ERN: Other Econometrics: Mathematical Methods & Programming (Topic)最新文献_第5页

Loss Data Analysis with Maximum Entropy 最大熵损失数据分析

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2018-04-07 DOI: 10.1007/978-3-319-89824-7_70

Erika Gomes-Gonçalves, H. Gzyl, Silvia Mayoral Blaya

引用次数: 0

Banded Spatio-Temporal Autoregressions 带状时空自回归

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2018-03-05 DOI: 10.2139/ssrn.3164924

Zhaoxing Gao, Yingying Ma, Hansheng Wang, Qiwei Yao

引用次数: 24

Optimal Posting of Collateral with Recurrent Neural Networks 递归神经网络优化投寄抵押品

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2018-02-16 DOI: 10.2139/ssrn.3140327

P. Henry-Labordère

引用次数: 0

Empirical Evidence on the Geometrical Properties of Structural Change Trajectories 结构变化轨迹几何性质的经验证据

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-08-16 DOI: 10.2139/ssrn.3020186

Denis Stijepic

引用次数: 4

Recursive Utiity and Thompson Aggregators 递归实用程序和汤普森聚合器

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-07-22 DOI: 10.2139/ssrn.3007788

R. Becker, J. P. Rincón-Zapatero

引用次数: 13

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming 让数据说话:用遗传规划方法对基于调查的期望进行实证建模

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-07-05 DOI: 10.2139/ssrn.2972667

Oscar Claveria, E. Monte, Salvador Torra

{"title":"Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming","authors":"Oscar Claveria, E. Monte, Salvador Torra","doi":"10.2139/ssrn.2972667","DOIUrl":"https://doi.org/10.2139/ssrn.2972667","url":null,"abstract":"In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive the optimal combination of expectations that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ “perception about the overall economy compared to last year” is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.","PeriodicalId":365755,"journal":{"name":"ERN: Other Econometrics: Mathematical Methods & Programming (Topic)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115009941","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

A Shortest-Path-Based Approach for the Stochastic Knapsack Problem with Non-Decreasing Expected Overfilling Costs

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-07-04 DOI: 10.2139/ssrn.2997916

T. Range, Dawid Kozlowski, N. Petersen

{"title":"A Shortest-Path-Based Approach for the Stochastic Knapsack Problem with Non-Decreasing Expected Overfilling Costs","authors":"T. Range, Dawid Kozlowski, N. Petersen","doi":"10.2139/ssrn.2997916","DOIUrl":"https://doi.org/10.2139/ssrn.2997916","url":null,"abstract":"Abstract The knapsack problem (KP) is concerned with the selection of a subset of multiple items with known positive values and weights such that the total value of selected items is maximized and their total weight does not exceed capacity. Item values, item weights, and capacity are known in the deterministic case. We consider the stochastic KP (SKP) with stochastic item weights. For this variant of the SKP we combine the chance constrained KP (CCKP) and the SKP with simple recourse (SRKP). The chance constraint allows for a violation of capacity, but the probability of a violation beyond an imposed limit is constrained. The violation of the capacity constraint is also included in the objective function in terms of a penalty function as in the SRKP. Penalty is an increasing function of the expected number of units of violation with proportionality as a special case. We formulate the SKP as a network problem and demonstrate that it can be solved by a label-setting dynamic programming approach for the shortest path problem with resource constraints (SPPRC). We develop a dominance criterion for an elimination of states in the dynamic programming approach using only the deterministic value of items along with mean and variance of the stochastic weight of items corresponding to the associated paths in the underlying network. It is shown that a lower bound for the impact of potential extensions of paths is available as an additional means to limit the number of states provided the penalty cost of expected overtime is convex. Our findings are documented in terms of a computational study.","PeriodicalId":365755,"journal":{"name":"ERN: Other Econometrics: Mathematical Methods & Programming (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127775024","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: 5

Self-Adaptive Evolutionary Computation Methods for GARCH Model Optimization GARCH模型优化的自适应进化计算方法

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-05-28 DOI: 10.2139/ssrn.2975943

A. Swain

引用次数: 0

Supplementary Appendix to: A Coskewness Shrinkage Approach for Estimating the Skewness of Linear Combinations of Random Variables 补充附录:估计随机变量线性组合偏度的高斯偏度收缩法

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-05-17 DOI: 10.2139/ssrn.2970015

Kris Boudt, D. Cornilly, Tim Verdonck

{"title":"Supplementary Appendix to: A Coskewness Shrinkage Approach for Estimating the Skewness of Linear Combinations of Random Variables","authors":"Kris Boudt, D. Cornilly, Tim Verdonck","doi":"10.2139/ssrn.2970015","DOIUrl":"https://doi.org/10.2139/ssrn.2970015","url":null,"abstract":"Full Paper is available at: <a href='https://ssrn.com/abstract=2839781'>https://ssrn.com/abstract=2839781</a> In the supplementary appendix to the paper Boudt, Cornilly, and Verdonck (2018) we discuss the impact of autocorrelation and a time-varying structure on the estima- tion of coskewness matrices and provide more explanation about structured estima- tion. We go into more detail about our approach of optimizing the targets and provide several examples of other coskewness matrices in the literature. In particular, we pro- vide guidance how to use the latent single-factor coskewness matrix of Simaan (1993) in a shrinkage setting and correct the estimators provided in Martellini and Ziemann (2010) to estimate C(Φ ,T) for their observed single-factor and constant correlation coskewness estimators. The simulation setting discussed in the main paper is explored into more detail and we study three additional data generating processes. Further- more, we provide details on the empirical application studied in the main paper and extend the empirical study of Martellini and Ziemann (2010). Finally, we introduce the R code, publicly available in the PerformanceAnalytics package of Peterson and Carl (2018), for all single- and multi-target shrinkage estimators.","PeriodicalId":365755,"journal":{"name":"ERN: Other Econometrics: Mathematical Methods & Programming (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112247","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

Optimal Trading Strategies within the Hamilton-Jacobi-Bellman Equation - An Application to Statistical Arbitrage Hamilton-Jacobi-Bellman方程中的最优交易策略——在统计套利中的应用

ERN: Other Econometrics: Mathematical Methods & Programming (Topic) Pub Date : 2017-05-09 DOI: 10.2139/ssrn.2964190

Atsunari Konishi

{"title":"Optimal Trading Strategies within the Hamilton-Jacobi-Bellman Equation - An Application to Statistical Arbitrage","authors":"Atsunari Konishi","doi":"10.2139/ssrn.2964190","DOIUrl":"https://doi.org/10.2139/ssrn.2964190","url":null,"abstract":"We propose optimal trading strategies based on the Hamilton-Jacobi-Bellman equation when a trader is allowed to place only market orders with a particular focus on a statistical arbitrage. A process that a placed limit order is filled is often modeled as a stochastic point process. In case of a market order, however, it is filled with a hundred percent probability meaning that it is not a stochastic process and is rather a deterministic one. They are therefore represented using the Heaviside step function and their infinitesimal generators are just time derivative and give delta functions. An integral linear utility function with an inventory penalty and a discount enables us to obtain optimal number of market order with a simple calculus and those in a limit T → ∞, i.e. a stationary limit. We show that, at least within our formulation, we cannot generate a positive PnL by trading multiple set of assets whose noises are correlated but their drifts are zero which contradicts the common statement that \"Given correlated multiple assets, we can exploit those correlations by trading an appropriate linear combination of them\". However, we show that if multiple assets are cointegrated, we can create a mean-reverting portfolio and exploit them. In that case, an optimal number of market order which takes current status of market as well as a trader's preference into account is given in a simple but intuitive and reasonable form and it does generate positive PnL while controlling risk of the portfolio.","PeriodicalId":365755,"journal":{"name":"ERN: Other Econometrics: Mathematical Methods & Programming (Topic)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123527443","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