Jump robust two time scale covariance estimation and realized volatility budgets
From MaRDI portal
Publication:4683042
DOI10.1080/14697688.2012.741692zbMath1398.62283OpenAlexW2044230091MaRDI QIDQ4683042
Publication date: 19 September 2018
Published in: Quantitative Finance (Search for Journal in Brave)
Full work available at URL: https://lirias.kuleuven.be/handle/123456789/386491
Applications of statistics to actuarial sciences and financial mathematics (62P05) Analysis of variance and covariance (ANOVA) (62J10) Portfolio theory (91G10)
Related Items (10)
Robust covariance estimation with noisy high-frequency financial data ⋮ Forecasting and trading high frequency volatility on large indices ⋮ A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio ⋮ ETF basket-adjusted covariance estimation ⋮ Sparse Kalman filtering approaches to realized covariance estimation from high frequency financial data ⋮ Testing for Jump Spillovers Without Testing for Jumps ⋮ On the estimation of integrated volatility in the presence of jumps and microstructure noise ⋮ The impact of jumps and leverage in forecasting covolatility ⋮ Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume ⋮ Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers
Uses Software
Cites Work
- Jump-robust volatility estimation using nearest neighbor truncation
- Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps
- Threshold bipower variation and the impact of jumps on volatility forecasting
- Estimating covariation: Epps effect, microstructure noise
- Threshold estimation of Markov models with jumps and interest rate modeling
- Ultra high frequency volatility estimation with dependent microstructure noise
- Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
- Jump robust daily covariance estimation by disentangling variance and correlation components
- Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?
- Realized kernels in practice: trades and quotes
- Transformation of non positive semidefinite correlation matrices
- Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection
- High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data
- A Tale of Two Time Scales
This page was built for publication: Jump robust two time scale covariance estimation and realized volatility budgets
