相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Forecasting Financial Returns Volatility: A GARCH-SVR Model
Hao Sun et al.
COMPUTATIONAL ECONOMICS (2020)
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
Matheus Henrique Dal Molin Ribeiro et al.
CHAOS SOLITONS & FRACTALS (2020)
Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
Monghwan Seo et al.
APPLIED SCIENCES-BASEL (2020)
A new investment method with AutoEncoder: Applications to crypto currencies
Masafumi Nakano et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Volatility forecasting accuracy for Bitcoin
Gerrit Koechling et al.
ECONOMICS LETTERS (2020)
Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple
Pinar Kaya Soylu et al.
JOURNAL OF RISK AND FINANCIAL MANAGEMENT (2020)
Forecasting of BTC volatility: comparative study between parametric and nonparametric models
Rohaifa Khaldi et al.
PROGRESS IN ARTIFICIAL INTELLIGENCE (2019)
Modelling volatility of cryptocurrencies using Markov-Switching GARCH models
Guglielmo Maria Caporale et al.
RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE (2019)
Forecasting Bitcoin risk measures: A robust approach
Carlos Trucios
INTERNATIONAL JOURNAL OF FORECASTING (2019)
Forecast Bitcoin Volatility with Least Squares Model Averaging
Tian Xie
ECONOMETRICS (2019)
Machine-Learning Models for Sales Time Series Forecasting
Bohdan M. Pavlyshenko
DATA (2019)
Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model
Mergani A. Khairalla et al.
ENERGIES (2018)
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
Federico Divina et al.
ENERGIES (2018)
A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis
Werner Kristjanpoller et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
Dealing with seasonality by narrowing the training set in time series forecasting with kNN
Francisco Martinez et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models
Ha Young Kim et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression
Yaohao Peng et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
Bitcoin technical trading with artificial neural network
Masafumi Nakano et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2018)
Scaling properties of extreme price fluctuations in Bitcoin markets
Stjepan Begusic et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2018)
Ensemble learning: A survey
Omer Sagi et al.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2018)
Random forest regression for online capacity estimation of lithium-ion batteries
Yi Li et al.
APPLIED ENERGY (2018)
Bitcoin, gold and the US dollar - A replication and extension
Dirk G. Baur et al.
FINANCE RESEARCH LETTERS (2018)
Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis
Christian Conrad et al.
JOURNAL OF RISK AND FINANCIAL MANAGEMENT (2018)
Cryptocurrency: A New Investment Opportunity?
David Lee Kuo Chuen et al.
JOURNAL OF ALTERNATIVE INVESTMENTS (2018)
Volatility and return jumps in bitcoin
Pedro Chaim et al.
ECONOMICS LETTERS (2018)
Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors
R. Werner Kristjanpoller et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Volatility estimation for Bitcoin: A comparison of GARCH models
Paraskevi Katsiampa
ECONOMICS LETTERS (2017)
Real-time inflation forecasting with high-dimensional models: The case of Brazil
Marcio G. P. Garcia et al.
INTERNATIONAL JOURNAL OF FORECASTING (2017)
Volatility forecasting via SVR-GARCH with mixture of Gaussian kernels
Pedro Correia S. Bezerra et al.
COMPUTATIONAL MANAGEMENT SCIENCE (2017)
Forecasting volatility of oil price using an artificial neural network-GARCH model
Werner Kristjanpoller et al.
EXPERT SYSTEMS WITH APPLICATIONS (2016)
A new model selection strategy in time series forecasting with artificial neural networks: IHTS
Serkan Aras et al.
NEUROCOMPUTING (2016)
Principal component analysis: a review and recent developments
Ian T. Jolliffe et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2016)
The economics of BitCoin price formation
Pavel Ciaian et al.
APPLIED ECONOMICS (2016)
Bitcoin, gold and the dollar - A GARCH volatility analysis
Anne Haubo Dyhrberg
FINANCE RESEARCH LETTERS (2016)
Gold price volatility: A forecasting approach using the Artificial Neural Network-GARCH model
Werner Kristjanpoller et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm
Guillermo Santamaria-Bonfil et al.
COMPUTATIONAL ECONOMICS (2015)
Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks
Michael J. Kane et al.
BMC BIOINFORMATICS (2014)
A hybrid modeling approach for forecasting the volatility of S&P 500 index return
E. Hajizadeh et al.
EXPERT SYSTEMS WITH APPLICATIONS (2012)
Jump-robust volatility estimation using nearest neighbor truncation
Torben G. Andersen et al.
JOURNAL OF ECONOMETRICS (2012)
Volatility forecast comparison using imperfect volatility proxies
Andrew J. Patton
JOURNAL OF ECONOMETRICS (2011)
Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange
Melike Bildirici et al.
EXPERT SYSTEMS WITH APPLICATIONS (2009)
A forecast comparison of volatility models: Does anything beat a GARCH(1,1)?
PR Hansen et al.
JOURNAL OF APPLIED ECONOMETRICS (2005)
Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities
TG Andersen et al.
ECONOMETRICA (2005)
Using support vector machines for time series prediction
U Thiessen et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2003)
Financial time series forecasting using support vector machines
KJ Kim
NEUROCOMPUTING (2003)
Range-based estimation of stochastic volatility models
S Alizadeh et al.
JOURNAL OF FINANCE (2002)
分享论文
