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

{"title":"Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models","authors":"C. Carvalho, Jared D. Fisher, Davide Pettenuzzo","doi":"10.2139/ssrn.3254935","DOIUrl":"https://doi.org/10.2139/ssrn.3254935","url":null,"abstract":"We introduce a fast, closed-form, simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian dynamic linear models of West and Harrison (Bayesian Forecasting and Dynamic Models (1997) Springer), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126571753","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":"Cryptoasset Factor Models","authors":"Zurab Kakushadze","doi":"10.2139/ssrn.3245641","DOIUrl":"https://doi.org/10.2139/ssrn.3245641","url":null,"abstract":"We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In \"cryptoassets\" we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market data is available. Based on our empirical analysis, we identify the leading factor that appears to strongly contribute into daily cryptoasset returns. Our results suggest that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121505858","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":"Assortment Optimization for a Multi-Stage Choice Model","authors":"Yunzong Xu, Zizhuo Wang","doi":"10.2139/ssrn.3243742","DOIUrl":"https://doi.org/10.2139/ssrn.3243742","url":null,"abstract":"Motivated by several practical selling scenarios that require previous purchases to unlock future options, we consider a multi-stage assortment optimization problem, where the seller makes sequential assortment decisions with commitment, and the customer makes sequential choices to maximize her expected utility. \u0000 \u0000We study the optimal solution to the problem when there are two stages. We show that this problem is polynomial-time solvable when the customer is fully myopic or fully forward-looking. In particular, when the customer is fully forward-looking, the optimal policy entails that the assortment in each stage is revenue-ordered and a product with higher revenue always leads to a wider range of future options. Moreover, we find that the optimal assortment in the first stage must be smaller than the optimal assortment when there were no second stage and the optimal assortment in the second stage must be larger than the optimal assortment when there were no first stage. When the customer is partially forward-looking, we show that the problem is NP-hard in general. In this case, we present efficient algorithms to solve this problem under various scenarios. \u0000 \u0000We further extend the above results to the multi-stage problem with an arbitrary number of stages, for which we derive generalized structural properties and efficient algorithms. We also study the performance of a class of static policies and discuss the estimation problem of the multi-stage choice model.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"6 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130364099","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":"Nowcasting New Zealand GDP Using Machine Learning Algorithms","authors":"Adam Richardson, T. Mulder, Tugru l Vehbi","doi":"10.2139/ssrn.3256578","DOIUrl":"https://doi.org/10.2139/ssrn.3256578","url":null,"abstract":"This paper analyses the real-time nowcasting performance of machine learning algorithms estimated on New Zealand data. Using a large set of real-time quarterly macroeconomic indicators, we train a range of popular machine learning algorithms and nowcast real GDP growth for each quarter over the 2009Q1-2018Q1 period. We compare the predictive accuracy of these nowcasts with that of other traditional univariate and multivariate statistical models. We find that the machine learning algorithms outperform the traditional statistical models. Moreover, combining the individual machine learning nowcasts further improves the performance than in the case of the individual nowcasts alone.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116457140","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":"Endogenous Inputs and Environmental Variables in Battese and Coelli's (1995) Stochastic Frontier Model","authors":"T. Tsukamoto","doi":"10.2139/ssrn.3231804","DOIUrl":"https://doi.org/10.2139/ssrn.3231804","url":null,"abstract":"In this study, we provide a guideline for dealing with endogenous inputs and environmental variables in Battese and Coelli’s (1995) stochastic frontier model. Our Monte Carlo simulations show that our estimators perform well.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115491688","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 General Framework for Time-Changed Markov Processes and Applications","authors":"Zhenyu Cui, J. Kirkby, D. Nguyen","doi":"10.2139/ssrn.3235766","DOIUrl":"https://doi.org/10.2139/ssrn.3235766","url":null,"abstract":"Abstract In this paper, we propose a general approximation framework for the valuation of (path-dependent) options under time-changed Markov processes. The underlying background process is assumed to be a general Markov process, and we consider the case when the stochastic time change is constructed from either discrete or continuous additive functionals of another independent Markov process. We first approximate the underlying Markov process by a continuous time Markov chain (CTMC), and derive the functional equation characterizing the double transforms of the transition matrix of the resulting time-changed CTMC. Then we develop a two-layer approximation scheme by further approximating the driving process in constructing the time change using an independent CTMC. We obtain a single Laplace transform expression. Our framework incorporates existing time-changed Markov models in the literature as special cases, such as the time-changed diffusion process and the time-changed Levy process. Numerical experiments illustrate the accuracy of our method.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126237653","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":"Flexible Modeling of Multivariate Risks in Pricing Margin Protection Insurance: Modeling Portfolio Risks with Mixtures of Mixtures","authors":"Seyyed Ali Zeytoon Nejad Moosavian","doi":"10.2139/ssrn.3219598","DOIUrl":"https://doi.org/10.2139/ssrn.3219598","url":null,"abstract":"Margin Protection Programs (MPPs) are relatively new insurance plans that have been introduced and made available by the USDA’s Risk Management Agency (RMA). These programs were initially implemented for livestock and dairy producers, and were subsequently extended to cover other agricultural products such as corn, rice, soybeans, and wheat. The attractiveness of these risk management instruments lies in the fact that the financial stability of agricultural production and farming operations is more dependent on margins than solely revenues. This paper examines the structure and rating of margin protection insurance policies. In particular, the paper considers a broad class of high-dimensional copula models that parameterize the dependence among multivariate sources of risks. To efficiently and accurately determine actuarially fair policy premiums, it is necessary to first model the joint distribution function of input and output prices. This task can be effectively carried out using copula methods. A variety of copula methods, including Archimedean Copulas (ACs), Mixture Copulas (MCs), and Vine Copulas (VCs) are used to analyze the dependence structure between revenues and input costs. In terms of methodology, flexible mixtures of parametric distributions are applied to characterize marginal densities, and likewise flexible mixtures of alternative copulas are used to model dependence. This paper also argues that the rating methodology that accounts for irregular and anomalous features of dependence such as asymmetry, non-linearity, non-ellipticity, and tail dependence between input prices and output prices can result in more accurate premiums, and therefore can increase the hedging effectiveness of the MPPs. Goodness-of-fit tests generally reject conventional approaches based upon log-normally distributed marginals and Gaussian copulas. In this paper, several reasons are identified to explain why the common methods being currently employed to determine policy premiums might not be adequate, realistic, or sufficiently flexible to take into account the multivariate aspects of risks involved in farming operations. To this end, the present paper investigates the underlying assumptions based on which the MPP policy premiums are determined. It is argued that assumptions made in pricing risks may induce important distortions in the production and marketing decisions of producers. It is also noted that precise measurement of the marginal densities for individual random variables is essential for accurately pricing a portfolio of multivariate risks. Finally, implications for the ever-expanding offerings of publicly-subsidized agricultural insurance mechanisms are offered.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398347","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":"Backward Stochastic Differential Equations under Enlarged Filtrations","authors":"M. Hess","doi":"10.2139/ssrn.3162122","DOIUrl":"https://doi.org/10.2139/ssrn.3162122","url":null,"abstract":"We provide solution formulas for (linear and non-linear) jump-diffusion backward stochastic differential equations (BSDEs) under diverse enlarged filtration approaches. We also derive a comparison theorem for BSDEs in an enlarged filtration framework and present several applications of our theoretical results. For example, we study temperature derivatives under weather forecasts modeled by an enlarged filtration BSDE, investigate portfolio selection and hedging for an insider with additional information on future price behavior and solve a particular predictive mean-field BSDE in an enlarged filtration setup.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121212531","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 the Stylized Fact of the Firm Size Distribution","authors":"L. Artige, Alexandre Reginster","doi":"10.2139/ssrn.3219439","DOIUrl":"https://doi.org/10.2139/ssrn.3219439","url":null,"abstract":"Due to truncated datasets and flawed statistical methods, the firm size distribution (FSD) does not yet have its undisputed stylized fact. Using comprehensive data on Belgian private firms, this paper proposes to estimate and test a new parametric distribution, called Generalized Pareto with cutoff (GPwC), and compare it to the commonly used Pareto and lognormal distributions. Our statistical test and graphical representations show that the GPwC is the best approximation of the entire Belgian FSD. This suggests that such a distribution is generated by a stochastic model that varies with firm size.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133698863","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":"Time-Varying Volatility Persistence in a GARCH-MIDAS Framework","authors":"K. Stürmer","doi":"10.2139/ssrn.3258136","DOIUrl":"https://doi.org/10.2139/ssrn.3258136","url":null,"abstract":"This paper presents a new volatility model with time-varying volatility persistence (TVP) that is governed by the dynamics of an explanatory variable. We extend the GJR-GARCH model by introducing a time-varying GARCH coefficient that is linked to the variable in a parsimonious way using MIDAS techniques. We refer to the model as the TVP-GARCH-MIDAS model. It nests the GJR-GARCH under the null that the variable has no explanatory power. We present a misspecification test based on the Lagrange multiplier principle and study its finite sample properties in a Monte-Carlo simulation. In an empirical application to the U.S. stock market, we show that volatility persistence is positively related to realized volatility and that it varies across the business cycle in a counter cyclical way. Finally, we assess forecasting gains of the new model in a direct forecasting comparison.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329939","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}