Journal ArticleDOI
A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss
Reads0
Chats0
TLDR
The authors used a boosting approach to study the time-varying out-of-sample informational content of various financial and macroeconomic variables for forecasting the volatility of gold-price fluctuations.About:
This article is published in Resources Policy.The article was published on 2016-03-01. It has received 24 citations till now. The article focuses on the topics: Volatility (finance) & Boosting (machine learning).read more
Citations
More filters
Journal ArticleDOI
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Leandro dos Santos Coelho,Leandro dos Santos Coelho +3 more
TL;DR: The use of ensembles is recommended to forecast agricultural commodities prices one month ahead, since a more assertive performance is observed, which allows to increase the accuracy of the constructed model and reduce decision-making risk.
Journal ArticleDOI
Forecasting realized oil-price volatility : the role of financial stress and asymmetric loss
TL;DR: In this article, the role of global and regional measures of financial stress in forecasting realized volatility of the oil market based on 5-min intraday data covering the period of 4th January, 2000 until 26th May, 2017 was analyzed.
Journal ArticleDOI
Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks
TL;DR: In this article, the effects of leverage, jumps, spillovers, and geopolitical risks on the covariance matrix of crude oil and gold futures were examined. But the results of the analysis were limited to the conditional Wishart model.
Journal ArticleDOI
Forecasting realized gold volatility: Is there a role of geopolitical risks?
TL;DR: The authors used a quantile-regression heterogeneous autoregressive realized volatility (QR-HAR-RV) model to study whether geopolitical risks have predictive value in sample and out-of-sample for realized gold-returns volatility estimated from intradaily data.
Journal ArticleDOI
Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility
Gabriel Trierweiler Ribeiro,André A. P. Santos,André A. P. Santos,Viviana Cocco Mariani,Leandro dos Santos Coelho +4 more
TL;DR: A novel hybrid model is proposed, named HAR-PSO-ESN, which combines the feature design of the HAR model with the prediction power of ESN, and the consistent PSO metaheuristic approach for hyperparameters tuning, which produces more accurate predictions on most of the cases.
References
More filters
Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Journal ArticleDOI
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.
Journal ArticleDOI
Modeling and forecasting realized volatility
TL;DR: In this article, the authors provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.
Related Papers (5)
Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold
Dirk G. Baur,Brian M. Lucey +1 more