Modeling CAC40 volatility using ultra-high frequency data

Forecasting Volatility using High Frequency Data

Using High-Frequency Data to Model Volatility Dynamics

he covariance matrix of asset returns is important for a wide range of individuals.1 Academics use estimates of the covariance matrix to test asset-pricing theories. Portfolio managers use the covariance matrix in designing tracking strategies where the return on their portfolio is designed to closely follow the return on a benchmark portfolio. Risk managers use the matrix to construct measures...

Efficient Estimation of Volatility using High Frequency Data

The limitations of volatilities computed with daily data as well as simple statistical considerations strongly suggest to use intraday data in order to obtain accurate volatility estimates. Under a continuous time arbitrage-free setup, the quadratic variations of the prices would allow us, in principle, to construct an approximately error free estimate of volatility by using data at the highest...

Ultra high frequency volatility estimation with dependent microstructure noise

We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context ...

Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component

In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the r...