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Specification Testing in Hawkes Models 霍克斯模型的规格测试
ERN: Time-Series Models (Single) (Topic) Pub Date : 2015-07-24 DOI: 10.2139/ssrn.2635436
F. Gresnigt, E. Kole, P. Franses
{"title":"Specification Testing in Hawkes Models","authors":"F. Gresnigt, E. Kole, P. Franses","doi":"10.2139/ssrn.2635436","DOIUrl":"https://doi.org/10.2139/ssrn.2635436","url":null,"abstract":"We propose various specification tests for Hawkes models based on the Lagrange Multiplier (LM) principle. Hawkes models can be used to model the occurrence of extreme events in financial markets. Our specific testing focus is on extending a univariate model to a multivariate model, that is, we examine whether there is a conditional dependence between extreme events in markets. Simulations show that the test has good size and power, in particular for sample sizes that are typically encountered in practice. Applying the specification test for dependence to US stocks, bonds and exchange rate data, we find strong evidence for cross-excitation within segments as well as between segments. Therefore, we recommend that univariate Hawkes models be extended to account for the cross-triggering phenomenon.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123869924","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}
引用次数: 7
L_1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations 具有柔性创新的高维时间序列模型的l_1 -正则化
ERN: Time-Series Models (Single) (Topic) Pub Date : 2015-07-03 DOI: 10.2139/ssrn.2626507
M. C. Medeiros, Eduardo F. Mendes
{"title":"L_1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations","authors":"M. C. Medeiros, Eduardo F. Mendes","doi":"10.2139/ssrn.2626507","DOIUrl":"https://doi.org/10.2139/ssrn.2626507","url":null,"abstract":"We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially orgeometrically). In other words, we let the number of candidate variables to be larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency) and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. This allows the adaLASSO to be applied to a myriad of applications in empirical finance and macroeconomics. A simulation study shows that the method performs well in very general settings with t-distributed and heteroskedastic errors as well with highly correlated regressors. Finally, we consider an application to forecast monthly US inflation with many predictors. The model estimated by the adaLASSO delivers superior forecasts than traditional benchmark competitors such as autoregressive and factor models.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127649440","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}
引用次数: 10
The Economic Value of Volatility Timing with Realized Jumps 具有已实现跳跃的波动时机的经济价值
ERN: Time-Series Models (Single) (Topic) Pub Date : 2015-01-29 DOI: 10.2139/ssrn.2406934
Ingmar Nolte, Qi Xu
{"title":"The Economic Value of Volatility Timing with Realized Jumps","authors":"Ingmar Nolte, Qi Xu","doi":"10.2139/ssrn.2406934","DOIUrl":"https://doi.org/10.2139/ssrn.2406934","url":null,"abstract":"This paper comprehensively investigates the role of realized jumps detected from high frequency data in predicting future volatility from both statistical and economic perspectives. Using seven major jump tests, we show that separating jumps from diffusion improves volatility forecasting both in-sample and out-of-sample. Moreover, we show that these statistical improvements can be translated into economic value. We find a risk-averse investor can significantly improve her portfolio performance by incorporating realized jumps into a volatility timing based portfolio strategy. Our results hold true across the majority of jump tests, and are robust to controlling for microstructure effects and transaction costs.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116543078","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}
引用次数: 16
When Does the Stock Market Listen to Economic News? New Evidence from Copulas and News Wires 股市什么时候会听经济新闻?来自Copulas和News Wires的新证据
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-10-30 DOI: 10.2139/ssrn.2578355
I. Medovikov
{"title":"When Does the Stock Market Listen to Economic News? New Evidence from Copulas and News Wires","authors":"I. Medovikov","doi":"10.2139/ssrn.2578355","DOIUrl":"https://doi.org/10.2139/ssrn.2578355","url":null,"abstract":"We study association between macroeconomic news and stock market returns using the statistical theory of copulas, and a new comprehensive measure of news based on textual review and classification of news wires. We find the impact of economic news on equity returns to be nonlinear and asymmetric. In particular, controlling for economic conditions and surprises associated with releases of economic data, we find that the market reacts strongly and negatively to the most unfavourable macroeconomic news, but appears to largely discount the good news. Further, the most-unfavorable news creates price drift, and we document that selling stocks short in the wake of unusually-bad news yields annual abnormal gross returns greater than four percent.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125416277","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}
引用次数: 41
Bayesian Forecasting of US Growth using Basic Time Varying Parameter Models and Expectations Data 基于基本时变参数模型和预期数据的美国经济增长贝叶斯预测
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-09-01 DOI: 10.2139/ssrn.2489956
N. Basturk, Pinar Ceyhan, H. V. Dijk
{"title":"Bayesian Forecasting of US Growth using Basic Time Varying Parameter Models and Expectations Data","authors":"N. Basturk, Pinar Ceyhan, H. V. Dijk","doi":"10.2139/ssrn.2489956","DOIUrl":"https://doi.org/10.2139/ssrn.2489956","url":null,"abstract":"Time varying patterns in US growth are analyzed using various univariate model structures, starting from a naive model structure where all features change every period to a model where the slow variation in the conditional mean and changes in the conditional variance are specified together with their interaction, including survey data on expected growth in order to strengthen the information in the model. Use is made of a simulation based Bayesian inferential method to determine the forecasting performance of the various model specifications. The extension of a basic growth model with a constant mean to models including time variation in the mean and variance requires careful investigation of possible identification issues of the parameters and existence conditions of the posterior under a diffuse prior. The use of diffuse priors leads to a focus on the likelihood fu nction and it enables a researcher and policy adviser to evaluate the scientific information contained in model and data. Empirical results indicate that incorporating time variation in mean growth rates as well as in volatility are important in order to improve for the predictive performances of growth models. Furthermore, using data information on growth expectations is important for forecasting growth in specific periods, such as the the recession periods around 2000s and around 2008.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701796","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}
引用次数: 0
Extreme-Quantile Tracking for Financial Time Series 金融时间序列的极端分位数跟踪
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-07-01 DOI: 10.2139/ssrn.1884053
V. Chavez-Demoulin, P. Embrechts, S. Sardy
{"title":"Extreme-Quantile Tracking for Financial Time Series","authors":"V. Chavez-Demoulin, P. Embrechts, S. Sardy","doi":"10.2139/ssrn.1884053","DOIUrl":"https://doi.org/10.2139/ssrn.1884053","url":null,"abstract":"Time series of financial asset values exhibit well-known statistical features such as heavy tails and volatility clustering. We propose a nonparametric extension of the classical Peaks-Over-Threshold method from extreme value theory to fit the time varying volatility in situations where the stationarity assumption may be violated by erratic changes of regime, say. As a result, we provide a method for estimating conditional risk measures applicable to both stationary and nonstationary series. A backtesting study for the UBS share price over the subprime crisis exemplifies our approach.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116761435","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}
引用次数: 93
COMFORT: A Common Market Factor Non-Gaussian Returns Model COMFORT:一个共同市场因素非高斯收益模型
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-06-18 DOI: 10.2139/ssrn.2287793
Marc S. Paolella, Pawel Polak
{"title":"COMFORT: A Common Market Factor Non-Gaussian Returns Model","authors":"Marc S. Paolella, Pawel Polak","doi":"10.2139/ssrn.2287793","DOIUrl":"https://doi.org/10.2139/ssrn.2287793","url":null,"abstract":"A new multivariate time series model with various attractive properties is motivated and studied. By extending the CCC model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility clustering, non-normality (excess kurtosis and asymmetry), and also dynamics in the dependency between assets over time. A fast EM-algorithm is developed for estimation. Each element of the vector return at time t is endowed with a common univariate shock, interpretable as a common market factor. This leads to the new model being a hybrid of GARCH and stochastic volatility, but without the estimation problems associated with the latter, and being applicable in the multivariate setting for potentially large numbers of assets. A feasible technique which allows for multivariate option pricing is presented, along with an empirical illustration.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116392179","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}
引用次数: 27
Real-Time Factor Model Forecasting and the Effects of Instability 实时因子模型预测及不稳定性的影响
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-05-31 DOI: 10.2139/ssrn.2444113
Michael P. Clements
{"title":"Real-Time Factor Model Forecasting and the Effects of Instability","authors":"Michael P. Clements","doi":"10.2139/ssrn.2444113","DOIUrl":"https://doi.org/10.2139/ssrn.2444113","url":null,"abstract":"Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123408378","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}
引用次数: 16
Subsampling of Almost Periodic Models for Weakly Dependent Times Series 弱相关时间序列概周期模型的子抽样
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-05-25 DOI: 10.2139/ssrn.2784256
Eva Pidwerbitzka
{"title":"Subsampling of Almost Periodic Models for Weakly Dependent Times Series","authors":"Eva Pidwerbitzka","doi":"10.2139/ssrn.2784256","DOIUrl":"https://doi.org/10.2139/ssrn.2784256","url":null,"abstract":"This paper applies the approach of weakly dependent times series to almost periodic models.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114795555","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}
引用次数: 0
Measuring Macroeconomic Uncertainty: US Inflation and Output Growth 衡量宏观经济不确定性:美国通胀与产出增长
ERN: Time-Series Models (Single) (Topic) Pub Date : 2014-05-14 DOI: 10.2139/SSRN.2436810
Michael P. Clements, A. Galvão
{"title":"Measuring Macroeconomic Uncertainty: US Inflation and Output Growth","authors":"Michael P. Clements, A. Galvão","doi":"10.2139/SSRN.2436810","DOIUrl":"https://doi.org/10.2139/SSRN.2436810","url":null,"abstract":"We find that model estimates of the term structure of ex ante or perceived macro uncertainty are more in line with realized uncertainty than survey respondents' perceptions for both inflation and output growth. Survey estimates contain short-term vari- ation in short-horizon uncertainty which is less evident in the model-based estimates. We consider the extent to which these short-term variations coincide with short-term movements in stock market uncertainty.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126237554","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}
引用次数: 14
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