Results 241 to 250 of about 23,089,259 (324)
When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns
ABSTRACT We study how statistical forecast gains for Bitcoin translate into trading profits. Using real‐time out‐of‐sample forecasts from daily bivariate VARs from October 2021 to February 2024, we show that Bitcoin returns are forecastable and that seven predictive indices yield significant gains in directional accuracy (DA).
Rehan Arain, Stephen Snudden
wiley +1 more source
Keeping Faith with Homoskedasticity or Going Back to Heteroskedasticity? The Case of Fatang Stocks
Curto JD.
europepmc +1 more source
Mortality Forecasting Using Variational Inference
ABSTRACT This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two‐step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use.
Patrik Andersson, Mathias Lindholm
wiley +1 more source
Economic Causes ofDeforestation in the Brazilian Amazon: an Empirical Analysis of the2000s [PDF]
Jorge Hargrave Gonçalves da Silva
core
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c$$ c $$ latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley +1 more source
ABSTRACT Using online job advertisement data improves the timeliness and granularity depth of analysis in the labor market in domains not covered by official data. Specifically, its variation over time may be used as an anticipator of official employment variations.
Pietro Giorgio Lovaglio +1 more
wiley +1 more source
ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann +2 more
wiley +1 more source
Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
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 +3 more
wiley +1 more source
A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios
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

