ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)最新文献_第5页

Wind of Change: Small-Scale Electricity Production and Distribution Grid Efficiency in Sweden 变革之风:瑞典小规模电力生产和配电网效率

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-04-15 DOI: 10.2139/ssrn.3577200

Mattias Vesterberg, Wenchao Zhou, T. Lundgren

引用次数: 3

Structural Estimation of Decision Making under Natural Hazard Risk 自然灾害风险下决策的结构估计

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-04-13 DOI: 10.2139/ssrn.3574835

Dylan Turner, C. Landry

引用次数: 1

Adaptive Robust Control in Continuous-Time 连续时间自适应鲁棒控制

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-04-09 DOI: 10.2139/ssrn.3571991

Theerawat Bhudisaksang, Á. Cartea

引用次数: 3

On the Valuation and Analysis of Risky Debt: A Practical Approach Using Rating Migrations 风险债务的评估与分析:一种实用的评级迁移方法

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-04-06 DOI: 10.2139/ssrn.3569520

E. O. Fischer, Lisa-Maria Kampl, Ines Wöckl

引用次数: 0

Time-Varying Realized GARCH Models for Tracking Measurement Error Bias in Volatility Forecasting 波动率预测中测量误差偏差跟踪的时变GARCH实现模型

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-03-12 DOI: 10.2139/ssrn.3553270

R. Gerlach, Antonio Naimoli, G. Storti

引用次数: 0

A DCC-Type Approach for Realized Covariance Modelling With Score-Driven Dynamics 分数驱动动态协方差建模的dcc型实现方法

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-03-03 DOI: 10.2139/ssrn.3305628

Danilo Vassallo, G. Buccheri, Fulvio Corsi

引用次数: 0

Predicting Authoritarian Crackdowns: A Machine Learning Approach 预测专制镇压:一种机器学习方法

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-02-10 DOI: 10.2139/ssrn.3545999

Julian TszKin Chan, Weifeng Zhong

引用次数: 2

High Frequency Trading Strategy for the 30 Year Treasury Bond 30年期国债高频交易策略

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-02-10 DOI: 10.2139/ssrn.3535756

Peter Decrem, Sasha Stoikov, Shuo Shen, Jiaxin Yin, Yikai Hua, Tengxiao Li, Zhengyi Fang, Yunze Huang, Colin Basco

{"title":"High Frequency Trading Strategy for the 30 Year Treasury Bond","authors":"Peter Decrem, Sasha Stoikov, Shuo Shen, Jiaxin Yin, Yikai Hua, Tengxiao Li, Zhengyi Fang, Yunze Huang, Colin Basco","doi":"10.2139/ssrn.3535756","DOIUrl":"https://doi.org/10.2139/ssrn.3535756","url":null,"abstract":"We construct new features based on order book data and separate them into three groups, e.g., time-insensitive features, time-sensitive features and cointegration features. For time-insensitive features, we applied serval transformation on imbalance in different levels, and some other features based on order book data. For time-sensitive features, we constructed features with historic information. Besides, we extracted information about deleting and adding order book to construct features on it. For cointegration, we applied linear regression, online regression and Kalman filter to both the treasury data and the corresponding futures data to construct cash and futures cointegration features separately. Then, we predicted the fair-price for each quote given each single feature and combination of features. At last, we designed two smart algorithms to trade 30 Year Treasury Bond given the predicted fair-price. We found that combination features from different groups can help to reduce transaction cost by 95% compared with one tick-size. We believe that the new features we constructed can extract more information from order book, and can be very effective for trading strategies.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129922354","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 New Interval Type-2 Fuzzy Logic System Under Dynamic Environment: Application to Financial Investment 动态环境下一种新的区间2型模糊逻辑系统:在金融投资中的应用

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-02-04 DOI: 10.2139/ssrn.3532096

Akihiko Takahashi, Soichiro Takahashi

{"title":"A New Interval Type-2 Fuzzy Logic System Under Dynamic Environment: Application to Financial Investment","authors":"Akihiko Takahashi, Soichiro Takahashi","doi":"10.2139/ssrn.3532096","DOIUrl":"https://doi.org/10.2139/ssrn.3532096","url":null,"abstract":"Abstract This paper proposes a new interval type-2 fuzzy logic system (IT2 FLS) for financial investment with time-varying parameters adaptive to real-time data streams by using an on-line learning method based on a state-space framework. Particularly, our state-space approach regards the parameters of IT2 FLSs as state variables to sequentially learn by Bayesian filtering algorithms under dynamic environments, where time-series data are continuously observed with occasional structural changes. Moreover, our proposal is effective for financial investment, which often involves various practical complex constraints, because general state-space model makes it possible to flexibly deal with non-linearities. In our empirical experiment with time-series data of global financial assets, our approach is applied to on-line parameter learning of type-1 and type-2 FLSs for portfolio decision making. As a result, it is shown that the IT2 FLS holds its advantage against the type-1 FLS, even though both of the type-1 and type-2 models have the adaptive time-varying parameters, which is an unexplored topic for empirical studies of this area.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"80 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128132742","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: 15

Density Forecast Combinations: The Real-Time Dimension 密度预测组合:实时维度

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-02-01 DOI: 10.2139/ssrn.3544060

P. Mcadam, A. Warne

{"title":"Density Forecast Combinations: The Real-Time Dimension","authors":"P. Mcadam, A. Warne","doi":"10.2139/ssrn.3544060","DOIUrl":"https://doi.org/10.2139/ssrn.3544060","url":null,"abstract":"Density forecast combinations are examined in real-time using the log score to compare five methods: fixed weights, static and dynamic prediction pools, as well as Bayesian and dynamic model averaging. Since real-time data involves one vintage per time period and are subject to revisions, the chosen actuals for such comparisons typically differ from the information that can be used to compute model weights. The terms observation lag and information lag are introduced to clarify the different time shifts involved for these computations and we discuss how they influence the combination methods. We also introduce upper and lower bounds for the density forecasts, allowing us to benchmark the combination methods. The empirical study employs three DSGE models and two BVARs, where the former are variants of the Smets and Wouters model and the latter are benchmarks. The models are estimated on real-time euro area data and the forecasts cover 2001–2014, focusing on inflation and output growth. We find that some combinations are superior to the individual models for the joint and the output forecasts, mainly due to over-confident forecasts of the BVARs during the Great Recession. Combinations with limited weight variation over time and with positive weights on all models provide better forecasts than those with greater weight variation. For the inflation forecasts, the DSGE models are better overall than the BVARs and the combination methods. JEL Classification: C11, C32, C52, C53, E37","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122887272","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