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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

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

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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

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

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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

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

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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

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A component model for dynamic correlations

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JOURNAL OF ECONOMETRICS 2011年第1期164卷 45-59页

作者： Colacito, Riccardo Engle, Robert F. Ghysels, Eric Univ N Carolina Dept Econ Chapel Hill NC 27599 USA Univ N Carolina Kenan Flagler Business Sch Div Finance Chapel Hill NC 27599 USA NYU Stern Sch Business Dept Finance New York NY 10003 USA

We propose a model of dynamic correlations with a short- and long-run component specification, by extending the idea of component models for volatility. We call this class of models DCC-MIDAS. The key ingredients are the Engle (2002) DCC model, the Engle and Lee (1999) component GARCH model replacing the original DCC dynamics with a component specification and the Engle et al. (2006) GARCHMIDAS specification that allows us to extract a long-run correlation component via mixed data sampling. We provide a comprehensive econometric analysis of the new class of models, and provide extensive empirical evidence that supports the model's specification. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Dynamic correlations Forecasting mixed data sampling

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Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks

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COMPUTATIONAL STATISTICS & data ANALYSIS 2014年 76卷 43-60页

作者： Audrino, Francesco Univ St Gallen Dept Econ CH-9000 St Gallen Switzerland

The predictive power of recently introduced components affecting correlations is investigated. The focus is on models allowing for a flexible specification of the short-run component of correlations as well as the long-run component. Moreover, models allowing the correlation dynamics to be subjected to regime-shift caused by threshold-based structural breaks of a different nature are also considered. The results indicate that in some cases there may be a superimposition of the long-term and short-term movements in correlations. Therefore, care is called for in interpretations when estimating the two components. Testing the forecasting accuracy of correlations during the late-2000s financial crisis yields mixed results. In general, component models allowing for a richer correlation specification possess an increased predictive accuracy. Economically speaking, no relevant gains are found by allowing for more flexibility in the correlation dynamics. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Correlation forecasting Component models Threshold regime-switching models mixed data sampling Performance evaluation

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The conditional autoregressive Wishart model for multivariate stock market volatility

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JOURNAL OF ECONOMETRICS 2012年第1期167卷 211-223页

作者： Golosnoy, Vasyl Gribisch, Bastian Liesenfeld, Roman Univ Kiel Inst Stat & Econometr D-24118 Kiel Germany

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a mixed data sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Component volatility models Covariance matrix mixed data sampling Observation-driven models Realized volatility

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Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit

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JOURNAL OF ECONOMETRICS 2021年第2期220卷 589-605页

作者： Khalaf, Lynda Kichian, Maral Saunders, Charles J. Voia, Marcel Carleton Univ Econ Dept Ottawa ON Canada Carleton Univ Ctr Monetary & Financial Econ CMFE Ottawa ON Canada Ctr Interuniv Rech Econ Quantitat CIREQ Montreal PQ Canada Univ Ottawa Econ Dept Ottawa ON Canada Univ Ottawa Grad Sch Publ & Int Affairs Ottawa ON Canada Univ Western Univ Dept Econ London ON Canada Univ Orleans Orleans France

This paper introduces mixed data sampling (MIDAS) into the panel data context. To address the unidentified nuisance parameter problem, we propose to invert model specification tests for inference on the MIDAS parameter along with bounds tests for model coefficients. Illustrative identification, simulation and empirical analyses are conducted in the dynamic GMM framework. Our framework allows for departures from i.i.d errors such as clustering and dynamic specifications. A simulation study and an application to a model of reserve holdings illustrate the usefulness of the proposed methods, and more broadly set a promising template for shrinkage approaches. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Dynamic panel model mixed data sampling Unidentified nuisance parameter Reserve holdings model

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

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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

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Deep learning on mixed frequency data

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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

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