Add to ORKGThis paper provides empirical evidence that continuous time models with one factor of volatility, in some conditions, are able to fit the main characteristics of financial data. It also reports the importance of the feedback factor in capturing the strong volatility clustering of data, caused by a possible change in the pattern of volatility in the last part of the sample. We use the Efficient Method of Moments (EMM) by Gallant and Tauchen (1996) to estimate logarithmic models with one and two stochastic volatility factors (with and without feedback) and to select among them
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity mark...
This paper sets up a statistical framework for modeling realised volatility (RV ) using a Dynamic Co...
This thesis consists of three studies that centre around forecasting realised volatility based on hi...
This paper provides empirical evidence that continuous time models with one factor of volatility, in...
This paper provides empirical evidence that continuous time models with one factor of volatility, in...
This paper provides empirical evidence that continuous time models with one factor of volatility, in...
This paper provides empirical evidence that continuous time models with one factor of volatility are...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
This paper explains in non-technical terms various techniques used to measure volatility ranging fro...
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity mark...
This Chapter reviews the main classes of models that incorporate volatility, with a focus on the mos...
This paper compares empirically the forecasting performance of a continuous time stochastic volatili...
This paper evaluates the forecasting performance of a continuous stochastic volatility model with tw...
A volatility model must be able to forecast volatility. This is the central requirement in almost al...
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity mark...
This paper sets up a statistical framework for modeling realised volatility (RV ) using a Dynamic Co...
This thesis consists of three studies that centre around forecasting realised volatility based on hi...
This paper provides empirical evidence that continuous time models with one factor of volatility, in...
This paper provides empirical evidence that continuous time models with one factor of volatility, in...
This paper provides empirical evidence that continuous time models with one factor of volatility, in...
This paper provides empirical evidence that continuous time models with one factor of volatility are...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
This paper explains in non-technical terms various techniques used to measure volatility ranging fro...
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity mark...
This Chapter reviews the main classes of models that incorporate volatility, with a focus on the mos...
This paper compares empirically the forecasting performance of a continuous time stochastic volatili...
This paper evaluates the forecasting performance of a continuous stochastic volatility model with tw...
A volatility model must be able to forecast volatility. This is the central requirement in almost al...
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity mark...
This paper sets up a statistical framework for modeling realised volatility (RV ) using a Dynamic Co...
This thesis consists of three studies that centre around forecasting realised volatility based on hi...