Mixed Data Sampling Research Articles

Exploring Multisource High‐Dimensional Mixed‐Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach

Exploring Multisource High‐Dimensional Mixed‐Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach

Predicting GDP with Machine Learning Technique

This paper proposes a method for reducing model errors in regressions when modelling macroeconomic variables by using machine learning algorithms and traditional time series regression models. In this paper, machine learning models are subjected to repeated k-fold cross validation and hyperparameter tuning. The linear model uses repeated k-fold cross validation, on the other hand, the traditional time series model Mixed Data Sampling Auto Regressive Distribution Lag model is run without repeated k-fold cross validation and hyperparameter tuning. The results show that integrating repeated k-fold cross validation with hyperparameter tuning increases the overall performance of machine learning algorithms and each model records the average outcome from all folds and runs. These findings demonstrate how machine learning models outperform the traditional time series model.

Which daily equity returns improve output forecasts?

We document the improvements in short term forecasts of real output growth for the United States and the euro area from incorporating daily financial data and using mixed data sampling (MIDAS) regressions. Furthermore, we show that a significant share of forecast improvements are driven by information embedded in stock returns of large, capital-intensive firms. In comparison, labor-intensive firms contribute less to improvements in output forecasts within a MIDAS framework.

Forecasting the market value of power battery industry chain: A novel RRMIDAS-SVR model

The emergence of electric vehicles has contributed to mitigating air pollution and greenhouse effects caused by traditional fuel vehicles. The power battery industry chain, which is a primary component of electric vehicles, requires more attention to monitor its development status. This study proposes a novel method for forecasting the development status of the power battery industry chain by monitoring the market value index of all listed companies in the power battery industry. It proposes a new forecasting model, RRMIDAS-SVR, which outlines reverse-restricted mixed data sampling (RRMIDAS) into support vector regression (SVR) to end the data-driven challenges of mixed-frequency data and nonlinear relationships. We estimate the RRMIDAS-SVR model using a quadratic programming problem and mixed-frequency West Texas Intermediate crude oil futures prices, electric vehicle sales, and the consumer price index as predictors of the market value of all listed companies in the power battery industry chain. The experimental findings reveal that the RRMIDAS-SVR model outperforms the other models, as evidenced by its lower mean absolute error and root-mean-square error. This study contributes to understanding the development status of the power battery industry value chain by proposing and developing a new approach, RRMIDAS-SVR, to monitor the industry's development status that considers a multi-source information set. Moreover, this study provides strategic insights for stakeholders in the power battery industry.

Research on China insurance demand forecasting: Based on mixed frequency data model.

In this paper, we introduce the mixed-frequency data model (MIDAS) to China's insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China's insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China's insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China's insurance policymaking.

Linkages between financial and macroeconomic indicators in emerging markets and developing economies

This paper provides empirical evidence on the finance-growth nexus, making key contributions by focusing on previously understudied Emerging Markets and Developing Economies (EMDEs) and employing mixed-frequency data. Utilizing panel forecasting models for 50 countries from 1990 to 2019, we examine the empirical link between macroeconomic indicators (e.g., aggregate production) and financial indicators (e.g., stock market indexes). Our results support the notion that financial indicators can indeed serve as robust predictors of macroeconomic indicators. Further, the use of mixed data sampling (MIDAS) models enhances the results, confirming the presence of valuable predictive information in higher-frequency data, even for lower-income countries. These findings bear particular significance for policymakers and investors, given the persistent challenge of accessing timely and reliable data on real indicators in EMDEs.

Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development

Global efforts to build sustainable e-commerce ecosystems through various prediction tools have suffered due to uncertainty in politics, the economy, and the environment. This paper proposes a new integrative prediction model to track the sustainable development of e-commerce. Using US e-commerce data, this study explores the prediction accuracy of the mixed data sampling (MIDAS) model in combination with the attention mechanism (AM) approach, analyzing the performance differences between the model’s before and after improvements. More so, the paper evaluates the performance of the new prediction approach against other competing models using the prediction accuracy metric, the probability interval test, and the Diebold and Mariann (DM) test methods. The results indicate that the introduction of the MIDAS and the AM approaches allows the prediction model to fully utilize the effective information of the mixed-frequency data while simultaneously capturing the differences in the importance of the variables in terms of their time series and the non-linear relationship of the learning variables, thereby positively influencing the economic prediction of the e-commerce industry. Second, the proposed prediction model combines the ability of long-term and short-term high-precision prediction and performs multi-step probability prediction on the development of the e-commerce industry. It can better track abnormal changes in macroeconomic indicators and fit their fluctuation trends. Third, based on the results of the three evaluation indicators, the MIDAS–AM–Deep autoregressive recurrent neural network (DeepAR) model achieves optimal prediction accuracy, allowing it to provide more timely, accurate, and comprehensive predictions for e-commerce management decisions when macroeconomic conditions are undergoing significant transformations.

Threshold mixed data sampling logit model with an application to forecasting US bank failures

Threshold mixed data sampling logit model with an application to forecasting US bank failures

Enhancing stock volatility prediction with the AO-GARCH-MIDAS model.

Research has substantiated that the presence of outliers in data usually introduces additional errors and biases, which typically leads to a degradation in the precision of volatility forecasts. However, correcting outliers can mitigate these adverse effects. This study corrects the additive outliers through a weighting method and let these corrected values to replace the original outliers. Then, the model parameters are re-estimated based on this new return series. This approach reduces the extent to which outliers distort volatility estimates, allowing the model to better adapt to market conditions and improving the accuracy of volatility forecasts. This study introduces this approach for the first time to generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) models, so as to establish an additional outliers corrected GARCH-MIDAS model (AO-GARCH-MIDAS). This pioneering approach marks a unique innovation. The research employs a diverse array of evaluation methods to validate the model's robustness and consistently demonstrates its dependable performance. Findings unequivocally reveal the substantial influence of outliers on the model's predictive capacity, with the AO-GARCH-MIDAS model exhibiting consistent superiority across all evaluation criteria. Additionally, while the GARCH model showcases stronger estimation capabilities compared to the GARCH-MIDAS model, the latter demonstrates heightened predictive prowess. Notably, regarding variable selection, the results underscore the greater predictive informational value inherent in realized volatility over other low-frequency factors.

A new multi-objective ensemble wind speed forecasting system: Mixed-frequency interval-valued modeling paradigm

Improving wind speed prediction is essential for increasing the use of wind energy and promoting sustainable utilization of resources. Most previous studies relied on single-valued data with a common frequency, resulting in limited wind energy utilization. To fill this research gap and strengthen the precision of wind speed prediction, this study proposes a new multi-objective ensemble forecasting system based on a mixed-frequency interval-valued modeling paradigm for power system decision-making. The system comprises three modules: interval-valued data preprocessing module, mixed-frequency interval-valued forecasting module, and multi-objective ensemble module. Firstly, to reduce the data complexity by preprocessing the mixed-frequency interval-valued wind speed data, an interval-valued data preprocessing module is designed. Then, different types of prediction models, including a mixed data sampling model, two artificial intelligence models, and a traditional statistical model, are used to predict each subsequence in the mixed-frequency interval-valued forecasting module. Finally, a multi-objective ensemble module is designed to ensemble the interval-valued prediction results of each sub-model, and the final interval-valued wind speed forecast results are obtained. The proposed system obtained IMAE values of 0.4634 and 0.3452 at sites from Inner Mongolia and Penglai, China, respectively; this provides a significant tool for wind speed prediction.

Can geopolitical risks impact the long-run correlation between crude oil and clean energy markets? Evidence from a regime-switching analysis

The increasingly volatile and complex geopolitical landscape has impacted the global energy market more frequently, posing serious challenges to the global energy low-carbon transition strategy. However, the heterogeneity of the impact of geopolitical risks (GPR) on the dynamic correlation between oil and clean energy across various market conditions and sub-industries of clean energy remains distinct. To address this gap, this paper combines a dynamic conditional correlation with the mixed data sampling (DCC-MIDAS) model and the Markov regime-switching model to investigate the influence of GPR on the correlation between oil and clean energy, particularly in diverse market conditions. Empirical findings reveal that the impacts of GPR on the long-run correlations of oil-clean energy vary across different market regimes, both in terms of the overall market and industry sectors. Specifically, GPR negatively affects the oil-clean energy correlation with stronger effects in the low-correlation regime. In particular, GPR plays a positive effect on the oil-biofuel nexus, however, the impact of GPR on the oil-fuel cells nexus exhibits opposite directions in high- and low-correlation regimes. Moreover, the transmission of geopolitical risks to the energy markets is predominantly driven by threats (GPT) during periods of low volatility and correlation, while acts (GPA) play a more significant role during periods of high volatility and correlation. These results emphasize the significance for both investors and policymakers in considering the time-varying impact of geopolitical risks across various market conditions and individual clean energy sub-sectors.

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

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.

Re-validating the Phillips Curve hypothesis in Africa and the role of oil prices: A mixed-frequency approach

This paper investigates the relationship between unemployment and inflation in 29 African economies by re-examining the Phillips Curve hypothesis and evaluating the role of global oil prices in predicting inflation. Existing studies often use uniform-frequency variables, which can lead to biased results by omitting information from high-frequency series. We employ a mixed data sampling regression model to analyze inflation in selected African countries by regressing monthly inflation rates on annual unemployment rates and daily oil price returns. Our findings show that the Phillips Curve hypothesis holds in a few countries across Central Africa (Cameroon, the Central African Republic, Chad, and Republic of the Congo), East Africa (Rwanda), North Africa (Algeria), Southern Africa (Botswana and Malawi), and West Africa (Burkina Faso and Mauritania). In the other countries, the hypothesis does not hold, potentially indicating a predominance of cost-push inflation. Our analysis demonstrates that global oil prices impact inflation differently across these countries. Additionally, daily oil price data significantly enhance inflation forecasting compared to monthly data. Incorporating additional economic variables, such as exchange rates and interest rates, further improves forecast accuracy. We recommend targeted monetary policies to mitigate the impacts of oil price fluctuations and unemployment on inflation.

Cryptocurrency returns and consumption-based asset pricing

ABSTRACT This paper examines whether the cross-section of cryptocurrency returns is captured by risk factors based on consumption-based asset pricing. It is an imperative task for financial economists to find the fundamental risk behind characteristic-based cryptocurrency factors in order to economically understand cryptocurrency. To address the data availability issue in the analysis of cryptocurrency, we employ mixed data sampling (MIDAS) regression to check the relation between the principal component analysis (PCA) factors in cryptocurrency returns and the factors in the consumption capital asset pricing models (CCAPMs) and intertemporal capital asset pricing models (ICAPMs). We establish significant links between them, which in turn implies that cryptocurrency returns are investors’ compensation for bearing consumption risk, conditional consumption risk, and intertemporal consumption risk. This finding underscores that cryptocurrency returns are the manifestation of macroeconomic equilibrium derived from investors’ utility maximization.

Do oil price forecast disagreement of survey of professional forecasters predict crude oil return volatility?

This paper explores whether the dispersion in forecasted crude oil prices from the European Central Bank Survey of Professional Forecasters can provide insights for predicting crude oil return volatility. It is well-documented that higher disagreement among forecasters of asset price implies greater uncertainty and higher return volatility. Using several Generalized Autoregressive Conditional Heteroskedasticity with Mixed Data Sampling (GARCH-MIDAS) models, we find, based on the in-sample estimation results, the oil market experiences greater volatility when the forecasters’ disagreements increase. The model that integrates both historical realized variance and forward-looking forecaster disagreement into the conditional variance, along with the model focusing solely on pure forward-looking forecaster disagreement, exhibits a much superior fit to the data compared to the model relying solely on realized variance and the models considering forward-looking forecasted mean return. The out-of-sample forecasting results unequivocally illustrate that incorporating forecaster disagreement offers valuable insights, markedly enhancing the predictive accuracy of crude oil return volatility within the GARCH-MIDAS model. Moreover, we illustrate the economic benefit of considering forecasters’ disagreement when forecasting volatility, demonstrating its significance for VaR risk management.

Nowcasting Vietnam's Export Growth with Mixed Frequency Data

Purpose: The primary objective of this study is to investigate and employ a practical and meaningful nowcasting model to predict Vietnam's export growth based on factors of export supply and demand alongside relevant financial indicators. Theoretical Framework: This study employs the concepts and theories of nowcasting model with mixed frequency data to create the conceptual framework. Methodology: This study employs four commonly-used models in nowcasting: the bridge equation model (BEQ), Bayesian VAR model (BVAR), mixed frequency vector autoregressive model (MFVAR), and mixed data sampling regression (MIDAS). Findings: According to the experimental findings, the mixed frequency data models outperformed the models utilizing the same frequency data in nowcasting Vietnam's export growth. Additionally, this model demonstrated effectiveness in instantaneous and short-term forecasting. MIDAS emerged as the most suitable choice for nowcasting Vietnam's export growth among the models examined. Implication of Research: using data with mixed frequency along with corrresponding methods is the good way for nowcasting. Originality/Value: This study used macroeconomics factors to nowcast the export growth in Vietnam. It applied four different models including BEQ, BVAR, MFVAR, and MIDAS. The study reveals the roles of data and the potential capability in nowcasting of MIDAS model.

Do topic and sentiment matter? Predictive power of online reviews for hotel demand forecasting

Studies integrating textual data for forecasting have mainly focused on the overall sentiment reflected in text. Yet textual data convey various types of information, such as review topics, that can be beneficial when forecasting hotel demand. This study aims to combine topic modeling and sentiment analysis to improve forecasting performance of hotel demand. Specifically, the latent Dirichlet allocation (LDA) topic modeling technique and the long short-term memory (LSTM) model are employed to construct topic-based sentiment indices. The autoregressive integrated moving average (ARIMA) with explanatory variable–type models and mixed data sampling (MIDAS) models are adopted for the evaluation of predictive power. Results reveal that MIDAS forecasting with topic–sentiment and COVID-19 variables generates most accurate forecasts. The findings contextualize the application of online textual big data in hotel demand forecasting research. Hotel management can utilize these online data for short-term forecasting to facilitate crowd management and respond more effectively to unforeseen public health events.

Forecasting the VaR of the crude oil market: A combination of mixed data sampling and extreme value theory

When forecasting the value-at-risk (VaR) of the crude oil market, traditional models often fail to capture the information embedded in low-frequency macro-variables and tend to underestimate the high quantiles caused by adopting commonly used distributions. To address these problems, this paper proposes a new approach, which combines the generalized autoregressive condition heteroskedasticity (GARCH)-mixed data sampling (MIDAS) models with extreme value theory (EVT). Our empirical results show that first, the GARCH-MIDAS models outperform the benchmark models when they incorporate suitable low-frequency macroeconomic variables. Second, the VaR forecasting accuracy of some GARCH-MIDAS models can be further improved when combined with EVT. Third, the EVT-based GARCH-MIDAS model that contains the demand-side information of the oil market achieves the best performance among all the models. Fourth, the historical simulation (HS) method that is widely used by financial institutions is extremely inaccurate.

Nowcasting the Russian economy macroeconomic indicators under uncertainty: Does taking into account the news sentiment help?

In this paper the following models are compared: restricted and unrestricted MIDAS-models (mixed data sampling models), MFBVAR-model (mixed frequency Bayesian vector autoregression), Linear model with regularization (MIDAS_L1-, MIDAS_L2and MIDAS_PC-model) and dynamic factor model. The results are compared with classical autoregression as a benchmark. Production indices for different industries and indicators characterizing Russian GDP and its components, energy prices and PMI of Russia and its main trading partners, as well as indicators derived from the analysis of sentiment of news articles published by a number of major media and blogs are used as explanatory variables. The paper also proposes a method of rapid assessment of the current state of the economy based on data for the first or first two months of the quarter in question only. The use of this approach in combination with news sentiment analysis allows to draw conclusions about current economic situation extremely rapidly. Models’ accuracy is assessed by cross-validation for periods before and after the Q2 2022, the significance of the effect of adding news variables is assessed using the Diebold—Mariano test. When testing during the crisis period (starting from the Q1 2022), the addition of news variables leads to an increase in accuracy for 45% of the models considered, and the average improvement (reduction in the average absolute error) was 1.39 points (the reduction in MAE for the science-based GDP growth rates of Russia is 0.64 p.p.). At the same time, in a calmer (pre-sanction) period, the advantage of news is less noticeable: an increase in accuracy was recorded in 30% of cases with an average decrease in error of 1.54 points (the decrease in MAE for Russia’s GDP growth rate is 0.26 p.p.), and the change accuracy of science data when adding variables reflecting the news background turns out to be statistically insignificant. Thus, the use of news sentiment is not a “silver bullet” in the task of nowcasting Russian GDP, but in times of crisis it can serve as a good and, importantly, very operative indicator of the state of the economy and can be used in conjunction with more traditional explanatory variables.

Revisiting Food Price Volatility in Nigeria: Climate Change or Terrorism?

We offer new insights into the volatility dynamics of food prices based on the increasing vulnerability of farming activity to climate change and terrorism in Nigeria. We employ a Generalized Autoregressive Conditional Heteroscedasticity framework with Mixed Data Sampling (GARCH-MIDAS) to distinguish between realized and exogenously induced food price volatility. We hypothesize and confirm the relative significance of terrorism’s impact on food price volatility in Nigeria. Our results show that terrorism, and not climate change, predominantly induces volatility in Nigeria’s food prices.