00704","DOIUrl":"https://doi.org/arxiv-2409.00704","url":null,"abstract":"When it comes to structural estimation of risk preferences from data on\u0000choices, random utility models have long been one of the standard research\u0000tools in economics. A recent literature has challenged these models, pointing\u0000out some concerning monotonicity and, thus, identification problems. In this\u0000paper, we take a second look and point out that some of the criticism - while\u0000extremely valid - may have gone too far, demanding monotonicity of choice\u0000probabilities in decisions where it is not so clear whether it should be\u0000imposed. We introduce a new class of random utility models based on carefully\u0000constructed generalized risk premia which always satisfy our relaxed\u0000monotonicity criteria. Moreover, we show that some of the models used in\u0000applied research like the certainty-equivalent-based random utility model for\u0000CARA utility actually lie in this class of monotonic stochastic choice models.\u0000We conclude that not all random utility models are bad.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193020","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}
Dynamic Stochastic General Equilibrium (DSGE) models, which are nowadays a crucial element of the set of quantitative tools that policy-makers have, did not emerge spontaneously. They rely on previously established ideas in Economics and relatively recent advancements in Mathematics. I aim to provide a comprehensive coverage of their history, starting from the pioneering Neoclassical general equilibrium theories and eventually reaching the New Neoclassical Synthesis (NNS). I thoroughly present the mathematical tools involved in formulating a DSGE model. I claim that this history has a mixed nature rather than an absolutist or relativist one, that the NNS may have emerged due to the complementary nature of New Classical and New Keynesian theories, and that the recent adoption and development of DSGE models by central banks from different countries has entailed a departure from the goal of building a universally valid theory that Economics has always had. The latter means that DSGE modeling has landed not without loss of generality.
{"title":"An essay on the history of DSGE models","authors":"Genaro Martín Damiani","doi":"arxiv-2409.00812","DOIUrl":"https://doi.org/arxiv-2409.00812","url":null,"abstract":"Dynamic Stochastic General Equilibrium (DSGE) models, which are nowadays a\u0000crucial element of the set of quantitative tools that policy-makers have, did\u0000not emerge spontaneously. They rely on previously established ideas in\u0000Economics and relatively recent advancements in Mathematics. I aim to provide a\u0000comprehensive coverage of their history, starting from the pioneering\u0000Neoclassical general equilibrium theories and eventually reaching the New\u0000Neoclassical Synthesis (NNS). I thoroughly present the mathematical tools\u0000involved in formulating a DSGE model. I claim that this history has a mixed\u0000nature rather than an absolutist or relativist one, that the NNS may have\u0000emerged due to the complementary nature of New Classical and New Keynesian\u0000theories, and that the recent adoption and development of DSGE models by\u0000central banks from different countries has entailed a departure from the goal\u0000of building a universally valid theory that Economics has always had. The\u0000latter means that DSGE modeling has landed not without loss of generality.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193016","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 emergence of new and disruptive technologies makes the economy and labor market more unstable. To overcome this kind of uncertainty and to make the labor market more comprehensible, we must employ labor market intelligence techniques, which are predominantly based on data analysis. Companies use job posting sites to advertise their job vacancies, known as online job vacancies (OJVs). LinkedIn is one of the most utilized websites for matching the supply and demand sides of the labor market; companies post their job vacancies on their job pages, and LinkedIn recommends these jobs to job seekers who are likely to be interested. However, with the vast number of online job vacancies, it becomes challenging to discern overarching trends in the labor market. In this paper, we propose a data mining-based approach for job classification in the modern online labor market. We employed structural topic modeling as our methodology and used the NASDAQ-100 indexed companies' online job vacancies on LinkedIn as the input data. We discover that among all 13 job categories, Marketing, Branding, and Sales; Software Engineering; Hardware Engineering; Industrial Engineering; and Project Management are the most frequently posted job classifications. This study aims to provide a clearer understanding of job market trends, enabling stakeholders to make informed decisions in a rapidly evolving employment landscape.
{"title":"Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market","authors":"Seyed Mohammad Ali Jafari, Ehsan Chitsaz","doi":"arxiv-2409.00658","DOIUrl":"https://doi.org/arxiv-2409.00658","url":null,"abstract":"The emergence of new and disruptive technologies makes the economy and labor\u0000market more unstable. To overcome this kind of uncertainty and to make the\u0000labor market more comprehensible, we must employ labor market intelligence\u0000techniques, which are predominantly based on data analysis. Companies use job\u0000posting sites to advertise their job vacancies, known as online job vacancies\u0000(OJVs). LinkedIn is one of the most utilized websites for matching the supply\u0000and demand sides of the labor market; companies post their job vacancies on\u0000their job pages, and LinkedIn recommends these jobs to job seekers who are\u0000likely to be interested. However, with the vast number of online job vacancies,\u0000it becomes challenging to discern overarching trends in the labor market. In\u0000this paper, we propose a data mining-based approach for job classification in\u0000the modern online labor market. We employed structural topic modeling as our\u0000methodology and used the NASDAQ-100 indexed companies' online job vacancies on\u0000LinkedIn as the input data. We discover that among all 13 job categories,\u0000Marketing, Branding, and Sales; Software Engineering; Hardware Engineering;\u0000Industrial Engineering; and Project Management are the most frequently posted\u0000job classifications. This study aims to provide a clearer understanding of job\u0000market trends, enabling stakeholders to make informed decisions in a rapidly\u0000evolving employment landscape.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193019","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 price of oil can rise because of a disruption to supply or an increase in demand. The nature of the price change determines the dynamic effects. As Kilian (2009) put it: "not all oil price shocks are alike." Using the latest available data, we extend Kilian's (2009) analysis using the R ecosystem and provide more evidence for Kilian's (2009) conclusions. Inference based on unknown conditional heteroskedasticity strengthens the conclusions. With the updated shocks, we assess how a local economy responds to the global oil market, an application that is relevant to policymakers concerned with the transition away from fossil fuels.
石油价格可能因供应中断或需求增加而上涨。价格变化的性质决定了动态效应。正如基里安（2009 年）所说，"并非所有的石油价格冲击都是一样的"："并非所有的石油价格冲击都是一样的。利用最新的可用数据，我们使用 R 生态系统扩展了 Kilian（2009 年）的分析，并为 Kilian（2009 年）的结论提供了更多证据。基于未知条件异方差的推断加强了结论。利用更新的冲击，我们评估了地方经济如何对全球石油市场做出反应，这一应用与关注从化石燃料过渡的政策制定者息息相关。
{"title":"Not All Oil Price Shocks Are Alike. A Replication of Kilian (American Economic Review, 2009)","authors":"Rich Ryan, Nyakundi Michieka","doi":"arxiv-2409.00769","DOIUrl":"https://doi.org/arxiv-2409.00769","url":null,"abstract":"The price of oil can rise because of a disruption to supply or an increase in\u0000demand. The nature of the price change determines the dynamic effects. As\u0000Kilian (2009) put it: \"not all oil price shocks are alike.\" Using the latest\u0000available data, we extend Kilian's (2009) analysis using the R ecosystem and\u0000provide more evidence for Kilian's (2009) conclusions. Inference based on\u0000unknown conditional heteroskedasticity strengthens the conclusions. With the\u0000updated shocks, we assess how a local economy responds to the global oil\u0000market, an application that is relevant to policymakers concerned with the\u0000transition away from fossil fuels.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193022","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 analysis of determinants of a company's financial performance has aroused significant attention, particularly, the environmental, social, and governance (ESG) has been the research focus in recent years. In addition to increasing revenue, the cruise industry has actively embraced the initiative of "green shipping". This study investigates the relationship between ESG and corporate financial performance (CFP) in the global cruise sector. This paper utilizes the sample data from the world's largest cruise companies over 2012-2023, to examine the ESG-CFP relationship by a regression model. The results indicate that ESG practices in cruise companies negatively influence CFP, which is further impacted by financial constraints. Furthermore, the heterogeneity analysis suggests that the high time interest earned (TIE) ratios and low total annual greenhouse gas (GHG) emissions worsen the adverse impacts of ESG on CFP. These findings contribute to the theoretical research on ESG and provide practical guidance for cruise industry operators and investors in their decision-making.
{"title":"Does ESG Consistently Promote the Corporate Financial Performance? A Study of the Global Cruise Industry","authors":"Yuechen Wu","doi":"arxiv-2409.00758","DOIUrl":"https://doi.org/arxiv-2409.00758","url":null,"abstract":"The analysis of determinants of a company's financial performance has aroused\u0000significant attention, particularly, the environmental, social, and governance\u0000(ESG) has been the research focus in recent years. In addition to increasing\u0000revenue, the cruise industry has actively embraced the initiative of \"green\u0000shipping\". This study investigates the relationship between ESG and corporate\u0000financial performance (CFP) in the global cruise sector. This paper utilizes\u0000the sample data from the world's largest cruise companies over 2012-2023, to\u0000examine the ESG-CFP relationship by a regression model. The results indicate\u0000that ESG practices in cruise companies negatively influence CFP, which is\u0000further impacted by financial constraints. Furthermore, the heterogeneity\u0000analysis suggests that the high time interest earned (TIE) ratios and low total\u0000annual greenhouse gas (GHG) emissions worsen the adverse impacts of ESG on CFP.\u0000These findings contribute to the theoretical research on ESG and provide\u0000practical guidance for cruise industry operators and investors in their\u0000decision-making.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193018","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}
Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit.
{"title":"Credit Scores: Performance and Equity","authors":"Stefania Albanesi, Domonkos F. Vamossy","doi":"arxiv-2409.00296","DOIUrl":"https://doi.org/arxiv-2409.00296","url":null,"abstract":"Credit scores are critical for allocating consumer debt in the United States,\u0000yet little evidence is available on their performance. We benchmark a widely\u0000used credit score against a machine learning model of consumer default and find\u0000significant misclassification of borrowers, especially those with low scores.\u0000Our model improves predictive accuracy for young, low-income, and minority\u0000groups due to its superior performance with low quality data, resulting in a\u0000gain in standing for these populations. Our findings suggest that improving\u0000credit scoring performance could lead to more equitable access to credit.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193021","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}
Werewolf game, also known as Mafia game, is a social deduction game that models the conflict between an informed minority (werewolf group) and an uninformed majority (citizen group). This paper explores the optimal strategies of the werewolf game from the perspective of game theory, focusing on cases both with and without prophet. First we examine the existing strategy in game without prophet and propose ``random strategy +", which provides an improved winning probability for the werewolve group. Then we further study the game with prophet, and find the game with prophet can be transformed into a extensive game with complete but imperfect information under a specific rule. We construct a model and design an algorithm to achieve PBE and maximize the citizen group's winning probability. In the end, we examine a property of PBE in game without any restriction.
{"title":"Optimal Strategy in Werewolf Game: A Game Theoretic Perspective","authors":"ST Wang","doi":"arxiv-2408.17177","DOIUrl":"https://doi.org/arxiv-2408.17177","url":null,"abstract":"Werewolf game, also known as Mafia game, is a social deduction game that\u0000models the conflict between an informed minority (werewolf group) and an\u0000uninformed majority (citizen group). This paper explores the optimal strategies\u0000of the werewolf game from the perspective of game theory, focusing on cases\u0000both with and without prophet. First we examine the existing strategy in game\u0000without prophet and propose ``random strategy +\", which provides an improved\u0000winning probability for the werewolve group. Then we further study the game\u0000with prophet, and find the game with prophet can be transformed into a\u0000extensive game with complete but imperfect information under a specific rule.\u0000We construct a model and design an algorithm to achieve PBE and maximize the\u0000citizen group's winning probability. In the end, we examine a property of PBE\u0000in game without any restriction.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193024","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}
Marina Dolfin, George Kapetanios, Leone Leonida, Jose De Leon Miranda
We propose an algorithm to capture emergent patterns in the cross-correlations of financial markets, highlighting regime changes on a global scale. In our approach, financial markets are viewed as complex adaptive systems, and multiscale properties and cross-correlations are considered, particularly during stress conditions such as the COVID-19 pandemic, the invasion of Ukraine by Russia in 2022, and Brexit. We investigate whether significant disruptions reflect an imbalance in investment horizons among investors, and we propose a measure based on this imbalance to depict the impact on global financial markets. The detrended cross-correlation cost (DCCC), which is derived from detrended cross-correlation analysis, uses cross-correlations at different timescales to capture variations in investment horizons amid financial uncertainties. Our algorithm, which combines DCCC analysis and the minimum-spanning-tree filtering approach, tracks system interconnectedness and investor imbalances. We tested the DCCC indicator using daily price series of G7, Russian, and Chinese markets over the past decade and found that it increases sharply during ``crash'' periods compared to ``business as usual'' periods. Our empirical results confirm that short-term investment horizons dominate during financial instabilities; this validates our hypothesis and indicates that the DCCC can serve as a leading indicator of shifts in financial-market regimes.
{"title":"Investor behavior and multiscale cross-correlations: Unveiling regime shifts in global financial markets","authors":"Marina Dolfin, George Kapetanios, Leone Leonida, Jose De Leon Miranda","doi":"arxiv-2408.17200","DOIUrl":"https://doi.org/arxiv-2408.17200","url":null,"abstract":"We propose an algorithm to capture emergent patterns in the\u0000cross-correlations of financial markets, highlighting regime changes on a\u0000global scale. In our approach, financial markets are viewed as complex adaptive\u0000systems, and multiscale properties and cross-correlations are considered,\u0000particularly during stress conditions such as the COVID-19 pandemic, the\u0000invasion of Ukraine by Russia in 2022, and Brexit. We investigate whether\u0000significant disruptions reflect an imbalance in investment horizons among\u0000investors, and we propose a measure based on this imbalance to depict the\u0000impact on global financial markets. The detrended cross-correlation cost\u0000(DCCC), which is derived from detrended cross-correlation analysis, uses\u0000cross-correlations at different timescales to capture variations in investment\u0000horizons amid financial uncertainties. Our algorithm, which combines DCCC\u0000analysis and the minimum-spanning-tree filtering approach, tracks system\u0000interconnectedness and investor imbalances. We tested the DCCC indicator using\u0000daily price series of G7, Russian, and Chinese markets over the past decade and\u0000found that it increases sharply during ``crash'' periods compared to ``business\u0000as usual'' periods. Our empirical results confirm that short-term investment\u0000horizons dominate during financial instabilities; this validates our hypothesis\u0000and indicates that the DCCC can serve as a leading indicator of shifts in\u0000financial-market regimes.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193023","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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