Relative forecasting performance of volatility models: Monte Carlo evidence
From MaRDI portal
Publication:5397468
DOI10.1080/14697688.2013.795675zbMath1281.91189OpenAlexW2009550281WikidataQ56609282 ScholiaQ56609282MaRDI QIDQ5397468
Leonardo Morales-Arias, Thomas C. H. Lux
Publication date: 20 February 2014
Published in: Quantitative Finance (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10419/30039
Related Items (2)
Forecasting and trading high frequency volatility on large indices ⋮ Short-term volatility forecasting with kernel support vector regression and Markov switching multifractal model
Cites Work
- Persistence in forecasting performance and conditional combination strategies
- Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment
- Empirical wavelet analysis of tail and memory properties of LARCH and FIGARCH models
- Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching
- Fractionally integrated generalized autoregressive conditional heteroskedasticity
- A simple nonlinear time series model with misleading linear properties
- Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study
- Quasi-maximum likelihood estimation of stochastic volatility models
- The detection and estimation of long memory in stochastic volatility
- Generalized Levinson--Durbin and Burg algorithms.
- Forecasting exchange rate volatility.
- Forecasting volatility under fractality, regime-switching, long memory and Student-\(t\) innovations
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Multivariate Stochastic Variance Models
- Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities
- Modeling and Forecasting Realized Volatility
This page was built for publication: Relative forecasting performance of volatility models: Monte Carlo evidence