The impact of jumps and leverage in forecasting covolatility
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Publication:5864641
DOI10.1080/07474938.2017.1307326OpenAlexW2100251878MaRDI QIDQ5864641
Publication date: 8 June 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: http://eprints.ucm.es/28343/1/1502.pdf
Related Items (4)
Realized stochastic volatility with general asymmetry and long memory ⋮ The contribution of intraday jumps to forecasting the density of returns ⋮ Econometric Reviews honors Esfandiar Maasoumi ⋮ Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers
Cites Work
- A discrete-time model for daily S\&P500 returns and realized variations: jumps and leverage effects
- Testing for jumps in noisy high frequency data
- Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data
- Threshold bipower variation and the impact of jumps on volatility forecasting
- Estimating covariation: Epps effect, microstructure noise
- Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
- Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance
- Covariance regularization by thresholding
- Alternative models for stock price dynamics.
- On covariance estimation of non-synchronously observed diffusion processes
- Jump robust daily covariance estimation by disentangling variance and correlation components
- Post-'87 crash fears in the S\&P 500 futures option market
- Regularized estimation of large covariance matrices
- Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches
- Jump robust two time scale covariance estimation and realized volatility budgets
- ESTIMATION OF INTEGRATED COVARIANCES IN THE SIMULTANEOUS PRESENCE OF NONSYNCHRONICITY, MICROSTRUCTURE NOISE AND JUMPS
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