Results 81 to 90 of about 10,511 (262)

Estimating nonlinear time-series models using simulated vector autoregressions [PDF]

Journal of Applied Econometrics, 1993
This paper develops two new methods for conducting formal statistical inference in nonlinear dynamic economic models. The two methods require very little analytical tractability, relying instead on numerical simulation of the model's dynamic behaviour.
openaire   +1 more source

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer, Kristofer Månsson, Pär Sjölander, Gazi Salah Uddin   +3 more
wiley   +1 more source

A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley   +1 more source

Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana

Nova Geodesia
Coastal areas are preferred home to about 40% of global population than any other ecosystems. Many organisms, including endangered species, live along sandy beaches.
Cynthia Borkai BOYE, Peter Ekow BAFFOE, Paul BOYE   +2 more
doaj   +1 more source

Enhancing Volatility Prediction: A Wavelet‐Based Hierarchical Forecast Reconciliation Approach

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting realized volatility (RV) has been widely studied, with numerous techniques developed to enhance predictive accuracy. Among these techniques, the use of RV decompositions based on intraday asset returns has been applied. However, the use of a frequency‐based decomposition, which provides unique insights into the dynamics of RV ...
Adam Clements, Ajith Perera
wiley   +1 more source

Recognition of narrowband rausing autoregressive models and pattern comparison approach

Journal of Telecommunications and Information Technology, 2002
This paper presents an improved spectral recognition method for digitally modulated radio signals. It is based on a signal autoregressive (AR) model. Model poles are used as signal features for neural network based on recognition process.
Jerzy Łopatka
doaj   +1 more source

Regime‐Dependent Nowcasting of the Austrian Economy

Journal of Forecasting, EarlyView.
ABSTRACT We nowcast and forecast economic activity in Austria, namely, real gross domestic product (GDP), consumption, and investment, which are available at a quarterly frequency, using a preselected number of monthly indicators based on a combination of statistical procedures.
Jaroslava Hlouskova, Ines Fortin
wiley   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Intraday Functional PCA Forecasting of Cryptocurrency Returns

Journal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source