Add to ORKGDiscrete time volatility models typically employ a latent scale factor to represent volatility. High frequency data may be used to construct proxies for these scale factors. Examples are the intraday high-low range and the realized volatility. This paper develops a method for ranking and optimizing volatility proxies. It is possible to outperform the quadratic variation as a proxy for the discrete time scale factor. For the S&P 500 index data over the years 1988-2006 this is achieved by a proxy which puts, among other things, more weight on the highs than on the lows over intraday intervals
This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained f...
We develop a novel observation-driven model for high-frequency prices. We account for irregularly sp...
We develop a discrete-time stochastic volatility option pricing model, which exploits the informatio...
Discrete time volatility models typically employ a latent scale factor to represent volatility. High...
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...
High frequency data are often used to construct proxies for the daily volatility in discrete time vo...
This paper decomposes volatility proxies according to upward and downward price movements in high-fr...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
This paper proposes a new method for estimating continuous-time stochastic volatility (SV) models fo...
The study provides evidence in favour of the price range as a proxy estimator of volatility in finan...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
textabstractThis paper proposes a new method for estimating continuous-time stochastic volatility (S...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained f...
We develop a novel observation-driven model for high-frequency prices. We account for irregularly sp...
We develop a discrete-time stochastic volatility option pricing model, which exploits the informatio...
Discrete time volatility models typically employ a latent scale factor to represent volatility. High...
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...
High frequency data are often used to construct proxies for the daily volatility in discrete time vo...
This paper decomposes volatility proxies according to upward and downward price movements in high-fr...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
This paper proposes a new method for estimating continuous-time stochastic volatility (SV) models fo...
The study provides evidence in favour of the price range as a proxy estimator of volatility in finan...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
textabstractThis paper proposes a new method for estimating continuous-time stochastic volatility (S...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained f...
We develop a novel observation-driven model for high-frequency prices. We account for irregularly sp...
We develop a discrete-time stochastic volatility option pricing model, which exploits the informatio...