Quality & Quantity最新文献_第2页

Is Abrams curve a myth or reality? Evidence from two Baltic countries 艾布拉姆斯曲线是神话还是现实?来自两个波罗的海国家的证据

Quality & Quantity Pub Date : 2023-11-07 DOI: 10.1007/s11135-023-01778-6

İbrahim Özmen, Selçuk Bali, Festus Victor Bekun

引用次数: 0

Economic policy uncertainty, corporate diversification and firm value: the global evidence 经济政策不确定性、企业多元化与企业价值:全球证据

Quality & Quantity Pub Date : 2023-11-06 DOI: 10.1007/s11135-023-01768-8

Zahid Jumah, Nabeel Safdar, Zahid Irshad Younas, Adeel Ahmed

引用次数: 0

Do social media use and gadget loving affect innovative job performance? The moderation role of generation cohort: an evaluation of the kitchen chefs 社交媒体的使用和对电子产品的热爱会影响创新工作绩效吗?世代世代的调节作用:对厨房厨师的评价

Quality & Quantity Pub Date : 2023-11-02 DOI: 10.1007/s11135-023-01773-x

Faruk Yüksel, Üzeyir Kement, Samet Can Aksu, Mehmet Kabacik, Raffaela Ciuffreda

引用次数: 0

Reinforcement learning for sequential decision making in population research 人口研究中序列决策的强化学习

Quality & Quantity Pub Date : 2023-11-02 DOI: 10.1007/s11135-023-01755-z

Nina Deliu

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引用次数: 0

Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions? 预测加密货币回报:宏观经济和金融变量是否会改善尾部预期预测?

Quality & Quantity Pub Date : 2023-11-02 DOI: 10.1007/s11135-023-01761-1

Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito

{"title":"Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?","authors":"Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito","doi":"10.1007/s11135-023-01761-1","DOIUrl":"https://doi.org/10.1007/s11135-023-01761-1","url":null,"abstract":"Abstract This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.","PeriodicalId":49649,"journal":{"name":"Quality & Quantity","volume":"16 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934625","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

Modelling labour productivity and the role of research intensity in 129 years: evidence from a new dynamic instrumental variable estimation approach 劳动生产率模型和129年来研究强度的作用:来自一种新的动态工具变量估计方法的证据

Quality & Quantity Pub Date : 2023-10-31 DOI: 10.1007/s11135-023-01766-w

Sakiru Adebola Solarin, Mufutau Opeyemi Bello

引用次数: 0

Correction: Mapping fear of crime: defining methodological orientations 更正:描绘对犯罪的恐惧:定义方法论取向

Quality & Quantity Pub Date : 2023-10-28 DOI: 10.1007/s11135-023-01769-7

Julien Noble, Antoine Jardin

引用次数: 0

Disentangling work–family conflict, support, and turnover intentions: quanti–quali mixed method approach 解开工作-家庭冲突、支持与离职意向:定量-质量混合方法

Quality & Quantity Pub Date : 2023-10-26 DOI: 10.1007/s11135-023-01759-9

Rakesh Belwal, Shweta Belwal, Suhaila Ebrahim AlHashemi