This article proposes a new predictive model for large-dimensional realized covariance matrices. Using high-frequency data, we estimate daily realized covariance matrices for the constituents of the S&P 500 Index and a set of observable factors. Using a standard decomposition of the joint covariance matrix, we express the covariance matrix of the individual assets similar to a dynamic factor model. To forecast the covariance matrix, we model the components of the covariance structure using a series of autoregressive processes. A novel feature of the model is the use of the data-driven hierarchical clustering algorithm to determine the structure of the idiosyncratic covariance matrix. A simulation study shows that this method can accurately estimate the block structure as long as the number of blocks is small relative to the number of stocks. In an out-of-sample portfolio selection exercise, we find that the proposed model outperforms other commonly used multivariate volatility models in extant literature.
{"title":"Large-Dimensional Portfolio Selection with a High-Frequency-Based Dynamic Factor Model","authors":"Simon T Bodilsen","doi":"10.1093/jjfinec/nbae018","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae018","url":null,"abstract":"This article proposes a new predictive model for large-dimensional realized covariance matrices. Using high-frequency data, we estimate daily realized covariance matrices for the constituents of the S&P 500 Index and a set of observable factors. Using a standard decomposition of the joint covariance matrix, we express the covariance matrix of the individual assets similar to a dynamic factor model. To forecast the covariance matrix, we model the components of the covariance structure using a series of autoregressive processes. A novel feature of the model is the use of the data-driven hierarchical clustering algorithm to determine the structure of the idiosyncratic covariance matrix. A simulation study shows that this method can accurately estimate the block structure as long as the number of blocks is small relative to the number of stocks. In an out-of-sample portfolio selection exercise, we find that the proposed model outperforms other commonly used multivariate volatility models in extant literature.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"46 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study empirically estimates the date of the structural change in the aggregate earnings–returns relation and reports it as the fourth quarter of 1991. We identify three sources of the structural change: (i) an increase in the relative importance of cash flow news contained in stock returns; (ii) a decrease in the importance of discount rate news contained in aggregate earnings; and (iii) a decrease in the persistence or the predictability of aggregate earnings and returns. Next, we examine the components of aggregate earnings and find that the change in the aggregate earnings–returns relation is largely driven by intertemporal changes in aggregate inventory accruals, suggesting the structural change is attributable to improved inventory management systems.
{"title":"A Structural Break in the Aggregate Earnings–Returns Relation","authors":"Asher Curtis, Chang-Jin Kim, Hyung Il Oh","doi":"10.1093/jjfinec/nbae015","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae015","url":null,"abstract":"This study empirically estimates the date of the structural change in the aggregate earnings–returns relation and reports it as the fourth quarter of 1991. We identify three sources of the structural change: (i) an increase in the relative importance of cash flow news contained in stock returns; (ii) a decrease in the importance of discount rate news contained in aggregate earnings; and (iii) a decrease in the persistence or the predictability of aggregate earnings and returns. Next, we examine the components of aggregate earnings and find that the change in the aggregate earnings–returns relation is largely driven by intertemporal changes in aggregate inventory accruals, suggesting the structural change is attributable to improved inventory management systems.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"111 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Massimiliano Caporin, Tommaso Di Fonzo, Daniele Girolimetto
We address the construction of Realized Variance (RV) forecasts by exploiting the hierarchical structure implicit in available decompositions of RV. We propose a post-forecasting approach that utilizes bottom-up and regression-based reconciliation methods. By using data referred to the Dow Jones Industrial Average Index and to its constituents we show that exploiting the informative content of hierarchies improves the forecast accuracy. Forecasting performance is evaluated out-of-sample based on the empirical MSE and QLIKE criteria as well as using the Model Confidence Set approach.
{"title":"Exploiting Intraday Decompositions in Realized Volatility Forecasting: A Forecast Reconciliation Approach","authors":"Massimiliano Caporin, Tommaso Di Fonzo, Daniele Girolimetto","doi":"10.1093/jjfinec/nbae014","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae014","url":null,"abstract":"We address the construction of Realized Variance (RV) forecasts by exploiting the hierarchical structure implicit in available decompositions of RV. We propose a post-forecasting approach that utilizes bottom-up and regression-based reconciliation methods. By using data referred to the Dow Jones Industrial Average Index and to its constituents we show that exploiting the informative content of hierarchies improves the forecast accuracy. Forecasting performance is evaluated out-of-sample based on the empirical MSE and QLIKE criteria as well as using the Model Confidence Set approach.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"28 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose estimators of the stochastic discount factor using large cross-sections of individual stocks. We introduce a short time-block structure on a large N, T panel to exploit unbalanced panels of individual stock returns and suggest a novel bias correction to achieve the consistency of our estimators. Our estimators can accommodate pre-specified traded and nontraded factors, and latent factors. The estimators perform well in simulations. We apply our estimators to return data for U.S. individual stocks over a 50-year sample period and identify those factors in popular asset pricing models that command significant premia. A number of proposed nontraded factors have insignificant risk premia. Contrary to many studies, we find the market factor has a significant premium, as do profitability, value, and momentum factors.
{"title":"Large Sample Estimators of the Stochastic Discount Factor","authors":"Soohun Kim, Robert A Korajczyk","doi":"10.1093/jjfinec/nbae012","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae012","url":null,"abstract":"We propose estimators of the stochastic discount factor using large cross-sections of individual stocks. We introduce a short time-block structure on a large N, T panel to exploit unbalanced panels of individual stock returns and suggest a novel bias correction to achieve the consistency of our estimators. Our estimators can accommodate pre-specified traded and nontraded factors, and latent factors. The estimators perform well in simulations. We apply our estimators to return data for U.S. individual stocks over a 50-year sample period and identify those factors in popular asset pricing models that command significant premia. A number of proposed nontraded factors have insignificant risk premia. Contrary to many studies, we find the market factor has a significant premium, as do profitability, value, and momentum factors.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"10 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141147889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the clustering behavior of stock return jumps modeled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterized by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.
{"title":"Jump Clustering, Information Flows, and Stock Price Efficiency","authors":"Jian Chen","doi":"10.1093/jjfinec/nbae009","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae009","url":null,"abstract":"We study the clustering behavior of stock return jumps modeled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterized by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"76 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We formulate the problem of estimating risk prices in a stochastic discount factor (SDF) model as an instrumental variables regression. The IV estimator allows efficient estimation for models with non-traded factors and many test assets. Optimal instruments are constructed using a regularized sparse first stage regression. In a simulation study, the IV estimator is close to the infeasible GMM estimator in a setting with many assets. In an empirical application, the tracking portfolio for consumption growth appears strongly correlated with consumption news. It implies that consumption is a priced factor for the cross-section of excess equity returns. A similar regularized regression, projecting the SDF on test assets, leads to an estimate of the Hansen–Jagannathan distance, and identifies portfolios that maximally violate the pricing implications of the model.
{"title":"Empirical Asset Pricing with Many Test Assets","authors":"Rasmus Lönn, Peter C Schotman","doi":"10.1093/jjfinec/nbae002","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae002","url":null,"abstract":"We formulate the problem of estimating risk prices in a stochastic discount factor (SDF) model as an instrumental variables regression. The IV estimator allows efficient estimation for models with non-traded factors and many test assets. Optimal instruments are constructed using a regularized sparse first stage regression. In a simulation study, the IV estimator is close to the infeasible GMM estimator in a setting with many assets. In an empirical application, the tracking portfolio for consumption growth appears strongly correlated with consumption news. It implies that consumption is a priced factor for the cross-section of excess equity returns. A similar regularized regression, projecting the SDF on test assets, leads to an estimate of the Hansen–Jagannathan distance, and identifies portfolios that maximally violate the pricing implications of the model.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"25 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140151889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Measuring model risk is required by regulators in financial and insurance markets. We separate model risk into parameter estimation risk (PER) and model specification risk (MSR), and we propose expected shortfall type model risk measures applied to Lévy jump, affine jump-diffusion, and multifactor models. We investigate the impact of PER and MSR on the models’ ability to capture the joint dynamics of stock and option prices. Using Markov chain Monte Carlo techniques, we implement two methodologies to estimate parameters under the risk-neutral probability measure and the real-world probability measure jointly.
衡量模型风险是金融和保险市场监管者的要求。我们将模型风险分为参数估计风险（PER）和模型规范风险（MSR），并提出了适用于莱维跳跃模型、仿射跳跃-扩散模型和多因素模型的预期缺口型模型风险度量。我们研究了 PER 和 MSR 对模型捕捉股票和期权价格联合动态能力的影响。利用马尔可夫链蒙特卡罗技术，我们实施了两种方法来共同估计风险中性概率度量和真实世界概率度量下的参数。
{"title":"Measures of Model Risk for Continuous-Time Finance Models","authors":"Emese Lazar, Shuyuan Qi, Radu Tunaru","doi":"10.1093/jjfinec/nbae001","DOIUrl":"https://doi.org/10.1093/jjfinec/nbae001","url":null,"abstract":"Measuring model risk is required by regulators in financial and insurance markets. We separate model risk into parameter estimation risk (PER) and model specification risk (MSR), and we propose expected shortfall type model risk measures applied to Lévy jump, affine jump-diffusion, and multifactor models. We investigate the impact of PER and MSR on the models’ ability to capture the joint dynamics of stock and option prices. Using Markov chain Monte Carlo techniques, we implement two methodologies to estimate parameters under the risk-neutral probability measure and the real-world probability measure jointly.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"92 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139678658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaoxin Hong, Daniel J Henderson, Jiancheng Jiang, Qingshan Ni
Abstract There is a discrepancy in the limiting distributions of least-squares estimators for stationary and integrated variables. For statistical inference, it must be decided which distribution should be used in advance. This motivates us to develop a unifying inference procedure based on weighted estimation. The asymptotic distributions of the proposed estimators are developed and a random weighting bootstrap method is proposed for constructing confidence regions. The proposed method outperforms existing methods (with time constant or time-varying error variance) in simulations. We further study the predictability of asset returns in a setting where some of our state variables are endogenous.
{"title":"Unifying Estimation and Inference for Linear Regression with Stationary and Integrated or Near-Integrated Variables","authors":"Shaoxin Hong, Daniel J Henderson, Jiancheng Jiang, Qingshan Ni","doi":"10.1093/jjfinec/nbad030","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad030","url":null,"abstract":"Abstract There is a discrepancy in the limiting distributions of least-squares estimators for stationary and integrated variables. For statistical inference, it must be decided which distribution should be used in advance. This motivates us to develop a unifying inference procedure based on weighted estimation. The asymptotic distributions of the proposed estimators are developed and a random weighting bootstrap method is proposed for constructing confidence regions. The proposed method outperforms existing methods (with time constant or time-varying error variance) in simulations. We further study the predictability of asset returns in a setting where some of our state variables are endogenous.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaohong Chen, Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin, Myunghyun Song
Abstract We introduce a new class of algorithms, stochastic generalized method of moments (SGMM), for estimation and inference on (overidentified) moment restriction models. Our SGMM is a novel stochastic approximation alternative to the popular Hansen (1982) (offline) GMM, and offers fast and scalable implementation with the ability to handle streaming datasets in real time. We establish the almost sure convergence, and the (functional) central limit theorem for the inefficient online 2SLS and the efficient SGMM. Moreover, we propose online versions of the Durbin–Wu–Hausman and Sargan–Hansen tests that can be seamlessly integrated within the SGMM framework. Extensive Monte Carlo simulations show that as the sample size increases, the SGMM matches the standard (offline) GMM in terms of estimation accuracy and gains over computational efficiency, indicating its practical value for both large-scale and online datasets. We demonstrate the efficacy of our approach by a proof of concept using two well-known empirical examples with large sample sizes.
{"title":"SGMM: Stochastic Approximation to Generalized Method of Moments","authors":"Xiaohong Chen, Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin, Myunghyun Song","doi":"10.1093/jjfinec/nbad027","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad027","url":null,"abstract":"Abstract We introduce a new class of algorithms, stochastic generalized method of moments (SGMM), for estimation and inference on (overidentified) moment restriction models. Our SGMM is a novel stochastic approximation alternative to the popular Hansen (1982) (offline) GMM, and offers fast and scalable implementation with the ability to handle streaming datasets in real time. We establish the almost sure convergence, and the (functional) central limit theorem for the inefficient online 2SLS and the efficient SGMM. Moreover, we propose online versions of the Durbin–Wu–Hausman and Sargan–Hansen tests that can be seamlessly integrated within the SGMM framework. Extensive Monte Carlo simulations show that as the sample size increases, the SGMM matches the standard (offline) GMM in terms of estimation accuracy and gains over computational efficiency, indicating its practical value for both large-scale and online datasets. We demonstrate the efficacy of our approach by a proof of concept using two well-known empirical examples with large sample sizes.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"13 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135169059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Journal Article Comment on: Eigenvalue Tests for the Number of Latent Factors in Short Panels Get access Markus Pelger Markus Pelger Department of Management Science & Engineering, Stanford University, Stanford, CA, USA Address correspondence to Markus Pelger, Department of Management Science & Engineering, Stanford University, Stanford, CA, USA, or email: mpelger@stanford.edu https://orcid.org/0000-0001-7111-3588 Search for other works by this author on: Oxford Academic Google Scholar Journal of Financial Econometrics, nbad026, https://doi.org/10.1093/jjfinec/nbad026 Published: 24 October 2023 Article history Editorial decision: 31 August 2023 Received: 31 August 2023 Published: 24 October 2023
期刊文章评论:短面板中潜在因素数量的特征值测试获取Markus Pelger Markus Pelger管理科学与工程系，斯坦福大学，斯坦福，CA，美国地址通信给Markus Pelger，管理科学与工程系，斯坦福大学，斯坦福，CA，美国，或电子邮件:mpelger@stanford.edu https://orcid.org/0000-0001-7111-3588搜索作者的其他作品:牛津学术谷歌学者金融计量经济学学报，nbad026, https://doi.org/10.1093/jjfinec/nbad026出版日期:2023年10月24日文章历史编辑决定:2023年8月31日收稿日期:2023年8月31日出版日期:2023年10月24日
{"title":"Comment on: Eigenvalue Tests for the Number of Latent Factors in Short Panels","authors":"Markus Pelger","doi":"10.1093/jjfinec/nbad026","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad026","url":null,"abstract":"Journal Article Comment on: Eigenvalue Tests for the Number of Latent Factors in Short Panels Get access Markus Pelger Markus Pelger Department of Management Science & Engineering, Stanford University, Stanford, CA, USA Address correspondence to Markus Pelger, Department of Management Science & Engineering, Stanford University, Stanford, CA, USA, or email: mpelger@stanford.edu https://orcid.org/0000-0001-7111-3588 Search for other works by this author on: Oxford Academic Google Scholar Journal of Financial Econometrics, nbad026, https://doi.org/10.1093/jjfinec/nbad026 Published: 24 October 2023 Article history Editorial decision: 31 August 2023 Received: 31 August 2023 Published: 24 October 2023","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"14 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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