Similar Search Results

Range-based volatility estimation and forecasting

Benčík, Daniel - 2014

In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more...

Persistent link: https://www.econbiz.de/10011340612

Range-based volatility estimation and forecasting

Benčík, Daniel - 2014

Persistent link: https://www.econbiz.de/10010461231

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

Virbickaite, Audrone; Nguyen, Hoang; Tran, Minh-Ngoc - 2023

such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and … relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative … inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV …

Persistent link: https://www.econbiz.de/10014331159

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran - 2023

Persistent link: https://www.econbiz.de/10014252427

Forecasting the volatility of energy transition metals

Bastianin, Andrea; Li, Xiao; Shamsudin, Luqman - 2025

The transition to a cleaner energy mix, essential for achieving net-zero greenhouse gas emissions by 2050, will …

Persistent link: https://www.econbiz.de/10015210001

Censored Posterior and Predictive Likelihood in Bayesian Left-Tail Prediction for Accurate Value at Risk Estimation

Gatarek, Lukasz; Hoogerheide, Lennart; Hooning, Koen; … - 2013

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise … posterior and traditional Bayesian Model Averaging techniques in applications of Value-at-Risk prediction in GARCH models. …

Persistent link: https://www.econbiz.de/10010326148

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Koopman, Siem Jan; Lucas, Andre; Scharth, Marcel - 2012

autoregressive score (GAS) models have similar predictive accuracy to correctly specified parameter-driven models. In most cases … alternatives. We also find that GAS models outperform many familiar observation-driven models in terms of forecasting accuracy. The …

Persistent link: https://www.econbiz.de/10010326198

Forecasting Volatility with Copula-Based Time Series Models

Sokolinskiy, Oleg; van Dijk, Dick - 2011

This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious...

Persistent link: https://www.econbiz.de/10010326314

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...

Persistent link: https://www.econbiz.de/10010334248

Metcalfe's law and herding behaviour in the cryptocurrencies market

Pele, Daniel Traian; Mazurencu-Marinescu-Pele, Miruna - 2019

In this paper, the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe's law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's...

Persistent link: https://www.econbiz.de/10011984445