Econometrics: Econometric & Statistical Methods - General eJournal最新文献

{"title":"Hawkes-driven stochastic volatility models: goodness-of-fit testing of alternative intensity specifications with S&P500 data","authors":"Iacopo Raffaelli, Simone Scotti, Giacomo Toscano","doi":"10.2139/ssrn.3941426","DOIUrl":"https://doi.org/10.2139/ssrn.3941426","url":null,"abstract":"We introduce a novel stochastic volatility model with price and volatility co-jumps driven by Hawkes processes and develop a feasible maximum-likelihood procedure to estimate the parameters driving the jump intensity. Using S &P500 high-frequency prices over the period May 2007–August 2021, we then perform a goodness-of-fit test of alternative jump intensity specifications and find that the hypothesis of the intensity being linear in the asset volatility provides the relatively best fit, thereby suggesting that jumps have a self-exciting nature.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76770255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Factor Risk Premia","authors":"P. Hansen, Maziar Kazemi","doi":"10.2139/ssrn.3934624","DOIUrl":"https://doi.org/10.2139/ssrn.3934624","url":null,"abstract":"This paper a develops novel statistical test of whether individual factor risk premia are identified from return data in multi-factor models. We give a necessary and sufficient condition for population identification of individual risk premia, which we call the kernel-orthogonality condition. This condition is weaker than the standard rank condition commonly assumed for linear factor models. Under misspecification, our condition ensures point identification of the risk premium with minimal pricing error. We show how to test this restriction directly in reduced-rank models. Finally, we apply our test methodology to assess identification of risk premia associated with consumption growth and intermediary leverage.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72610250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Herding in Probabilistic Forecasts","authors":"Yanwei Jia, J. Keppo, Ville A. Satopää","doi":"10.2139/ssrn.3674961","DOIUrl":"https://doi.org/10.2139/ssrn.3674961","url":null,"abstract":"Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public information may lower forecasting accuracy. This result, however, is derived only for point forecasts. In this paper, we consider experts’ probabilistic forecasts under herding, find a closed-form expression for the first two moments of a unique equilibrium forecast, and show that the experts report too similar locations and inflate the variance of their forecasts because of herding. Furthermore, we show that the negative externality of public information no longer holds. In addition to reacting to new information as expected, probabilistic forecasts contain more information about the experts’ full beliefs and interpersonal structure. This facilitates model estimation. To this end, we consider a one-shot setting with one forecast per expert and show that our model is identifiable up to an infinite number of solutions based on point forecasts but up to two solutions based on probabilistic forecasts. We then provide a Bayesian estimation procedure for these two solutions and apply it to economic forecasting data collected by the European Central Bank and the Federal Reserve Bank of Philadelphia. We find that, on average, the experts invest around 19% of their efforts into making similar forecasts. The level of herding shows an increasing trend from 1999 to 2007 but drops sharply during the financial crisis of 2007–2009 and then rises again until 2019. This paper was accepted by Yan Chen, behavioral economics and decision analysis.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77196535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Bias Robust Cross Section Factor Models","authors":"R. Martin, Daniel Z. Xia","doi":"10.2139/ssrn.3921175","DOIUrl":"https://doi.org/10.2139/ssrn.3921175","url":null,"abstract":"This paper introduces a theory based robust regression estimator, called the mOpt estimator, that minimizes the maximum bias with respect to a Tukey-Huber mixture model that includes a standard linear regression model with normally distribution errors as a special case, but also allows for a small fraction of unrestricted fat-tailed and skewed non-normal distribution variations from the standard model. The estimator has a very intuitive weighted least squares interpretation based on a data-dependent weight function that is equal to zero for robustly scaled prediction residuals that are larger in magnitude than 3.0, and thereby rejects outliers. We apply the robust regression method to single factor and multiple factor cross-section models for Size, BM, Beta and EP factors, and find that the robust regression results reverse the Fama-French 1992 (FF92) conclusions concerning the significance of the Size, Beta and EP factors. The difference in our results and those of FF92 is that the robust regression rejects approximately 4% to 5% of outliers, most of which, but not all, occur for microcap stocks, with smallcap stocks also having some influential outliers, and even largecaps have a few. We strongly recommend standard use of the mOpt estimator as an important complement to least squares for empirical asset pricing research, as well as for quantitative finance applications in general.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73063075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-factor, Age-Cohort, Affine Mortality Models: A Multi-Country Comparison","authors":"F. Ungolo, M. Sherris, Yuxin Zhou","doi":"10.2139/ssrn.3912981","DOIUrl":"https://doi.org/10.2139/ssrn.3912981","url":null,"abstract":"Affine mortality models, developed in continuous time, are well suited to longevity applications including pricing and risk management. Advantages of this modelling approach include closed-form derivations of cohort survival curves, with these survival curves consistent with the dynamics of mortality rates. We compare a number of multi-factor continuous-time affine models applied to age-cohort mortality data in a multi-country comparison of five countries with differing lengths of time-series mortality data. We develop improved estimation methods for these models and provide R code. Parameters are estimated using maximum likelihood with the univariate Kalman Filter, which accounts for the Poisson variation in the measurement equation. We show how this estimation method is faster and more robust compared to the traditional formulation which heavily uses large matrix multiplication and inversion. We also discuss and address numerical issues with the estimation process. We provide graphical and numerical goodness-of-fit checks and assess model robustness. We then project cohort survival curves and assess the out-of-sample performance of the analysed models. Although the CIR mortality model fits historical data well, particularly at older ages. Other affine mortality models provide better out-of-sample performance, although less so old ages. We show that the affine mortality models analysed are robust with respect to the set of age-cohort data used for parameter estimation.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89532509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P-range DCC: A Score-Driven Extension to Time-Varying Correlation Models","authors":"Philipp Prange","doi":"10.2139/ssrn.3912188","DOIUrl":"https://doi.org/10.2139/ssrn.3912188","url":null,"abstract":"We propose a score-driven extension to the well-known dynamic conditional correlation (DCC) model. The p-range DCC model provides means to capture the time-varying influence from news on correlation dynamics. The recursion to update the news parameter over time is based on the observations of past periods. By and large, the model increases the flexibility of DCC-type models whilst maintaining their appealing characteristics for applications with large cross-sections. We demonstrate that the model performs well in a variety of different situations and show that incorporation of the time-varying severity of news enriches the examination of correlation dynamics for a global cross-section of equity indices. More particularly, the article shows that the time-varying parameter can account for significant increases in equity return linkages in response to plunging markets amid the outbreak of COVID-19 in early 2020 and subsequent economic recoveries.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88266480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEMinR: Domain-Specific Language for Building, Estimating, and Visualizing Structural Equation Models in R","authors":"Soumya Ray, N. Danks, André Calero Valdez","doi":"10.2139/ssrn.3900621","DOIUrl":"https://doi.org/10.2139/ssrn.3900621","url":null,"abstract":"SEMinR seeks to bring the latest state-of-the-art advances in SEM methods to the R ecosystem. This package also seeks to make describing and analyzing SEMs easier for practitioners. There have been several recent advances in the various branches of SEM that are often not reflected in existing R packages. For example, the PLS-PM approach requires adjustment in how models with interaction terms are estimated. PLS-PM methods have recently incorporated predictive methods such as plsPredict. Meanwhile, CB-SEM approach can avail ten Berge factor-score extraction that obtains construct scores with the same correlation patterns as the latent factors themselves. CB-SEM researchers should also consider VIF scores in their regression models. SEMinR incorporates these and other advancements. Estimating an SEM using CB-SEM and PLS-PM requires different packages for the two estimation methods, which often requires researchers to wholly redescribe their models in different syntax. SEMinR allows researchers to describe their model once in a common syntax, and estimate the model using different estimation methods. SEMinr includes its own implementation of PLS-PM estimation that is tested against leading commercial applications to ensure comparable results. For CB-SEM estimation, SEMinR delegates the estimation to the popular Lavaan package. Regardless of which estimation method one uses, the results are structured in a similar way for reporting and visualization. R packages for SEM often use a custom syntax that does not correspond to any programming language; nor does the syntax not reflect the terminology of SEM with which practitioners are familiar. SEMinR offers researchers a domain-specific language for modeling SEMs that uses function names that evoke major SEM components: constructs, relationships, paths, reflective, composite, etc. As SEMinR’s syntax is built using R functions, researchers can inject their own custom functions to extend the behavior of SEMinR. SEMinR is the first package that allows researchers applying PLS-PM to visualize their graphical models and measurement qualities. Visualization of CB-SEM models is delegated to the semplot package. Moreover, SEMinR allows researchers to visualize models either before or after estimation.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"249 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74903847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Robust Testing for Trend","authors":"A. Skrobotov","doi":"10.2139/ssrn.3893116","DOIUrl":"https://doi.org/10.2139/ssrn.3893116","url":null,"abstract":"This paper provides a simple approach for robust testing for the trend function in the time series under uncertainty over the order of integration of the error term. The proposed approach relies on the asymptotic normality of the trend coefficient estimator and utilises t-statistic approach of Ibragimov and Muler (2010) based on splitting the sample. The Monte-Carlo results demonstrate that the approach has the correct finite sample size and favorable finite sample power properties for all data generating processes considered. The proposed approach is robust to very general assumptions on the error term including various forms of non-stationary volatility and heavy tails.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88646376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Skeptical Appraisal of Robust Asset Pricing Tests","authors":"Tim A. Kroencke, Julian Thimme","doi":"10.2139/ssrn.3875657","DOIUrl":"https://doi.org/10.2139/ssrn.3875657","url":null,"abstract":"We analyze the size and power of a large number of “robust” asset pricing tests, investigating the hypothesis that the price of risk of a candidate factor is equal to zero. Different from earlier studies, our bootstrap approach puts all tests on an equal footing and focuses on sample sizes comparable to standard applications in asset pricing research. Thus, our paper provides guidance for researchers about which method to use. We find that the classic Fama-MacBeth/Shanken approach rarely over-rejects useless factors and provides a reasonable balance between size and power. In contrast, some of the “robust” methods suffer from poor power in realistic sample sizes, especially in situations where the asset pricing model is mildly misspecified.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87903504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sieve BLP: A Semi-Nonparametric Model of Demand for Differentiated Products","authors":"Ao Wang","doi":"10.2139/ssrn.3569077","DOIUrl":"https://doi.org/10.2139/ssrn.3569077","url":null,"abstract":"We develop a semi-nonparametric approach to identify and estimate the demand for differentiated products. The proposed method adopts a random coefficients discrete choice logit model (i.e., mixed logit model) in which the distribution of random coefficients is nonparametrically specified. Our method minimizes misspecification error in this distribution to which routinely used parametric approach is subject. In addition, it overcomes the practical challenge of dimensionality in the number of products that remains the main hurdle in the nonparametric estimation of demand functions. We propose a sieve estimation procedure (referred to as sieve BLP) that remains simple to implement. Extensive Monte Carlo simulations show its robust finite-sample performance under various data generating processes. We use our method to investigate the welfare implications of a sugar tax in the ready-to-eat cereal industry in the US. This application underscores the usefulness of sieve BLP due to its ability to allow for flexibly specified individual heterogeneity in demand, especially when the researcher aims to quantify the distributional effects of a policy change.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74272332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}