检索结果-内蒙古大学图书馆

Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model

JOURNAL OF FORECASTING 2022年第3期41卷 407-421页

作者： Xu, Qifa Chen, Lu Jiang, Cuixia Liu, Yezheng Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Peoples R China

Low-frequency risk measures can filter out noise and better reflect the trend. In order to improve the forecasting accuracy of low-frequency risk through making full use of the valuable information contained in high-frequency independent variables, we propose a novel joint elicitable mixed data sampling (JE-MIDAS) model by introducing MIDAS method into JE regression model. We utilize the JE-MIDAS model to forecast value at risk and expected shortfall simultaneously and compare its performance with that of other popular models through both the Monte Carlo simulations and real-world applications. The numerical results show that our model is superior to other models because it can model mixed-frequency data directly, which avoids the information loss caused by frequency conversion. The empirical results on three stock indices also show that market volatility can increase financial risk. Interest rate has positive effects on risks for U.S. S&P500 and U.K. FTSE, whereas negative for SHCI of China.

关键词： expected shortfall financial risk forecasting joint elicitable mixed data sampling mixed-frequency data

来源：评论

学校读者我要写书评

暂无评论

A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets

引用

ECONOMIC MODELLING 2018年 68卷 586-598页

作者： Gong, Yuting Chen, Qiang Liang, Jufang Shanghai Univ SHU UTS SILC Business Sch Dept Econ Room 511Wenhui Bldg20 Chengzhong Rd Shanghai 201800 Peoples R China Shanghai Univ Finance & Econ Sch Econ Shanghai Peoples R China Hunan Univ Sch Finance & Stat Changsha Hunan Peoples R China

Understanding and quantifying the dependence of returns and liquidity is critical for liquidity risk management. In this paper the idea of mixed data sampling (MIDAS) is extended from linear correlation in Colacito et al. (2011) to the more general dependence measure: copula, and a copula-MIDAS model is proposed to describe the asymmetric return-liquidity dependence of CSI 300 index futures with short-run and long-run components. Based on the skewed t copula-MIDAS model, it is found that extreme decreases in returns tend to be accompanied by extreme increases in bid-ask spreads, but extreme increases in returns may not coincide with extreme reductions in bid-ask spreads. Furthermore, the return-spread dependence consists of both short-run and long-run components, and the long-run component will influence the return-spread dependence in the next two weeks. Last, the out-of-sample forecast of liquidity risk stresses the importance of considering asymmetry and long-run trend in return-spread dependence as it enables investors to well predict liquidity risk in times of market crashes. The results imply that high frequency trading investors of CSI 300 index futures should pay more attentions to prevent the potential liquidity risk when the bid-ask spreads are widened. And investors are suggested to use the past two-week high frequency data to forecast the current return-spread dependence in liquidity risk management.

关键词： mixed data sampling Copula CSI 300 index futures Liquidity Return

来源：评论

学校读者我要写书评

暂无评论

Exogenous drivers of Bitcoin and Cryptocurrency volatility - A mixed data sampling approach to forecasting

引用

JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY 2019年 63卷 101133-000页

作者： Walther, Thomas Klein, Tony Bouri, Elie Univ Utrecht Utrecht Sch Econ Kriekenpitpl 21-22 NL-3584 EC Utrecht Netherlands Univ St Gallen Inst Operat Res & Computat Finance St Gallen Switzerland Tech Univ Dresden Fac Business & Econ Dresden Germany Queens Univ Belfast Queens Management Sch Belfast Antrim North Ireland Holy Spirit Univ Kaslik USEK Business Sch Jounieh Lebanon

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.

关键词： Bitcoin Cryptocurrencies GARCH mixed data sampling Volatility

来源：评论

学校读者我要写书评

暂无评论

Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials

引用

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2015年第1期178卷 57-82页

作者： Foroni, Claudia Marcellino, Massimiliano Schumacher, Christian Norges Bank Oslo Norway Bocconi Univ I-20136 Milan Italy Ctr Econ Policy Res London SW1Y 6LA England Deutsch Bundesbank Frankfurt Germany

mixed data sampling (MIDAS) regressions allow us to estimate dynamic equations that explain a low frequency variable by high frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically employed to model dynamics avoiding parameter proliferation. In macroeconomic applications, however, differences in sampling frequencies are often small. In such a case, it might not be necessary to employ distributed lag functions. We discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted-MIDAS (U-MIDAS) regressions from linear high frequency models, discuss identification issues and show that their parameters can be estimated by ordinary least squares. In Monte Carlo experiments, we compare U-MIDAS with MIDAS with functional distributed lags estimated by non-linear least squares. We show that U-MIDAS performs better than MIDAS for small differences in sampling frequencies. However, with large differing sampling frequencies, distributed lag functions outperform unrestricted polynomials. The good performance of U-MIDAS for small differences in frequency is confirmed in empirical applications on nowcasting and short-term forecasting euro area and US gross domestic product growth by using monthly indicators.

关键词： Distributed lag polynomals mixed data sampling Nowcasting Time aggregation

来源：评论

学校读者我要写书评

暂无评论

A novel mixed frequency sampling discrete grey model for forecasting hard disk drive failure

引用

ISA TRANSACTIONS 2024年 147卷 304-327页

作者： Chen, Rongxing Xiao, Xinping Gao, Mingyun Ding, Qi Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Cent China Normal Univ Sch Informat Management Wuhan 430079 Peoples R China Nanjing Univ Sch Business Nanjing 210008 Peoples R China

The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure o

关键词： mixed frequency sampling grey model mixed data sampling Chimp optimization algorithm Hard disk drive failure forecasting

来源：评论

学校读者我要写书评

暂无评论

Real-time nowcasting the monthly unemployment rates with daily Google Trends data

引用

SOCIO-ECONOMIC PLANNING SCIENCES 2024年 95卷

作者： Costa, Eduardo Andre Silva, Maria Eduarda Galvao, Ana Beatriz Univ Porto Sch Econ & Management Rua Dr Roberto Frias S-N P-4200464 Porto Portugal Univ Porto INESC TEC LIAAD Campus Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Warwick Dept Econ Gibbet Hill Rd Coventry CV4 7AL England

Policymakers often have to make decisions based on incomplete economic data because of the usual delay in publishing official statistics. To circumvent this issue, researchers use data from Google Trends (GT) as an early indicator of economic performance. Such data have emerged in the literature as alternative and complementary predictors of macroeconomic outcomes, such as the unemployment rate, featuring readiness, public availability and no costs. This study deals with extensive daily GT data to develop a framework to nowcast monthly unemployment rates tailored to work with real-time data availability, resorting to mixed data sampling (MIDAS) regressions. Portugal is chosen as a use case for the methodology since extracting GT data requires the selection of culturally dependent keywords. The nowcasting period spans 2019 to 2021, encompassing the time frame in which the coronavirus pandemic initiated. The findings indicate that using daily GT data with MIDAS provides timely and accurate insights into the unemployment rate, especially during the COVID-19 pandemic, showing accuracy gains even when compared to nowcasts obtained from typical monthly GT data via traditional ARMAX models.

关键词： Google Trends Predictors Nowcasting Unemployment rate mixed data sampling Portugal

来源：评论

学校读者我要写书评

暂无评论

Deep learning on mixed frequency data

引用

JOURNAL OF FORECASTING 2023年第8期42卷 2099-2120页

作者： Xu, Qifa Wang, Zezhou Jiang, Cuixia Liu, Yezheng Hefei Univ Technol Sch Management Hefei Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Peoples R China Hefei Univ Technol Sch Management Tunxi Rd 193 Hefei 230009 Anhui Peoples R China

In deep learning, it is common to encounter data observed at different frequencies. mixed data sampling (MIDAS) is an efficient technique for handling mixed frequency data, where a high frequency predictor is converted into a set of low frequency variables using the frequency alignment approach and parametric function constraints. This efficiently prevents the proliferation of parameters, ensuring the consistency of data frequency. We introduce the MIDAS technique into the deep learning architecture and develop a novel deep learning-MIDAS (DL-MIDAS) model, which enables to conduct deep learning on raw mixed frequency data directly. Its efficacy is then illustrated through extensive Monte Carlo simulations and a real-world application. The simulation experiments show that the DL-MIDAS model is able to explore nonlinear patterns in mixed frequency data and achieves more stable and accurate prediction results than several competing models, such as the artificial neural network for mixed frequency data (ANN-MIDAS), long short-term memory (LSTM), and MIDAS regressions. Additionally, the real-world application of predicting the inflation rate of China also confirms the strength of DL-MIDAS. The model can exploit high frequency information contained in financial market to produce timely and accurate prediction results on the inflation rate.

关键词： deep learning DL-MIDAS inflation rate forecasting mixed data sampling nonlinear pattern timely prediction

来源：评论

学校读者我要写书评

暂无评论

The effectiveness of fiscal policy in Brazil through the MIDAS Lens

引用

JOURNAL OF POLICY MODELING 2024年第1期46卷 113-128页

作者： Alves, Renan Santos Palma, Andreza A. Rabobank Macro Strategist Sao Paulo Brazil Fed Univ Sao Carlos UFSCar Dept Econ Sao Carlos Brazil

This paper aims to examine the effects of fiscal policy on economic growth in Brazil between 1999 and 2017. For this purpose, a novel methodology is applied, using a Vector Autoregressive with mixed Frequency (MIDAS-VAR) model, proposed by Ghysels (2016), which allows for the estimation of the spending multiplier by measuring the impact of high-frequency data at low-frequency and vice versa. The impact of various types of spending on the gross domestic product (GDP) is analyzed, including primary expenditure, personnel, social benefits, subsidies, investment, and costing, while the central government's primary revenue is used as the revenue variable. The expenditure and tax revenue are the high-frequency variables (monthly observations), whereas GDP is a low-frequency series (quarterly). The estimated fiscal multipliers for primary spending are less than one, suggesting no significant Keynesian effect on output, with particular attention given to the investment multiplier, which is estimated to be close to zero. Our results suggest that the frequency of data matters, and government expenditure has no significant impact on real GDP growth in Brazil. Therefore, the ability of Brazilian fiscal policy to influence economic growth may be limited. (c) 2023 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

关键词： Fiscal multiplier mixed frequency vector autoregression mixed data sampling Fiscal Policy

来源：评论

学校读者我要写书评

暂无评论

Asymmetric causality between Bitcoin and tech stocks in the US market using mixed frequency data

引用

JOURNAL OF ECONOMIC STUDIES 2024年第3期51卷 569-586页

作者： Valadkhani, Abbas Swinburne Univ Technol Dept Accounting Econ & Finance Hawthorn Australia

PurposeThis study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S & P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more ***/methodology/approachThis paper utilizes an Unrestricted Asymmetric mixed data sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer *** study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to *** implicationsThe findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their p

关键词： Exchange-traded funds Bitcoin Stock market Causality mixed data sampling E44 G11 G23 G32

来源：评论

学校读者我要写书评

暂无评论

Banking Innovation, Financial Inclusion and Economic Growth in Nigeria

引用

JOURNAL OF THE KNOWLEDGE ECONOMY 2024年第2期15卷 7014-7043页

作者： Oyadeyi, Olajide Obafemi Awolowo Univ Dept Econ Ife Osun State Nigeria

The paper examined the impacts of financial inclusion and banking innovation on economic growth in Nigeria using monthly and quarterly data from 2009 to 2021. The paper expanded the frontier of knowledge by adopting a monthly and quarterly approach using mixed data sampling (MIDAS) and autoregressive distributed lag (ARDL) to ascertain the level of consistency of the impact of financial inclusion and banking innovation on economic growth and identify the banking channel which has the strongest impact on economic growth. data on the variables were sourced from 2021 editions of the Central Bank of Nigeria Statistical Bulletin and the World Development Indicator. The findings showed that financial inclusion and banking innovation channels such as ATM and mobile transactions had a positive impact on economic growth across the ARDL and MIDAS approaches. The impact of the value of POS and web transactions was also significant, but their results were not consistent across the two techniques, while the value of cheque transactions does not impact economic growth using the ARDL approach. The results also showed that the value of POS transactions had the strongest impact on economic growth, followed by ATM, mobile, and web transactions respectively. As a result of these findings, the paper concluded that financial inclusion and banking sector innovations have an important role to play in fostering economic growth in Nigeria. Finally, the paper is unique as it is the first to compare the consistency of findings across the MIDAS and ARDL approach on the study objectives.

关键词： Banking innovation Financial inclusion Financial innovation Economic growth mixed data sampling Financial intermediation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：