{"title":"Correction to: A Machine Learning Approach to Volatility Forecasting","authors":"","doi":"10.1093/jjfinec/nbac032","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac032","url":null,"abstract":"","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42497525","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 refine the approximate factor model of asset returns by distinguishing between strong factors, whose sum of squared factor betas grow at the same rate as the number of assets, and semi-strong factors, whose sum of squared factor betas grow to infinity, but at a slower rate. We develop a test statistic for strength of factors based on the cross-sectional mean-square of regression-estimated betas. We also describe an adjusted version of the test statistic to differentiate semi-strong factors from strong factors. We apply the methodology to daily equity returns to characterize some pre-specified factors as strong or semi-strong.
{"title":"Semi-Strong Factors in Asset Returns","authors":"Gregory Connor, Robert A Korajczyk","doi":"10.1093/jjfinec/nbac028","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac028","url":null,"abstract":"We refine the approximate factor model of asset returns by distinguishing between strong factors, whose sum of squared factor betas grow at the same rate as the number of assets, and semi-strong factors, whose sum of squared factor betas grow to infinity, but at a slower rate. We develop a test statistic for strength of factors based on the cross-sectional mean-square of regression-estimated betas. We also describe an adjusted version of the test statistic to differentiate semi-strong factors from strong factors. We apply the methodology to daily equity returns to characterize some pre-specified factors as strong or semi-strong.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"8 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138527905","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 consider inference for predictive regressions with multiple predictors. Extant tests for predictability (especially for joint predictability) may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental variables-based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrates the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided.
{"title":"A New Test for Multiple Predictive Regression","authors":"Ke-Li Xu, Junjie Guo","doi":"10.1093/jjfinec/nbac030","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac030","url":null,"abstract":"We consider inference for predictive regressions with multiple predictors. Extant tests for predictability (especially for joint predictability) may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental variables-based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrates the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"41 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138527970","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 present empirical evidence showing that option-implied risk measures (OIRMs) are substantially impacted by bid–ask spreads in underlying options. Asking prices are more sensitive to shocks than bids, leading to highly skewed distributions of spreads. We derive and estimate a model of market making that empirically matches these asymmetric responses as well as the time-series properties of bid–ask spreads. Using these estimates to obtain bias-corrected option quotes, we compute several popular OIRMs. We find that fear and risk premia associated with market events that affect the center of the return distribution or unpredictable return jumps are on average overstated when relying on option mid-quotes, whereas risk associated with return-tail events is larger once the bias has been corrected.
{"title":"Market Maker Inventory, Bid–Ask Spreads, and the Computation of Option Implied Risk Measures","authors":"Bjørn Eraker, Daniela Osterrieder","doi":"10.1093/jjfinec/nbac025","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac025","url":null,"abstract":"\u0000 We present empirical evidence showing that option-implied risk measures (OIRMs) are substantially impacted by bid–ask spreads in underlying options. Asking prices are more sensitive to shocks than bids, leading to highly skewed distributions of spreads. We derive and estimate a model of market making that empirically matches these asymmetric responses as well as the time-series properties of bid–ask spreads. Using these estimates to obtain bias-corrected option quotes, we compute several popular OIRMs. We find that fear and risk premia associated with market events that affect the center of the return distribution or unpredictable return jumps are on average overstated when relying on option mid-quotes, whereas risk associated with return-tail events is larger once the bias has been corrected.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"1 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42085034","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 article extends Fama and French (FF) models of observed factors by introducing latent factors (LFs) to further extract information from FF residual returns. A diagonally dominant (DD) rather than a diagonal or sparse matrix structure is adopted in this study to estimate remaining covariance between disturbance terms. Such an enhanced factor (EF) model provides a more comprehensive analysis for portfolio selection in high dimensions and also has certain advantages of estimation stability and computational efficiency. It is shown that the proposed EF–DD approach achieves overall better performance than competing models in terms of portfolio variance and the net Sharpe ratio.
{"title":"An Enhanced Factor Model for Portfolio Selection in High Dimensions","authors":"Fangquan Shi, L. Shu, X. Gu","doi":"10.1093/jjfinec/nbac029","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac029","url":null,"abstract":"\u0000 This article extends Fama and French (FF) models of observed factors by introducing latent factors (LFs) to further extract information from FF residual returns. A diagonally dominant (DD) rather than a diagonal or sparse matrix structure is adopted in this study to estimate remaining covariance between disturbance terms. Such an enhanced factor (EF) model provides a more comprehensive analysis for portfolio selection in high dimensions and also has certain advantages of estimation stability and computational efficiency. It is shown that the proposed EF–DD approach achieves overall better performance than competing models in terms of portfolio variance and the net Sharpe ratio.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42955209","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}
Julian F Kölbel, Markus Leippold, Jordy Rillaerts, Qian Wang
We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant.
{"title":"Ask BERT: How Regulatory Disclosure of Transition and Physical Climate Risks Affects the CDS Term Structure","authors":"Julian F Kölbel, Markus Leippold, Jordy Rillaerts, Qian Wang","doi":"10.1093/jjfinec/nbac027","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac027","url":null,"abstract":"We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"9 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138527898","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 article investigates the estimation and inference of quantile impulse response functions. We propose a new estimation method using the idea of local projections by Jordà (2005). We establish consistency and asymptotic normality of the estimator, thereby enabling asymptotic inference. We also consider the confidence interval construction based on the stationary bootstrap and prove its consistency. Confirmatory simulation results and empirical practices on value-at-risk dynamics are provided.
{"title":"Estimation and Inference of Quantile Impulse Response Functions by Local Projections: With Applications to VaR Dynamics","authors":"Heejoon Han, Whayoung Jung, Ji Hyung Lee","doi":"10.1093/jjfinec/nbac026","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac026","url":null,"abstract":"This article investigates the estimation and inference of quantile impulse response functions. We propose a new estimation method using the idea of local projections by Jordà (2005). We establish consistency and asymptotic normality of the estimator, thereby enabling asymptotic inference. We also consider the confidence interval construction based on the stationary bootstrap and prove its consistency. Confirmatory simulation results and empirical practices on value-at-risk dynamics are provided.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"10 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138527906","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}
F. Audrino, Jonathan Chassot, Chen-Jui Huang, M. Knaus, M. Lechner, J. Ortega
We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms’ return, volatility, and trade volume dynamics. To this end, we apply causal machine learning on the earnings announcements of a wide cross-section of U.S. companies. This approach allows us to investigate firms’ price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic, financial, and aggregated investors’ moods in a properly defined causal framework. Our empirical results support the presence of (i) economically sizable differences in the effects among sentiment types that are mostly of a non-linear nature depending on the underlying economic and financial conditions; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors’ underreaction to news. In particular, we show that the difference in the average causal effects of the sentiment’s types is larger and more relevant when the general macroeconomic conditions are worse, the investors are pessimist about the behavior of the market and/or its uncertainty is higher, and in market regimes characterized by high stocks’ liquidity.
{"title":"How Does Post-Earnings Announcement Sentiment Affect Firms’ Dynamics? New Evidence from Causal Machine Learning","authors":"F. Audrino, Jonathan Chassot, Chen-Jui Huang, M. Knaus, M. Lechner, J. Ortega","doi":"10.1093/jjfinec/nbac018","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac018","url":null,"abstract":"\u0000 We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms’ return, volatility, and trade volume dynamics. To this end, we apply causal machine learning on the earnings announcements of a wide cross-section of U.S. companies. This approach allows us to investigate firms’ price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic, financial, and aggregated investors’ moods in a properly defined causal framework. Our empirical results support the presence of (i) economically sizable differences in the effects among sentiment types that are mostly of a non-linear nature depending on the underlying economic and financial conditions; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors’ underreaction to news. In particular, we show that the difference in the average causal effects of the sentiment’s types is larger and more relevant when the general macroeconomic conditions are worse, the investors are pessimist about the behavior of the market and/or its uncertainty is higher, and in market regimes characterized by high stocks’ liquidity.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49447466","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}
Self- and cross-excitation in point processes are commonly captured in the financial econometrics literature using a multivariate exponential memory kernel. In this article, the exponential assumption is relaxed and the resultant non-parametric memory kernel is estimated by a method based on second-order cumulants. The estimator is shown to be consistent and asymptotically normally distributed and performs well under simulation. An empirical application based on 10 international stock indices is presented. Two different indices of contagion between markets are constructed from the point process models in order to examine interconnection over time. A conclusion which emerges from these results is the assumption that a parametric kernel may be too restrictive as the application reveals interesting features, and in some cases substantial differences, between the exponential and non-parametric kernels.
{"title":"Estimating a Non-parametric Memory Kernel for Mutually Exciting Point Processes","authors":"A. Clements, A. Hurn, K. A. Lindsay, V. Volkov","doi":"10.1093/jjfinec/nbac022","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac022","url":null,"abstract":"\u0000 Self- and cross-excitation in point processes are commonly captured in the financial econometrics literature using a multivariate exponential memory kernel. In this article, the exponential assumption is relaxed and the resultant non-parametric memory kernel is estimated by a method based on second-order cumulants. The estimator is shown to be consistent and asymptotically normally distributed and performs well under simulation. An empirical application based on 10 international stock indices is presented. Two different indices of contagion between markets are constructed from the point process models in order to examine interconnection over time. A conclusion which emerges from these results is the assumption that a parametric kernel may be too restrictive as the application reveals interesting features, and in some cases substantial differences, between the exponential and non-parametric kernels.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48189664","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 Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time-series data structures, we focus on the sparse-group LASSO (sg-LASSO) estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low-dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sg-LASSO residuals. This leads to valid time-series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk–Nagaev inequality for a class of τ-mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news.
{"title":"High-Dimensional Granger Causality Tests with an Application to VIX and News","authors":"Andrii Babii, Eric Ghysels, Jonas Striaukas","doi":"10.1093/jjfinec/nbac023","DOIUrl":"https://doi.org/10.1093/jjfinec/nbac023","url":null,"abstract":"We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time-series data structures, we focus on the sparse-group LASSO (sg-LASSO) estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low-dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sg-LASSO residuals. This leads to valid time-series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk–Nagaev inequality for a class of τ-mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"41 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138527907","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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