Add to ORKGEstimation of the parameters of Garch models for financial data is typically based on daily close-to-close returns. This paper shows that the efficiency of the parameter estimators may be greatly improved by using volatility proxies based on intraday data. The paper develops a Garch quasi maximum likelihood estimator (QMLE) based on these proxies. Examples of such proxies are the realized volatility and the intraday high-low range. Empirical analysis of the S&P 500 index tick data shows that the use of a suitable proxy may reduce the variances of the estimators of the Garch autoregression parameters by a factor 20
The introduction of the Autoregressive Conditional Heteroskedasticity (ARCH) model in 1982 by Engle ...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
Over the past decades, the worldwide financial markets have been continually evolving. Along with th...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
We consider estimates of the parameters of GARCH models of daily financial returns obtained using in...
This paper decomposes volatility proxies according to upward and downward price movements in high-fr...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
Discrete time volatility models typically employ a latent scale factor to represent volatility. High...
In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path...
The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained fo...
GARCH models and their variants are usually estimated using quasi-Maximum Likelihood (QML). Recent w...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
This paper introduces a unified factor overnight GARCH-It\^o model for large volatility matrix estim...
The introduction of the Autoregressive Conditional Heteroskedasticity (ARCH) model in 1982 by Engle ...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
Over the past decades, the worldwide financial markets have been continually evolving. Along with th...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
We consider estimates of the parameters of GARCH models of daily financial returns obtained using in...
This paper decomposes volatility proxies according to upward and downward price movements in high-fr...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
Discrete time volatility models typically employ a latent scale factor to represent volatility. High...
In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path...
The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained fo...
GARCH models and their variants are usually estimated using quasi-Maximum Likelihood (QML). Recent w...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
This paper introduces a unified factor overnight GARCH-It\^o model for large volatility matrix estim...
The introduction of the Autoregressive Conditional Heteroskedasticity (ARCH) model in 1982 by Engle ...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
Over the past decades, the worldwide financial markets have been continually evolving. Along with th...