Abstract We propose a stochastic price duration model to estimate high-frequency volatility. A price duration is directly linked to volatility from the passage time theory for Brownian motions, and it possesses several advantages over returns for estimating volatility. We employ price durations in a parametric model that directly specifies stochastic volatility dynamics. Our approach allows us to estimate intraday spot volatility and our empirical results suggest the presence of important intraday volatility dynamics. We conduct an extensive integrated variance forecast comparison, which demonstrates the superior performance of our proposed models compared with other duration-based or return-based estimators.
{"title":"A Stochastic Price Duration Model for Estimating High-Frequency Volatility","authors":"Denis Pelletier, Wei Wei","doi":"10.1093/jjfinec/nbad029","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad029","url":null,"abstract":"Abstract We propose a stochastic price duration model to estimate high-frequency volatility. A price duration is directly linked to volatility from the passage time theory for Brownian motions, and it possesses several advantages over returns for estimating volatility. We employ price durations in a parametric model that directly specifies stochastic volatility dynamics. Our approach allows us to estimate intraday spot volatility and our empirical results suggest the presence of important intraday volatility dynamics. We conduct an extensive integrated variance forecast comparison, which demonstrates the superior performance of our proposed models compared with other duration-based or return-based estimators.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"48 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135413819","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 Alexei Onatski Alexei Onatski Faculty of Economics, University of Cambridge, Cambridge, CB3 9DD, UK ao319@cam.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Journal of Financial Econometrics, nbad028, https://doi.org/10.1093/jjfinec/nbad028 Published: 20 October 2023 Article history Received: 26 September 2023 Editorial decision: 29 September 2023 Accepted: 02 October 2023 Published: 20 October 2023
{"title":"Comment on: Eigenvalue Tests for the Number of Latent Factors in Short Panels","authors":"Alexei Onatski","doi":"10.1093/jjfinec/nbad028","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad028","url":null,"abstract":"Journal Article Comment on: Eigenvalue Tests for the Number of Latent Factors in Short Panels Get access Alexei Onatski Alexei Onatski Faculty of Economics, University of Cambridge, Cambridge, CB3 9DD, UK ao319@cam.ac.uk Search for other works by this author on: Oxford Academic Google Scholar Journal of Financial Econometrics, nbad028, https://doi.org/10.1093/jjfinec/nbad028 Published: 20 October 2023 Article history Received: 26 September 2023 Editorial decision: 29 September 2023 Accepted: 02 October 2023 Published: 20 October 2023","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618696","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}
Abstract The higher order dynamics of individual stocks is investigated. We show that classical powers correlation analysis can lead to a spurious assessment of the volatility persistence or long memory volatility effects, if the zero return probability is non-constant over time. In other words, classical tools are not able to distinguish between long-run volatility effects, such as IGARCH, and the case where the zero returns are not evenly distributed over time. As a remedy, new diagnostic tools are proposed that are robust to changes in the zero return probability. Since a time-varying zero return probability could potentially be accompanied by a non-constant unconditional variance, we also develop powers correlation analysis that is robust in such a case. In addition, the diagnostic tools we propose offer a rigorous analysis of the short-run volatility effects, while the use of the classical powers correlations lead to doubtful conclusions. Monte Carlo experiments, and the study of the absolute value correlation of daily returns taken from the Chilean financial market and the 1-min returns of Facebook stocks, suggest that the volatility effects are only short-run in many cases.
{"title":"Powers Correlation Analysis of Returns with a Non-stationary Zero-Process","authors":"Valentin Patilea, Hamdi Raïssi","doi":"10.1093/jjfinec/nbad025","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad025","url":null,"abstract":"Abstract The higher order dynamics of individual stocks is investigated. We show that classical powers correlation analysis can lead to a spurious assessment of the volatility persistence or long memory volatility effects, if the zero return probability is non-constant over time. In other words, classical tools are not able to distinguish between long-run volatility effects, such as IGARCH, and the case where the zero returns are not evenly distributed over time. As a remedy, new diagnostic tools are proposed that are robust to changes in the zero return probability. Since a time-varying zero return probability could potentially be accompanied by a non-constant unconditional variance, we also develop powers correlation analysis that is robust in such a case. In addition, the diagnostic tools we propose offer a rigorous analysis of the short-run volatility effects, while the use of the classical powers correlations lead to doubtful conclusions. Monte Carlo experiments, and the study of the absolute value correlation of daily returns taken from the Chilean financial market and the 1-min returns of Facebook stocks, suggest that the volatility effects are only short-run in many cases.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136025839","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}
Alain-Philippe Fortin, Patrick Gagliardini, Olivier Scaillet
Abstract This article studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance–covariance matrices of (possibly weighted) asset returns and rely on either an assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to U.S. equity data. Evidence for a different number of latent factors according to market downturns and market upturns is statistically ambiguous in the considered subperiods. In particular, our results contradict the common wisdom of a single-factor model in bear markets.
{"title":"Eigenvalue Tests for the Number of Latent Factors in Short Panels","authors":"Alain-Philippe Fortin, Patrick Gagliardini, Olivier Scaillet","doi":"10.1093/jjfinec/nbad024","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad024","url":null,"abstract":"Abstract This article studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance–covariance matrices of (possibly weighted) asset returns and rely on either an assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to U.S. equity data. Evidence for a different number of latent factors according to market downturns and market upturns is statistically ambiguous in the considered subperiods. In particular, our results contradict the common wisdom of a single-factor model in bear markets.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136363932","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}
I propose a novel measure of information share, termed tail information share (TIS), which focuses on modeling the tail dependence of price innovations using copulas. I discuss its detailed technical properties, including unique identifiability, estimation procedures, and statistical properties. The proposed TIS improves over two commonly used measures by providing meaningful economic rationale and unique identifiability. My simulation studies indicate that TIS can yield more accurate estimates of market-specific contributions to price discovery when tail dependence is present. Additionally, I demonstrate the asymptotic consistency and efficiency of TIS estimators. An empirical illustration is provided using a new dataset of high-frequency crude oil futures.
{"title":"A Tale of Two Tails: A New Unique Information Share Measure Based on Copulas","authors":"Yanlin Shi","doi":"10.1093/jjfinec/nbad023","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad023","url":null,"abstract":"\u0000 I propose a novel measure of information share, termed tail information share (TIS), which focuses on modeling the tail dependence of price innovations using copulas. I discuss its detailed technical properties, including unique identifiability, estimation procedures, and statistical properties. The proposed TIS improves over two commonly used measures by providing meaningful economic rationale and unique identifiability. My simulation studies indicate that TIS can yield more accurate estimates of market-specific contributions to price discovery when tail dependence is present. Additionally, I demonstrate the asymptotic consistency and efficiency of TIS estimators. An empirical illustration is provided using a new dataset of high-frequency crude oil futures.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45566719","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 a model for interval-valued time series that specifies the conditional joint distribution of the upper and lower bounds as a mixture of truncated bivariate normal distributions. It preserves the interval natural order and provides great flexibility on capturing potential conditional heteroscedasticity and non-Gaussian features. The standard expectation maximization (EM) algorithm applied to truncated mixtures does not provide a closed-form solution in the M step. A new EM algorithm solves this problem. The model applied to the interval-valued IBM daily stock returns exhibits superior performance over competing models in-sample and out-of-sample evaluation. A trading strategy showcases the usefulness of our approach.
{"title":"A Truncated Mixture Transition Model for Interval-Valued Time Series","authors":"Yunzhao Luo, Gloria González-Rivera","doi":"10.1093/jjfinec/nbad022","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad022","url":null,"abstract":"\u0000 We propose a model for interval-valued time series that specifies the conditional joint distribution of the upper and lower bounds as a mixture of truncated bivariate normal distributions. It preserves the interval natural order and provides great flexibility on capturing potential conditional heteroscedasticity and non-Gaussian features. The standard expectation maximization (EM) algorithm applied to truncated mixtures does not provide a closed-form solution in the M step. A new EM algorithm solves this problem. The model applied to the interval-valued IBM daily stock returns exhibits superior performance over competing models in-sample and out-of-sample evaluation. A trading strategy showcases the usefulness of our approach.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48897872","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}
Rustam Ibragimov, Rasmus Pedersen, Anton Skrobotov
Abstract We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019, J. Financ. Econ., 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study.
摘要:我们提出了新的、鲁棒的方法来推断市场(非)效率、波动性聚类和金融收益序列的非线性依赖。与现有方法相比，我们提出的方法对非线性动力学和尾重收益具有鲁棒性。具体地说，我们的方法只依赖于返回过程是平稳的和弱依赖的(混合)，具有适当顺序的有限矩。这包括对与非线性动态模型(如GARCH和随机波动)相关的幂律分布的鲁棒性。该方法易于实现，在实际环境中表现良好。我们回顾了Baltussen, van Bekkum和Da (2019, J. finance)最近的一项研究。经济学。[j] .证券学报，2013,26 - 48)。使用我们稳健的方法，我们证明，与原始研究的结论相比，存在负自相关的证据较弱。
{"title":"New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence","authors":"Rustam Ibragimov, Rasmus Pedersen, Anton Skrobotov","doi":"10.1093/jjfinec/nbad020","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad020","url":null,"abstract":"Abstract We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019, J. Financ. Econ., 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135746224","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}
A point process for event arrivals in high-frequency trading is presented. The intensity is the product of a Hawkes process and high-dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high-dimensional space. Large sample sizes can be common for high-frequency data applications using multiple instruments. Consistency results under weak conditions are established. A test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange. The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self-exciting nature of high-frequency trading events.
{"title":"Estimation of an Order Book Dependent Hawkes Process for Large Datasets","authors":"Luca Mucciante, Alessio Sancetta","doi":"10.1093/jjfinec/nbad021","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad021","url":null,"abstract":"\u0000 A point process for event arrivals in high-frequency trading is presented. The intensity is the product of a Hawkes process and high-dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high-dimensional space. Large sample sizes can be common for high-frequency data applications using multiple instruments. Consistency results under weak conditions are established. A test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange. The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self-exciting nature of high-frequency trading events.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47239764","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}
{"title":"Correction to: A New Test on Asset Return Predictability with Structural Breaks","authors":"","doi":"10.1093/jjfinec/nbad019","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad019","url":null,"abstract":"","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44080270","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 generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the case of no disagreement among investors. In contrast, the elasticity estimates decrease with increases in the absolute value of moneyness, indicating investors have a higher disagreement about rare events. Likewise, the elasticity decreases with increases in tenor, implying higher investors’ disagreement about more distant events.
{"title":"Disagreement in Market Index Options","authors":"Guilherme Salomé, George Tauchen, Jia Li","doi":"10.1093/jjfinec/nbad017","DOIUrl":"https://doi.org/10.1093/jjfinec/nbad017","url":null,"abstract":"\u0000 We generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the case of no disagreement among investors. In contrast, the elasticity estimates decrease with increases in the absolute value of moneyness, indicating investors have a higher disagreement about rare events. Likewise, the elasticity decreases with increases in tenor, implying higher investors’ disagreement about more distant events.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45737101","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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