T. Andersen, Ilya Archakov, Leon Eric Grund, N. Hautsch, Yifan Li, S. Nasekin, Ingmar Nolte, Manh Cuong Pham, Stephen L Taylor, V. Todorov
This paper provides a guide to high frequency option trade and quote data disseminated by the Options Price Reporting Authority (OPRA). We present a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges. We review the existing literature that utilizes high-frequency options data, summarize the general structure of the OPRA dataset and present a thorough empirical description of the observed option trades and quotes for a selected sample of underlying assets that contains more than 25 billion records. We outline several types of irregular observations and provide recommendations for data filtering and cleaning. Finally, we illustrate the usefulness of the high frequency option data with two empirical applications: option-implied variance estimation and risk-neutral density estimation. Both applications highlight the rich information content of the high frequency OPRA data.
{"title":"A Descriptive Study of High-Frequency Trade and Quote Option Data*","authors":"T. Andersen, Ilya Archakov, Leon Eric Grund, N. Hautsch, Yifan Li, S. Nasekin, Ingmar Nolte, Manh Cuong Pham, Stephen L Taylor, V. Todorov","doi":"10.1093/JJFINEC/NBAA036","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA036","url":null,"abstract":"This paper provides a guide to high frequency option trade and quote data disseminated by the \u0000Options Price Reporting Authority (OPRA). We present a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges. We review the existing literature that utilizes high-frequency options data, summarize the general structure of the OPRA dataset and present a thorough empirical description of the observed option trades and quotes for a selected sample of underlying assets that contains more than 25 billion records. We outline several types of irregular observations and provide recommendations for data filtering and cleaning. Finally, we illustrate the usefulness of the high frequency option data with two empirical applications: option-implied variance estimation and risk-neutral density estimation. Both applications highlight the rich information content of the high frequency OPRA data.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"19 1","pages":"128-177"},"PeriodicalIF":2.5,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45671257","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 introduce an index of market return autocorrelation based on the prices of index options and of forward-start index options and implement it at a six-month horizon. The results suggest that the autocorrelation of the S&P 500 index was close to zero before the subprime crisis but was negative in its aftermath, attaining values around –20% to –30%. I speculate that this may reflect market perceptions about the likely reaction, via quantitative easing, of policymakers to future market moves.
{"title":"On the Autocorrelation of the Stock Market*","authors":"Ian Martin","doi":"10.1093/JJFINEC/NBAA033","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA033","url":null,"abstract":"I introduce an index of market return autocorrelation based on the prices of index options and of forward-start index options and implement it at a six-month horizon. The results suggest that the autocorrelation of the S&P 500 index was close to zero before the subprime crisis but was negative in its aftermath, attaining values around –20% to –30%. I speculate that this may reflect market perceptions about the likely reaction, via quantitative easing, of policymakers to future market moves.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"19 1","pages":"39-52"},"PeriodicalIF":2.5,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42461163","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 investigate the effects of constraining gross-exposure and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller versus larger universe of stocks, alternative portfolio formation objectives (global minimum variance versus exposure to profitable factors), and various transaction cost assumptions, we find that a judiciously chosen shrinkage method always outperforms an arbitrarily determined constraint on gross-exposure. We extend the mathematical connection between constraints on the gross-exposure and shrinkage of the covariance matrix from static to dynamic, and provide a new explanation for our finding from the perspective of degrees of freedom. In addition, both simulation and empirical analysis show that the dynamic conditional correlation-nonlinear shrinkage (DCC-NL) estimator results in risk reduction and efficiency increase in large portfolios as long as a small amount of short position is allowed, whereas imposing a constraint on gross-exposure often hurts a DCC-NL portfolio.
{"title":"Risk Reduction and Efficiency Increase in Large Portfolios: Gross-Exposure Constraints and Shrinkage of the Covariance Matrix","authors":"Zhao Zhao, Olivier Ledoit, Hui Jiang","doi":"10.1093/JJFINEC/NBAB001","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAB001","url":null,"abstract":"\u0000 We investigate the effects of constraining gross-exposure and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller versus larger universe of stocks, alternative portfolio formation objectives (global minimum variance versus exposure to profitable factors), and various transaction cost assumptions, we find that a judiciously chosen shrinkage method always outperforms an arbitrarily determined constraint on gross-exposure. We extend the mathematical connection between constraints on the gross-exposure and shrinkage of the covariance matrix from static to dynamic, and provide a new explanation for our finding from the perspective of degrees of freedom. In addition, both simulation and empirical analysis show that the dynamic conditional correlation-nonlinear shrinkage (DCC-NL) estimator results in risk reduction and efficiency increase in large portfolios as long as a small amount of short position is allowed, whereas imposing a constraint on gross-exposure often hurts a DCC-NL portfolio.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAB001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49521718","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 examine the tail systemic risk between the global financial system and financial institutions that belong to different industry groups. Our main contribution is the development of a systemic risk measure Delta Quantile-Located Conditional Autoregressive Expected Shortfall, ΔQLMV−CoCARES. This new measure captures the extreme downside risk in terms of the ES of the system should both the financial system and the institution simultaneously be in distress. The evidence suggests that cross significant volatility and ES effects exist between the system and financial institutions. Furthermore, our measure presents better forecasting performance than standard or novel systemic risk measures based on VaR such as CoVaR or ΔQLMV−CoCAViaR and it is effective at predicting financial crises. We also develop a new systemic stress indicator SSIES based on ΔQLMV−CoCARES systemic risk measure which presents higher forecasting ability than other standard stress indicators.
{"title":"Measuring Systemic Risk Using Multivariate Quantile-Located ES Models","authors":"Laura Garcia-Jorcano, Lidia Sanchis-Marco","doi":"10.1093/JJFINEC/NBAA050","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA050","url":null,"abstract":"\u0000 We examine the tail systemic risk between the global financial system and financial institutions that belong to different industry groups. Our main contribution is the development of a systemic risk measure Delta Quantile-Located Conditional Autoregressive Expected Shortfall, ΔQLMV−CoCARES. This new measure captures the extreme downside risk in terms of the ES of the system should both the financial system and the institution simultaneously be in distress. The evidence suggests that cross significant volatility and ES effects exist between the system and financial institutions. Furthermore, our measure presents better forecasting performance than standard or novel systemic risk measures based on VaR such as CoVaR or ΔQLMV−CoCAViaR and it is effective at predicting financial crises. We also develop a new systemic stress indicator SSIES based on ΔQLMV−CoCARES systemic risk measure which presents higher forecasting ability than other standard stress indicators.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":"1 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41519882","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}
Empirical evidence for multivariate stock suggests that there are changes from asymptotic independence to asymptotic dependence and vice versa. Under asymptotic independence, the probability of joint extremes vanishes, whereas under asymptotic dependence, this probability remains positive. In this paper, we propose a dynamic model for bivariate extremes that allows for smooth transitions between regimes of asymptotic independence and asymptotic dependence. In doing so, we ignore the bulk of the distribution and only model the joint tail of interest. We propose a maximum-likelihood estimator for the model parameters and demonstrate its accuracy in simulations. An empirical application to losses on the CAC 40 and DAX 30 illustrates that our model provides a detailed description of changes in the extremal dependence structure. Furthermore, we show that our model issues adequate forecasts of systemic risk, as measured by CoVaR. Finally, we find some evidence that our CoVaR forecasts outperform those of a benchmark dynamic t-copula model.returns
{"title":"Modeling Time-Varying Tail Dependence, with Application to Systemic Risk Forecasting*","authors":"Y. Hoga","doi":"10.1093/JJFINEC/NBAA043","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA043","url":null,"abstract":"\u0000 Empirical evidence for multivariate stock suggests that there are changes from asymptotic independence to asymptotic dependence and vice versa. Under asymptotic independence, the probability of joint extremes vanishes, whereas under asymptotic dependence, this probability remains positive. In this paper, we propose a dynamic model for bivariate extremes that allows for smooth transitions between regimes of asymptotic independence and asymptotic dependence. In doing so, we ignore the bulk of the distribution and only model the joint tail of interest. We propose a maximum-likelihood estimator for the model parameters and demonstrate its accuracy in simulations. An empirical application to losses on the CAC 40 and DAX 30 illustrates that our model provides a detailed description of changes in the extremal dependence structure. Furthermore, we show that our model issues adequate forecasts of systemic risk, as measured by CoVaR. Finally, we find some evidence that our CoVaR forecasts outperform those of a benchmark dynamic t-copula model.returns","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44220590","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}
J. Westerlund, Hande Karabıyık, P. Narayan, S. Narayan
Dynamic panel data regression models with fixed effects to account for unobserved heterogeneity are standard econometric tools. It is not until recently, however, that the problems involved when fitting such regressions to leverage data have been investigated. The main problem is that models of leverage are extremely noisy, much more so than what can be accommodated using fixed effects. The present article can be seen as a reaction to this. The purpose is to consider a more general interactive effects model in which there are multiple time effects, each with their own firm-specific sensitivities. Our empirical results suggest that proper accounting for the interactive effects and the bias that they cause leads to a marked increase in the estimated speed of adjustment to target leverage.
{"title":"Estimating the Speed of Adjustment of Leverage in the Presence of Interactive Effects*","authors":"J. Westerlund, Hande Karabıyık, P. Narayan, S. Narayan","doi":"10.1093/JJFINEC/NBAB002","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAB002","url":null,"abstract":"\u0000 Dynamic panel data regression models with fixed effects to account for unobserved heterogeneity are standard econometric tools. It is not until recently, however, that the problems involved when fitting such regressions to leverage data have been investigated. The main problem is that models of leverage are extremely noisy, much more so than what can be accommodated using fixed effects. The present article can be seen as a reaction to this. The purpose is to consider a more general interactive effects model in which there are multiple time effects, each with their own firm-specific sensitivities. Our empirical results suggest that proper accounting for the interactive effects and the bias that they cause leads to a marked increase in the estimated speed of adjustment to target leverage.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAB002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47734944","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}
Christophe Chorro, Rahantamialisoa H Fanirisoa Zazaravaka
In this article, we discuss the pricing performances of a large collection of GARCH models by questioning the global synergy between the choice of the affine/nonaffine GARCH specification, the use of competing alternatives to the Gaussian distribution, the selection of an appropriate pricing kernel, and the choice of different estimation strategies based on several sets of financial information. Furthermore, the study answers an important question in relation to the correlation between the performance of a pricing scheme and its ability to forecast VIX dynamics. VIX analysis clearly appears as a parsimonious first-stage filter to discard the worst GARCH option pricing models.
{"title":"Discriminating Between GARCH Models for Option Pricing by Their Ability to Compute Accurate VIX Measures","authors":"Christophe Chorro, Rahantamialisoa H Fanirisoa Zazaravaka","doi":"10.1093/JJFINEC/NBAA042","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA042","url":null,"abstract":"\u0000 In this article, we discuss the pricing performances of a large collection of GARCH models by questioning the global synergy between the choice of the affine/nonaffine GARCH specification, the use of competing alternatives to the Gaussian distribution, the selection of an appropriate pricing kernel, and the choice of different estimation strategies based on several sets of financial information. Furthermore, the study answers an important question in relation to the correlation between the performance of a pricing scheme and its ability to forecast VIX dynamics. VIX analysis clearly appears as a parsimonious first-stage filter to discard the worst GARCH option pricing models.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43239542","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}
The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises on the linkage variables that compare conventional regression methods with popular machine learning techniques.
{"title":"Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks*","authors":"Yue Qiu, Tian Xie, Jun Yu, Qiankun Zhou","doi":"10.1093/JJFINEC/NBAA005","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA005","url":null,"abstract":"\u0000 The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises on the linkage variables that compare conventional regression methods with popular machine learning techniques.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48546955","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 considerable number of individuals infected by COVID-19 died in self-isolation. This paper uses a graphical inference method to examine if patients were endogenously assigned to self-isolation during the early phase of COVID-19 epidemic in Ontario. The endogeneity of patient assignment is evaluated from a dependence measure revealing relationships between patients’ characteristics and their location at the time of death. We test for absence of assignment endogeneity in daily samples and study the dynamic of endogeneity. This methodology is applied to patients’ characteristics, such as age, gender, location of the diagnosing health unit, presence of symptoms, and underlying health conditions.
{"title":"Testing for Endogeneity of Covid-19 Patient Assignments*","authors":"C. Gouriéroux, Antoine A. Djogbenou, J. Jasiak","doi":"10.1093/JJFINEC/NBAA047","DOIUrl":"https://doi.org/10.1093/JJFINEC/NBAA047","url":null,"abstract":"\u0000 A considerable number of individuals infected by COVID-19 died in self-isolation. This paper uses a graphical inference method to examine if patients were endogenously assigned to self-isolation during the early phase of COVID-19 epidemic in Ontario. The endogeneity of patient assignment is evaluated from a dependence measure revealing relationships between patients’ characteristics and their location at the time of death. We test for absence of assignment endogeneity in daily samples and study the dynamic of endogeneity. This methodology is applied to patients’ characteristics, such as age, gender, location of the diagnosing health unit, presence of symptoms, and underlying health conditions.","PeriodicalId":47596,"journal":{"name":"Journal of Financial Econometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2021-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JJFINEC/NBAA047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46550687","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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