High frequency data are often used to construct proxies for the daily volatility in discrete time volatility models. This paper introduces a calculus for such proxies, making it possible to compare and optimize them. The two distinguishing features of the approach are (1) a simple continuous time extension of discrete time volatility models and (2) an abstract definition of volatility proxy. The theory is applied to eighteen years worth of S&P 500 index data. It is used to construct a proxy that outperforms realized volatility.Les données de haute fréquence sont souvent employées pour construire des proxies pour la volatilité journalière dans les modèles de volatilité à temps discret. Cet article présente une théorie mathématique pour de te...
In this paper we show how to compute a daily VaR measure for two stock indexes (CAC40 and SP500) usi...
textabstractThis paper proposes a new method for estimating continuous-time stochastic volatility (S...
In this paper we compare the forecast performance of continuous and discrete-time volatility models...
High frequency data are often used to construct proxies for the daily volatility in discrete time vo...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
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...
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...
This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained f...
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...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
Volatility prediction is the key variable in forecasting the prices of options, value-at-risk and, i...
The study provides evidence in favour of the price range as a proxy estimator of volatility in finan...
In this paper we show how to compute a daily VaR measure for two stock indexes (CAC40 and SP500) usi...
textabstractThis paper proposes a new method for estimating continuous-time stochastic volatility (S...
In this paper we compare the forecast performance of continuous and discrete-time volatility models...
High frequency data are often used to construct proxies for the daily volatility in discrete time vo...
Daily volatility proxies based on intraday data, such as the high-low range and the realized volatil...
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...
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...
This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained f...
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...
Estimation of the parameters of Garch models for financial data is typically based on daily close-to...
Volatility prediction is the key variable in forecasting the prices of options, value-at-risk and, i...
The study provides evidence in favour of the price range as a proxy estimator of volatility in finan...
In this paper we show how to compute a daily VaR measure for two stock indexes (CAC40 and SP500) usi...
textabstractThis paper proposes a new method for estimating continuous-time stochastic volatility (S...
In this paper we compare the forecast performance of continuous and discrete-time volatility models...