DYNAMIC MODELING OF HIGH-DIMENSIONAL CORRELATION MATRICES IN FINANCE
From MaRDI portal
Publication:3166712
DOI10.1142/S0219024912500355zbMath1262.91053MaRDI QIDQ3166712
Helmut Herwartz, Vasyl Golosnoy
Publication date: 15 October 2012
Published in: International Journal of Theoretical and Applied Finance (Search for Journal in Brave)
Related Items (1)
Cites Work
- Unnamed Item
- Computing the nearest correlation matrix--a problem from finance
- Predicting volatility: getting the most out of return data sampled at different frequencies
- The Wishart autoregressive process of multivariate stochastic volatility
- Efficient estimation of a multivariate multiplicative volatility model
- Forecasting multivariate realized stock market volatility
- A reduced form framework for modeling volatility of speculative prices based on realized variation measures
- Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
- The conditional autoregressive Wishart model for multivariate stock market volatility
- Formulation and estimation of dynamic models using panel data
- Jump robust daily covariance estimation by disentangling variance and correlation components
- Microstructure Noise, Realized Variance, and Optimal Sampling
- Using High-Frequency Data in Dynamic Portfolio Choice
- Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?
- Robust estimation and outlier detection with correlation coefficients
- Forecasting Using Principal Components From a Large Number of Predictors
- The Distribution of Realized Exchange Rate Volatility
- Modeling and Forecasting Realized Volatility
- A Tale of Two Time Scales
This page was built for publication: DYNAMIC MODELING OF HIGH-DIMENSIONAL CORRELATION MATRICES IN FINANCE