ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)最新文献_第7页

Robust Inference In Time-Varying Structural VAR Models: The DC-Cholesky Multivariate Stochastic Volatility Model 时变结构VAR模型的鲁棒推断:DC-Cholesky多元随机波动模型

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-10-09 DOI: 10.2139/ssrn.3466739

Benny Hartwig

引用次数: 8

Benchmarking Global Optimizers 评测全局优化器

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-10-01 DOI: 10.3386/w26340

Antoine Arnoud, Fatih Guvenen, Tatjana Kleineberg

{"title":"Benchmarking Global Optimizers","authors":"Antoine Arnoud, Fatih Guvenen, Tatjana Kleineberg","doi":"10.3386/w26340","DOIUrl":"https://doi.org/10.3386/w26340","url":null,"abstract":"We benchmark seven global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are taken from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL) algorithm, (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The other two algorithms are versions of TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Non-linear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use a set of benchmarking tools recently developed in the applied mathematics literature. We find that the success rate of many optimizers vary dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer on both the math test functions and the economic application. The next-best performing optimizers are StoGo and CRS for the test functions and MLSL for the economic application.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114853887","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: 37

Online Appendix to Accompany 'Parameter Calibration and Rediscovery in the Bates Model' “贝茨模型中的参数校准和重新发现”的在线附录

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-09-12 DOI: 10.2139/ssrn.3453486

Ruixian Huang, Chenghao Li, Yaofei Xu, Yukun Shi

引用次数: 0

Methodological Redirections for an Evolutionary Approach of the External Business Environment 外部商业环境演化方法的方法论重定向

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-08-29 DOI: 10.5539/jms.v9n2p25

Charis Vlados, Dimos Chatzinikolaou

{"title":"Methodological Redirections for an Evolutionary Approach of the External Business Environment","authors":"Charis Vlados, Dimos Chatzinikolaou","doi":"10.5539/jms.v9n2p25","DOIUrl":"https://doi.org/10.5539/jms.v9n2p25","url":null,"abstract":"The usual strategic analysis perceives the external business environment fragmentarily and without a coherent and unifying way. The three levels that a typical analysis of the external business environment involves are a) the macroenvironment and PEST analysis, b) mesoenvironment and “Porter’s diamond”, and c) industrial environment and “Porter’s five forces”. Contrary to the fragmentary analysis of the three levels, this article aims to counter-propose a restructured method of a unified and evolutionary analysis of the external business environment. After presenting the usual analytical handling of the external business environment in the three levels, we suggest that these are rather co-evolving than separate and autonomous spheres of analysis. Therefore, after introducing some elements of the evolutionary socioeconomic theory, we propose a systemic web that perceives the external environment of the socioeconomic organisations in dynamically unified and evolutionary terms. The systemic web conceptualises the approach of the external socioeconomic environment as an open and interactive system comprising three co-evolving spheres in the context of global dynamics: the institutional character of each spatially structured socioeconomic formation; the firm’s functions within the system; and the public-state intervention that contributes to the establishment and reproduction of the system. This conceptual redirection of the methodology of the external business environment can be useful for building an integrated strategic analysis that studies all “micro-meso-macro” components of the entire socioeconomic system.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124938255","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

On the Sparsity of Mallows’ Model Averaging Estimator 关于Mallows模型平均估计的稀疏性

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-07-23 DOI: 10.2139/ssrn.3425424

Yang Feng, Qingfeng Liu, R. Okui

引用次数: 0

Dissecting Momentum: We Need to Go Deeper 剖析动量:我们需要更深入

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-07-23 DOI: 10.2139/ssrn.3424793

Dmitry Borisenko

{"title":"Dissecting Momentum: We Need to Go Deeper","authors":"Dmitry Borisenko","doi":"10.2139/ssrn.3424793","DOIUrl":"https://doi.org/10.2139/ssrn.3424793","url":null,"abstract":"Cross-sectional predictability of returns by past prices, or momentum, is a lasting market anomaly. Previous research reports numerous ways to measure momentum and establishes a multitude of factors predicting its performance. The emerging machine learning asset pricing literature further identifies price-based firm characteristics as major predictors of returns. I investigate predictive power of a broad set of price-based variables over various time horizons in a deep learning framework and document rich non-linear structure in impact of these variables on expected returns in the US equity market. The magnitude and sign of the impact exhibit substantial time variation and are modulated by interaction effects among the variables. The degree of non-linearity in expected returns varies over time and is highest in distressed markets. Incorporating insights from the literature on time-varying, market state-dependent momentum risks and momentum crashes helps to improve out-of-sample performance of neural network portfolios, especially with respect to the downside risk -- investment strategies built on predictions of the deep learning model actively exploit the non-linearities and interaction effects, generating high and statistically significant returns with a robust risk profile and their performance virtually uncorrelated with the established risk factors including momentum. Lastly, I make a case for adoption of automated hyperparameter optimization techniques as an important component of disciplined research in financial machine learning.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237741","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: 1

Company Valuation as Result of Risk Analysis: Replication Approach as an Alternative to the CAPM 作为风险分析结果的公司估值:作为CAPM替代方案的复制方法

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-06-07 DOI: 10.2139/ssrn.3458862

Werner Gleißner, Dietmar Ernst

引用次数: 7

Investmentstrategien im Rahmen von Übernahmen börsennotierter Gesellschaften – Merger Arbitrage und Maschinelles Lernen (Merger Arbitrage and Machine Learning) 通过大规模上市公司套利和机器学习进行投资战略

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-06-01 DOI: 10.2139/ssrn.3454408

Frank B. Lehrbaß, Alexander Raasch

引用次数: 0

Statistical Tests for Cross-Validation of Kriging Models Kriging模型交叉验证的统计检验

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-05-29 DOI: 10.2139/ssrn.3395872

J. Kleijnen, W. V. Beers

{"title":"Statistical Tests for Cross-Validation of Kriging Models","authors":"J. Kleijnen, W. V. Beers","doi":"10.2139/ssrn.3395872","DOIUrl":"https://doi.org/10.2139/ssrn.3395872","url":null,"abstract":"Kriging or Gaussian process models are popular metamodels (surrogate models or emulators) of simulation models; these metamodels give predictors for input combinations that are not simulated. To validate these metamodels for computationally expensive simulation models, the analysts often apply computationally efficient cross-validation. In this paper, we derive new statistical tests for so-called leave-one-out cross-validation. Graphically, we present these tests as scatterplots augmented with confidence intervals that use the estimated variances of the Kriging predictors. To estimate the true variances of these predictors, we might use bootstrapping. Like other statistical tests, our tests—with or without bootstrapping—have type I and type II error probabilities; to estimate these probabilities, we use Monte Carlo experiments. We also use such experiments to investigate statistical convergence. To illustrate the application of our tests, we use (i) an example with two inputs and (ii) the popular borehole example with eight inputs. Summary of Contribution: Simulation models are very popular in operations research (OR) and are also known as computer simulations or computer experiments. A popular topic is design and analysis of computer experiments. This paper focuses on Kriging methods and cross-validation methods applied to simulation models; these methods and models are often applied in OR. More specifically, the paper provides the following; (1) the basic variant of a new statistical test for leave-one–out cross-validation; (2) a bootstrap method for the estimation of the true variance of the Kriging predictor; and (3) Monte Carlo experiments for the evaluation of the consistency of the Kriging predictor, the convergence of the Studentized prediction error to the standard normal variable, and the convergence of the expected experimentwise type I error rate to the prespecified nominal value. The new statistical test is illustrated through examples, including the popular borehole model.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114253234","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: 11

Warnings About Future Jumps: Properties of the Exponential Hawkes Model 关于未来跳跃的警告:指数霍克斯模型的性质

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-05-28 DOI: 10.2139/ssrn.3639050

Rachele Foschi, Francesca Lilla, C. Mancini

引用次数: 4