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 GARCH model is modified to capture the effect on volatilities of the consecutive number of days ...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
[[abstract]]This article investigates the feasibility of using range-based estimators to evaluate an...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
We consider estimates of the parameters of GARCH models of daily financial returns obtained using in...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
GARCH models and their variants are usually estimated using quasi-Maximum Likelihood (QML). Recent w...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
This paper decomposes volatility proxies according to upward and downward price movements in high-fr...
Over the past decades, the worldwide financial markets have been continually evolving. Along with th...
GARCH models are useful tools in the investigation of phenomena, where volatility changes are promin...
Forecasting volatility with precision in financial market is very important. This paper examines the...
This paper studies the performance of GARCH model and its modifications, using the rate of returns f...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
The GARCH model is modified to capture the effect on volatilities of the consecutive number of days ...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
[[abstract]]This article investigates the feasibility of using range-based estimators to evaluate an...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
We consider estimates of the parameters of GARCH models of daily financial returns obtained using in...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
GARCH models and their variants are usually estimated using quasi-Maximum Likelihood (QML). Recent w...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
This paper decomposes volatility proxies according to upward and downward price movements in high-fr...
Over the past decades, the worldwide financial markets have been continually evolving. Along with th...
GARCH models are useful tools in the investigation of phenomena, where volatility changes are promin...
Forecasting volatility with precision in financial market is very important. This paper examines the...
This paper studies the performance of GARCH model and its modifications, using the rate of returns f...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
The GARCH model is modified to capture the effect on volatilities of the consecutive number of days ...
Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accu...
[[abstract]]This article investigates the feasibility of using range-based estimators to evaluate an...