ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)最新文献_第8页

A Self-Calibrated Direct Approach to Precision Matrix Estimation and Linear Discriminant Analysis in High Dimensions 高精度矩阵估计和高维线性判别分析的自校准直接方法

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-04-23 DOI: 10.2139/ssrn.3422590

Chi Seng Pun, Matthew Zakharia Hadimaja

引用次数: 8

The Impact of Data Aggregation and Risk Attributes on Stress Testing Models of Mortgage Default 数据聚合和风险属性对抵押贷款违约压力测试模型的影响

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-04-15 DOI: 10.2139/ssrn.3690970

F. Li, Yan Zhang

引用次数: 0

Network-Based Financial Forecasting: A Statistical and Economic Analysis 基于网络的财务预测:一个统计和经济分析

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-04-11 DOI: 10.2139/ssrn.3370098

Eduard Baitinger

引用次数: 0

Markov Chain and Its Applications 马尔可夫链及其应用

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-03-27 DOI: 10.2139/ssrn.3562746

Xinye Yang

引用次数: 4

Knitting Multi-Annual High-Frequency Google Trends to Predict Inflation and Consumption 编织多年高频谷歌趋势预测通货膨胀和消费

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-03-21 DOI: 10.2139/ssrn.3357424

J. Bleher, T. Dimpfl

引用次数: 4

Sparse Macro Factors 稀疏宏观因素

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2019-01-31 DOI: 10.2139/ssrn.3259447

D. Rapach, Guofu Zhou

{"title":"Sparse Macro Factors","authors":"D. Rapach, Guofu Zhou","doi":"10.2139/ssrn.3259447","DOIUrl":"https://doi.org/10.2139/ssrn.3259447","url":null,"abstract":"We use machine learning to estimate sparse principal components (PCs) for 120 monthly macro variables spanning 1960:02 to 2018:06 from the FRED-MD database. For comparison, we also extract the ﬁrst ten conventional PCs from the macro variables. Each of the conventional PCs is a linear combination of all the underlying macro variables, making them difficult to interpret. In contrast, each of the sparse PCs is a sparse linear combination, whose active weights allow for intuitive economic interpretations of the sparse PCs. The ﬁrst ten sparse PCs can be interpreted as yields, inﬂation, production, housing, employment, yield spreads, wages, optimism, money, and credit. Innovations to the conventional (sparse) PCs constitute a set of conventional (sparse) macro factors. Robust tests indicate that only one of the conventional macro factors earns a signﬁcant risk premium. In contrast, three of sparse macro factors — corresponding to yields, housing, and optimism — earn signﬁcant risk premia. Compared to leading risk factors from the literature, mimicking portfolios for the yields, housing, and optimism factors deliver sizable Sharpe ratios. A four-factor model comprised of the market factor and mimicking portfolio returns for the yields, housing, and optimism factors performs on par with or better than leading multi-factor models from the literature in accounting for numerous anomalies in cross-sectional stock returns.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130203720","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: 9

An Investigation of Machine Learning Approaches in the Solvency II Valuation Framework 偿付能力II评估框架中机器学习方法的研究

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2018-12-14 DOI: 10.2139/ssrn.3303296

G. Castellani, Ugo Fiore, Z. Marino, L. Passalacqua, F. Perla, Salvatore Scognamiglio, P. Zanetti

{"title":"An Investigation of Machine Learning Approaches in the Solvency II Valuation Framework","authors":"G. Castellani, Ugo Fiore, Z. Marino, L. Passalacqua, F. Perla, Salvatore Scognamiglio, P. Zanetti","doi":"10.2139/ssrn.3303296","DOIUrl":"https://doi.org/10.2139/ssrn.3303296","url":null,"abstract":"The insurance regulatory regime introduced in the European Union by the \"Solvency II\" Directive 2009/138, that has become applicable on January 1, 2016, is aimed to safeguard policyholders and beneficiaries by requiring insurance undertakings to hold own funds able to cover losses, in excess to the expected ones, at the 99.5% confidence level, over a one-year period. In order to assess risks and evaluate the regulatory Solvency Capital Requirement undertakings should compute the probability distribution of the Net Asset Value - i.e., value of assets minus value of liabilities - over a one-year period, with a financially inspired market consistent approach. In life insurance, given the peculiarities of the contracts, the valuation of the Net Asset Value distribution requires a nested Monte Carlo simulation, which is extremely time consuming. \u0000 \u0000Machine learning techniques are considered a promising candidate to reduce the computational burden of nested simulations. This work investigates the potential of well-established methods, such as Deep Learning Networks and Support Vector Regressors, when applied to the valuation of the Solvency Capital Requirement of participating life insurance polices, by empirically assessing their effectiveness and by comparing their efficiency and accuracy, also w.r.t. the \"traditional\" Least Squares Monte Carlo technique. \u0000 \u0000The work aims also to contribute to the global process of renewal of the European insurance industry, where Solvency II has made the board of directors fully responsible for the choice of evaluation techniques and algorithmic processes, under the periodic monitoring of national supervisory authorities.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132214518","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

Trend Following with Momentum Versus Moving Average: A Tale of Differences 动量趋势跟踪vs .移动平均线:差异的故事

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2018-11-29 DOI: 10.2139/ssrn.3293521

Valeriy Zakamulin, Javier Giner

{"title":"Trend Following with Momentum Versus Moving Average: A Tale of Differences","authors":"Valeriy Zakamulin, Javier Giner","doi":"10.2139/ssrn.3293521","DOIUrl":"https://doi.org/10.2139/ssrn.3293521","url":null,"abstract":"Despite the ever-growing interest in trend following and a series of publications in academic journals, there is still a great shortage of theoretical results on the properties of trend following rules. Our paper fills this gap by comparing and contrasting the two most popular trend following rules, the Momentum (MOM) and Moving Average (MA) rules, from a theoretical perspective. Our approach is based on the return-based formulation of trading rules and modelling the price trends by an autoregressive return process. We provide theoretical results on the similarity between various trend following rules and the forecast accuracy of trading rules. Our results show that the similarity between the MOM and MA rules is rather high and increases with increasing trend strength. However, as compared to the MOM rule, the MA rules have a more robust forecast accuracy of the future direction of price trends. As a result, under uncertain market dynamics the MA rules tend to gain an advantage over the MOM rule. Overall, the results reported in this paper help traders to understand more deeply the properties of trend following rules as well as the differences and similarities between them.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121556690","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: 2

An Integrated Two-Level Demand-Side Management Game Applied to Smart Energy Hubs with Storage 应用于智能储能枢纽的集成两级需求侧管理博弈

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2018-11-01 DOI: 10.2139/ssrn.3549759

S. Sobhani, Siamak Sheykhha, R. Madlener

{"title":"An Integrated Two-Level Demand-Side Management Game Applied to Smart Energy Hubs with Storage","authors":"S. Sobhani, Siamak Sheykhha, R. Madlener","doi":"10.2139/ssrn.3549759","DOIUrl":"https://doi.org/10.2139/ssrn.3549759","url":null,"abstract":"The integration of energy hubs – as an important component of future energy networks that will employ demand-side management techniques – has a key role in the process of efficiency improvement and reliability enhancement of power grids. In such power grids, energy hub operators need to optimally schedule the consumption, conversion, and storage of available resources based on their own utility functions. In sufficiently large networks, scheduling an individual hub can affect the utility of the other energy hubs. In this paper, the interaction between energy hubs is modeled as a potential game. Each energy hub operator (player) participates in a dynamic energy pricing market and tries to maximize his own payoff with regard to energy consumption satisfaction. We propose a distributed algorithm based on a potential game, which guarantees the existence of a Nash equilibrium. Furthermore, two different types of signaling are developed and simulation results are compared. Simulation results show that with the implementation of either setup the peak-to-average ratio between electricity networks and natural gas networks diminishes. An analysis of the results shows that either setup can have superiority over the other one with regard to generation costs, convergence rate, price level, and the stability perspective. Hence, energy providers and consumers can choose a favorable setup based on their respective needs.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131706799","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: 47

A Closed-Formula Characterization of the Epps Effect Epps效应的一个封闭公式表征

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2018-10-22 DOI: 10.2139/ssrn.3255070

G. Buccheri, G. Livieri, Davide Pirino, A. Pollastri

引用次数: 4