A review of predictive uncertainty estimation with machine learning
H Tyralis, G Papacharalampous - Artificial Intelligence Review, 2024 - Springer
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
A critical investigation of cryptocurrency data and analysis
C Alexander, M Dakos - Quantitative Finance, 2020 - Taylor & Francis
Full article: A critical investigation of cryptocurrency data and analysis Skip to Main Content
Taylor and Francis Online homepage Taylor and Francis Online homepage Log in | Register Cart …
Taylor and Francis Online homepage Taylor and Francis Online homepage Log in | Register Cart …
[图书][B] Bayesian inference of state space models
K Triantafyllopoulos - 2021 - Springer
The discovery and development of state space models and the Kalman filter have their roots
in the dynamical and control systems research communities. The Kalman filter was …
in the dynamical and control systems research communities. The Kalman filter was …
Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis
N Chalissery, S Anagreh, M Nishad T… - Journal of Risk and …, 2022 - mdpi.com
The past few years have witnessed renewed interest in modelling and forecasting
asymmetry in financial time series using a variety of approaches. The most intriguing of …
asymmetry in financial time series using a variety of approaches. The most intriguing of …
Bayesian semiparametric modeling of realized covariance matrices
This paper introduces several new Bayesian nonparametric models suitable for capturing
the unknown conditional distribution of realized covariance (RCOV) matrices. Existing …
the unknown conditional distribution of realized covariance (RCOV) matrices. Existing …
How does oil price uncertainty affect output in the Central and Eastern European economies?–the Bayesian-based approaches
D Živkov, J Đurašković - Applied Economic Analysis, 2022 - emerald.com
How does oil price uncertainty affect output in the Central and Eastern European economies?
– the Bayesian-based approaches | Emerald Insight Books and journals Case studies Expert …
– the Bayesian-based approaches | Emerald Insight Books and journals Case studies Expert …
The dependence structure and portfolio risk of Malaysia's foreign exchange rates: the Bayesian GARCH–EVT–copula model
Purpose This paper investigates the dependence structure and market risk of the currency
exchange rate portfolio from the Malaysian ringgit perspective. Design/methodology …
exchange rate portfolio from the Malaysian ringgit perspective. Design/methodology …
Uncertainty in systemic risks rankings: bayesian and frequentist analysis
E Goldman - Finance Research Letters, 2023 - Elsevier
Abstract We propose efficient Bayesian Hamiltonian Monte Carlo method for estimation of
systemic risk measures, LRMES, SRISK and Δ C o V a R, and apply it for thirty global …
systemic risk measures, LRMES, SRISK and Δ C o V a R, and apply it for thirty global …
Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo
The advantages of sequential Monte Carlo (SMC) are exploited to develop parameter
estimation and model selection methods for GARCH (Generalized AutoRegressive …
estimation and model selection methods for GARCH (Generalized AutoRegressive …
Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model
MG Sampid, HM Hasim, H Dai - PloS one, 2018 - journals.plos.org
In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian
Markov-switching GJR-GARCH (1, 1) model with skewed Student's-t innovation, copula …
Markov-switching GJR-GARCH (1, 1) model with skewed Student's-t innovation, copula …