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Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model
Building on the GJR-GARCH model, this paper uses the mixed-data sampling (MIDAS) approach to link monthly realized volatility of EU carbon future...
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A Literature Review on the Model of EGARCH-MIDAS, LMM, GBM for Stock Market Prediction
The stock market prediction has been an active research area in finance and eco-nomics for decades. In recent years, mathematical models have often...
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Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model
This study investigates the impact of economic policy uncertainty (EPU) on the volatility of European Union (EU) carbon futures prices and whether it...
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Forecasting Annual Inflation Using Weekly Money Supply
Forecasting inflation may be challenging, especially when inflation is high. Over the past decades, many develo** countries have faced, and some...
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Portfolio Optimization During the COVID-19 Epidemic: Based on an Improved QBAS Algorithm and a Dynamic Mixed Frequency Model
The determination of weights and the measurement of risk have been the core problems of portfolio optimization. In this paper, we propose the...
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Banking Innovation, Financial Inclusion and Economic Growth in Nigeria
The paper examined the impacts of financial inclusion and banking innovation on economic growth in Nigeria using monthly and quarterly data from 2009...
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Forecasting GDP with many predictors in a small open economy: forecast or information pooling?
This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into...
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Spillover effects of carbon, energy, and stock markets considering economic policy uncertainty
This paper uses a two-regime Markov-switching GARCH model to illustrate that the state-switching of returns in the EU carbon market and its...
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Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)
This paper studies the comparative predictive accuracy of forecasting methods using mixed-frequency data, as applied to nowcasting Philippine...
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Nowcasting East German GDP growth: a MIDAS approach
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans...
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Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors
For modeling the threshold effect in parameters of the mixed data sampling (MIDAS) models, this paper introduces a model called threshold mixed data...
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Does the macroeconomy matter to market volatility? Evidence from US industries
The paper employs a generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) model to examine the relationship...
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Recession Risk Prediction with Machine Learning and Big Panel Data
The machine learning models have been considered a good choice for forecast recession, especially with multiple variables. In this paper, we compare...
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Threshold mixed data sampling logit model with an application to forecasting US bank failures
This paper introduces a threshold mixed data sampling logit (TM-logit) model, which allows for a threshold effect of independent variables sampled at...
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Benchmarking econometric and machine learning methodologies in nowcasting GDP
Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures....
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The D-model for GDP nowcasting
The paper provides a disaggregated mixed-frequency framework for the estimation of GDP. The GDP is disaggregated into components that can be...
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Unleashing the Potential of Mixed Frequency Data: Measuring Risk with Dynamic Tail Index Regression Model
Understanding why extreme events occur is crucial in many fields, particularly in managing financial market risk. In order to explain such...
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Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth
We run a ‘horse race’ among popular forecasting methods, including machine learning (ML) and deep learning (DL) methods, that are employed to...
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Flow count data-driven static traffic assignment models through network modularity partitioning
Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a...
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A mixed-frequency VAR application to studying joint dynamics of foreign investor trading and stock market returns
We present the first application of the mixed-frequency VAR (MF-VAR) method in the market microstructure literature, studying the interaction between...
