Pub Date : 2025-02-07DOI: 10.1016/j.jempfin.2025.101579
Aleksandra Rzeźnik
I examine the active liquidity management of U.S. equity mutual funds facing unexpected, persistent investor withdrawals by exploiting two independent shocks: the 2003 mutual fund scandal and the 2016 introduction of Morningstar Sustainability Ratings. I document that fund managers increase portfolio liquidity by adjusting both equity and cash holdings when subject to sudden, moderate, and prolonged outflows. Among affected funds, those that more aggressively increase portfolio liquidity significantly outperform their less liquidity-focused peers, suggesting that skilled managers employ active liquidity management to minimize costs imposed by redemption obligations.
{"title":"Skilled active liquidity management: Evidence from shocks to fund flows","authors":"Aleksandra Rzeźnik","doi":"10.1016/j.jempfin.2025.101579","DOIUrl":"10.1016/j.jempfin.2025.101579","url":null,"abstract":"<div><div>I examine the active liquidity management of U.S. equity mutual funds facing unexpected, persistent investor withdrawals by exploiting two independent shocks: the 2003 mutual fund scandal and the 2016 introduction of Morningstar Sustainability Ratings. I document that fund managers increase portfolio liquidity by adjusting both equity and cash holdings when subject to sudden, moderate, and prolonged outflows. Among affected funds, those that more aggressively increase portfolio liquidity significantly outperform their less liquidity-focused peers, suggesting that skilled managers employ active liquidity management to minimize costs imposed by redemption obligations.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101579"},"PeriodicalIF":2.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.jempfin.2025.101593
Fernanda Fuentes , Rodrigo Herrera , Adam Clements
This paper proposes a new class of marked point process models to capture the clustering behavior in extreme financial events. The idea of multiple dynamic parameters embedded in the context of score driven models is utilized to estimate a dynamic extreme value approach, labeled as the Orthogonal Score-Driven Peaks Over Threshold model. A Monte-Carlo study is conducted to study different time-varying parameter specifications. The results show that this approach can capture a range of different dynamics for the parameters. In an empirical application, we study the dynamics of the tail distribution over time, and in particular on VaR and ES forecasts, for the constituents of the S&P Banks Index. Finally, we study the behavior of extremely adverse returns in the financial system by means of a decomposition of the tail- risk measure, giving a deeper understanding of both the dynamics of the risk of an individual bank, and the systemic linkages associated with the stability of the global financial system.
{"title":"Tail risk dynamics of banks with score-driven extreme value models","authors":"Fernanda Fuentes , Rodrigo Herrera , Adam Clements","doi":"10.1016/j.jempfin.2025.101593","DOIUrl":"10.1016/j.jempfin.2025.101593","url":null,"abstract":"<div><div>This paper proposes a new class of marked point process models to capture the clustering behavior in extreme financial events. The idea of multiple dynamic parameters embedded in the context of score driven models is utilized to estimate a dynamic extreme value approach, labeled as the Orthogonal Score-Driven Peaks Over Threshold model. A Monte-Carlo study is conducted to study different time-varying parameter specifications. The results show that this approach can capture a range of different dynamics for the parameters. In an empirical application, we study the dynamics of the tail distribution over time, and in particular on VaR and ES forecasts, for the constituents of the S&P Banks Index. Finally, we study the behavior of extremely adverse returns in the financial system by means of a decomposition of the tail-<span><math><mi>β</mi></math></span> risk measure, giving a deeper understanding of both the dynamics of the risk of an individual bank, and the systemic linkages associated with the stability of the global financial system.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101593"},"PeriodicalIF":2.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-06DOI: 10.1016/j.jempfin.2025.101594
Rodrigo Hizmeri , Marwan Izzeldin , Giovanni Urga
In this paper, we examine the finite sample properties of test statistics designed to identify distinct underlying components of high-frequency financial data, specifically the Brownian component and infinite vs. finite activity jumps. We conduct a comprehensive set of Monte Carlo simulations to evaluate the tests under various types of microstructure noise, price staleness, and different levels of jump activity. We apply these tests to a dataset comprising 100 individual S&P 500 constituents from diverse business sectors and the SPY (S&P 500 ETF) to empirically assess the relative magnitude of these components. Our findings strongly support the presence of both Brownian and jump components. Furthermore, we investigate the time-varying nature of rejection rates and we find that periods with more jumps days are usually associated with an increase in infinite jumps and a decrease in finite jumps. This suggests a dynamic interplay between jump components over time.
{"title":"Identifying the underlying components of high-frequency data: Pure vs jump diffusion processes","authors":"Rodrigo Hizmeri , Marwan Izzeldin , Giovanni Urga","doi":"10.1016/j.jempfin.2025.101594","DOIUrl":"10.1016/j.jempfin.2025.101594","url":null,"abstract":"<div><div>In this paper, we examine the finite sample properties of test statistics designed to identify distinct underlying components of high-frequency financial data, specifically the Brownian component and infinite vs. finite activity jumps. We conduct a comprehensive set of Monte Carlo simulations to evaluate the tests under various types of microstructure noise, price staleness, and different levels of jump activity. We apply these tests to a dataset comprising 100 individual S&P 500 constituents from diverse business sectors and the SPY (S&P 500 ETF) to empirically assess the relative magnitude of these components. Our findings strongly support the presence of both Brownian and jump components. Furthermore, we investigate the time-varying nature of rejection rates and we find that periods with more jumps days are usually associated with an increase in infinite jumps and a decrease in finite jumps. This suggests a dynamic interplay between jump components over time.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101594"},"PeriodicalIF":2.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We assess the differential impacts of “Big Bang” and “Small Bang” contracts and convention changes on market participants across CDS markets and couple comprehensive bank-firm-level CDS trading data from the DTCC to the German credit register containing bi-lateral bank-firm credit exposures. We find that after the Bangs, the cost of buying CDS contracts becomes lower for non-dealer banks and that, because of this decrease in insurance costs, these banks extend relatively more credit to CDS-traded and affected firms compared to dealers, and hedge more effectively. Hence, standardization lowers the cost of credit insurance and leads to a relative increase in credit extensions by non-dealer banks.
{"title":"CDS and credit: The effect of the bangs on credit insurance, lending and hedging","authors":"Yalin Gündüz , Steven Ongena , Günseli Tümer-Alkan , Yuejuan Yu","doi":"10.1016/j.jempfin.2025.101583","DOIUrl":"10.1016/j.jempfin.2025.101583","url":null,"abstract":"<div><div>We assess the differential impacts of “Big Bang” and “Small Bang” contracts and convention changes on market participants across CDS markets and couple comprehensive bank-firm-level CDS trading data from the DTCC to the German credit register containing bi-lateral bank-firm credit exposures. We find that after the Bangs, the cost of buying CDS contracts becomes lower for non-dealer banks and that, because of this decrease in insurance costs, these banks extend relatively more credit to CDS-traded and affected firms compared to dealers, and hedge more effectively. Hence, standardization lowers the cost of credit insurance and leads to a relative increase in credit extensions by non-dealer banks.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101583"},"PeriodicalIF":2.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143231743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jempfin.2025.101595
Jiawen Luo , Oguzhan Cepni , Riza Demirer , Rangan Gupta
We propose a procedure to forecast the realized covariance matrix for a given set of assets within a multivariate heterogeneous autoregressive (MHAR) framework. Utilizing high-frequency data for the U.S. aggregate and industry indexes and a large set of exogenous predictors that include financial, macroeconomic, sentiment, and climate-based factors, we evaluate the out-of-sample performance of industry portfolios constructed from forecasted realized covariance matrices across various univariate and multivariate forecasting models. Our findings show that LASSO-based multivariate HAR models employing predictors that capture climate uncertainty generally yield more consistent evidence regarding the accuracy of the realized covariance forecasts, providing further support for the growing evidence that climate related factors significantly drive return and volatility dynamics in financial markets. While international summits and global warming stand out as the dominant climate predictors for realized volatility forecasts, both climate and macroeconomic predictors prove equally important for longer term correlation forecasts. In these forecasts, the U.S. EPU index and natural disasters, along with U.S. climate policy uncertainty, play dominant predictive roles. Our results suggest that the MHAR framework, coupled with DRD decomposition that splits the covariance matrix into a diagonal matrix of realized variances and realized correlations, can be utilized in a high-frequency setting to implement diversification and smart beta strategies for various investment horizons.
{"title":"Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies","authors":"Jiawen Luo , Oguzhan Cepni , Riza Demirer , Rangan Gupta","doi":"10.1016/j.jempfin.2025.101595","DOIUrl":"10.1016/j.jempfin.2025.101595","url":null,"abstract":"<div><div>We propose a procedure to forecast the realized covariance matrix for a given set of assets within a multivariate heterogeneous autoregressive (MHAR) framework. Utilizing high-frequency data for the U.S. aggregate and industry indexes and a large set of exogenous predictors that include financial, macroeconomic, sentiment, and climate-based factors, we evaluate the out-of-sample performance of industry portfolios constructed from forecasted realized covariance matrices across various univariate and multivariate forecasting models. Our findings show that LASSO-based multivariate HAR models employing predictors that capture climate uncertainty generally yield more consistent evidence regarding the accuracy of the realized covariance forecasts, providing further support for the growing evidence that climate related factors significantly drive return and volatility dynamics in financial markets. While international summits and global warming stand out as the dominant climate predictors for realized volatility forecasts, both climate and macroeconomic predictors prove equally important for longer term correlation forecasts. In these forecasts, the U.S. EPU index and natural disasters, along with U.S. climate policy uncertainty, play dominant predictive roles. Our results suggest that the MHAR framework, coupled with DRD decomposition that splits the covariance matrix into a diagonal matrix of realized variances and realized correlations, can be utilized in a high-frequency setting to implement diversification and smart beta strategies for various investment horizons.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101595"},"PeriodicalIF":2.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1016/j.jempfin.2025.101592
Shun Fu , Emma Li , Li Liao , Zhengwei Wang , Hongyu Xiang
Based on unique data containing the loan history and online consumption information of cash loan borrowers, we apply an exogenous credit supply shock to these borrowers and show that increased credit increases individuals' delinquency rates and reliance on cash loans. Higher credit supply increases the likelihood of a loan being overdue over 60 days by 5.7% and decreases platform exit by 33%. This effect on delinquency is significantly less prominent among individuals with greater financial literacy. Second, we demonstrate that credit expansion is positively associated with an increase in subsequent borrower consumption, particularly addictive consumption.
{"title":"Unveiling the villain: Credit supply and the debt trap","authors":"Shun Fu , Emma Li , Li Liao , Zhengwei Wang , Hongyu Xiang","doi":"10.1016/j.jempfin.2025.101592","DOIUrl":"10.1016/j.jempfin.2025.101592","url":null,"abstract":"<div><div>Based on unique data containing the loan history and online consumption information of cash loan borrowers, we apply an exogenous credit supply shock to these borrowers and show that increased credit increases individuals' delinquency rates and reliance on cash loans. Higher credit supply increases the likelihood of a loan being overdue over 60 days by 5.7% and decreases platform exit by 33%. This effect on delinquency is significantly less prominent among individuals with greater financial literacy. Second, we demonstrate that credit expansion is positively associated with an increase in subsequent borrower consumption, particularly addictive consumption.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101592"},"PeriodicalIF":2.1,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143231742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1016/j.jempfin.2025.101580
Jie Cao , Jason C. Hsu , Linjia Song , Zhanbing Xiao , Xintong Zhan
We document that when smart beta ETFs are more actively traded, mutual fund flow sensitivity to multi-factor alphas increases significantly. This evidence is consistent with a friction hypothesis that active smart beta ETF trading reduces the costs of investing in non-market risk factors (e.g., SMB and HML). Consequently, when this friction is diminished, investors reward mutual fund managers more for multi-factor alphas. We show that the results are driven by sophisticated investors, ruling out behavioral explanations. The results are concentrated among mutual funds with high exposures to non-market risk factors. We further find that the gap between CAPM alpha and multi-factor alphas in explaining flows reduces when smart beta ETFs are actively traded.
{"title":"Smart beta, “smarter” flows","authors":"Jie Cao , Jason C. Hsu , Linjia Song , Zhanbing Xiao , Xintong Zhan","doi":"10.1016/j.jempfin.2025.101580","DOIUrl":"10.1016/j.jempfin.2025.101580","url":null,"abstract":"<div><div>We document that when smart beta ETFs are more actively traded, mutual fund flow sensitivity to multi-factor alphas increases significantly. This evidence is consistent with a friction hypothesis that active smart beta ETF trading reduces the costs of investing in non-market risk factors (e.g., SMB and HML). Consequently, when this friction is diminished, investors reward mutual fund managers more for multi-factor alphas. We show that the results are driven by sophisticated investors, ruling out behavioral explanations. The results are concentrated among mutual funds with high exposures to non-market risk factors. We further find that the gap between CAPM alpha and multi-factor alphas in explaining flows reduces when smart beta ETFs are actively traded.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101580"},"PeriodicalIF":2.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1016/j.jempfin.2025.101581
Rui Duan , Yelena Larkin
What informational advantage do short-term investors have? This paper demonstrates that short-term investors can benefit from the ability to process public, but slowly diffusing, supply chain information ahead of other market participants. In support of this argument, we find that short-term investors establish larger long and short positions in firms with high customer concentration. In addition, an increase in short-term institutional ownership is associated with higher stock returns in firms with high customer concentration, supporting the informational advantage hypothesis. Finally, the relationship between customer concentration and short-term institutional ownership strengthens in high information asymmetry environment. In contrast, we do not find preference towards high customer concentration firms among long-term institutions, who are less positioned to exploit short-lived informational benefits.
{"title":"Short-term institutional investors and the diffusion of supply chain information","authors":"Rui Duan , Yelena Larkin","doi":"10.1016/j.jempfin.2025.101581","DOIUrl":"10.1016/j.jempfin.2025.101581","url":null,"abstract":"<div><div>What informational advantage do short-term investors have? This paper demonstrates that short-term investors can benefit from the ability to process public, but slowly diffusing, supply chain information ahead of other market participants. In support of this argument, we find that short-term investors establish larger long and short positions in firms with high customer concentration. In addition, an increase in short-term institutional ownership is associated with higher stock returns in firms with high customer concentration, supporting the informational advantage hypothesis. Finally, the relationship between customer concentration and short-term institutional ownership strengthens in high information asymmetry environment. In contrast, we do not find preference towards high customer concentration firms among long-term institutions, who are less positioned to exploit short-lived informational benefits.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101581"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.jempfin.2025.101582
Zhonghao Jiang , Yukun Shi , Lu Xing
We investigate the role of social connectedness in cross-border mergers and acquisitions (M&As) using the Facebook social connectedness index. We show that stronger social connectedness between countries leads to higher announcement returns for acquirers in cross-border M&As. This effect is attributed to improved information dissemination, which reduces target premiums, increases deal completion likelihood, and supports acquirers to achieve long-term success. Furthermore, social connectedness increases the frequency and dollar value of cross-border M&As between countries. This relation is weaker for countries in the same customs union, but stronger in the presence of greater political disagreement or significant time zone differences. Extending our analysis to domestic M&As in the U.S., we find that social connectedness between the headquarters’ cities of acquirers and targets improves acquirers’ announcement returns.
{"title":"Social connectedness and cross-border mergers and acquisitions","authors":"Zhonghao Jiang , Yukun Shi , Lu Xing","doi":"10.1016/j.jempfin.2025.101582","DOIUrl":"10.1016/j.jempfin.2025.101582","url":null,"abstract":"<div><div>We investigate the role of social connectedness in cross-border mergers and acquisitions (M&As) using the Facebook social connectedness index. We show that stronger social connectedness between countries leads to higher announcement returns for acquirers in cross-border M&As. This effect is attributed to improved information dissemination, which reduces target premiums, increases deal completion likelihood, and supports acquirers to achieve long-term success. Furthermore, social connectedness increases the frequency and dollar value of cross-border M&As between countries. This relation is weaker for countries in the same customs union, but stronger in the presence of greater political disagreement or significant time zone differences. Extending our analysis to domestic M&As in the U.S., we find that social connectedness between the headquarters’ cities of acquirers and targets improves acquirers’ announcement returns.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"81 ","pages":"Article 101582"},"PeriodicalIF":2.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jempfin.2024.101575
Jiawen Luo , Zhenbiao Chen , Mingmian Cheng
This paper studies the importance of structural breaks and asymmetric risk effects for accurate forecasts of the realized beta. Specifically, structural breaks in the realized beta are detected by Iterated Cumulative Sum of Square (ICSS) algorithm and asymmetric risk effects are captured by decomposing the realized beta further into various components following Ang et al. (2006) and Bollerslev et al. (2021). We propose a set of Heterogeneous Autoregressive (HAR) model variants by incorporating these new predictors. To achieve model parsimony and to keep only the predictors with significant power, we employ Least Absolute Shrinkage and Selection Operator (LASSO) method for variable selection. Our proposed LASSOHAR model with estimators of structural breaks and asymmetric risk effects is found to yield more accurate out-of-sample beta forecasts than a variety of alternative models in terms of both statistical and economic criteria. In particular, our model successfully achieves the long-memory feature of realized betas in a tractable and parsimonious way. These empirical findings are robust across different data sampling frequencies, different estimation windows, different sub-samples, different quantiles of the beta distribution and different industrial sectors.
{"title":"Forecasting realized betas using predictors indicating structural breaks and asymmetric risk effects","authors":"Jiawen Luo , Zhenbiao Chen , Mingmian Cheng","doi":"10.1016/j.jempfin.2024.101575","DOIUrl":"10.1016/j.jempfin.2024.101575","url":null,"abstract":"<div><div>This paper studies the importance of structural breaks and asymmetric risk effects for accurate forecasts of the realized beta. Specifically, structural breaks in the realized beta are detected by Iterated Cumulative Sum of Square (ICSS) algorithm and asymmetric risk effects are captured by decomposing the realized beta further into various components following Ang et al. (2006) and Bollerslev et al. (2021). We propose a set of Heterogeneous Autoregressive (HAR) model variants by incorporating these new predictors. To achieve model parsimony and to keep only the predictors with significant power, we employ Least Absolute Shrinkage and Selection Operator (LASSO) method for variable selection. Our proposed LASSO<img>HAR model with estimators of structural breaks and asymmetric risk effects is found to yield more accurate out-of-sample beta forecasts than a variety of alternative models in terms of both statistical and economic criteria. In particular, our model successfully achieves the long-memory feature of realized betas in a tractable and parsimonious way. These empirical findings are robust across different data sampling frequencies, different estimation windows, different sub-samples, different quantiles of the beta distribution and different industrial sectors.</div></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"80 ","pages":"Article 101575"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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