This paper provides empirical evidence that continuous time models with one factor of volatility are, in some circumstances, able to fit the main characteristics of financial data and reports insights about the importance of introducing feedback factors for capturing the strong persistence caused by the presence of changes in the variance. We use the Efficient Method of Moments (EMM) by Gallant and Tauchen (1996) to estimate and to select among logarithmic models with one and two stochastic volatility factors (with and without feedback)
A volatility model must be able to forecast volatility; this is the central requirement in almost al...
This paper explains in non-technical terms various techniques used to measure volatility ranging fro...
We use high frequency financial data to proxy, via the realised variance, each day's financial varia...
This paper provides empirical evidence that continuous time models with one factor of volatility are...
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...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
This paper evaluates the forecasting performance of a continuous stochastic volatility model with tw...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
This paper compares empirically the forecasting performance of a continuous time stochastic volatili...
The persistent nature of equity volatility is investigated by means of a multi-factor stochastic vol...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
The persistent nature of equity volatility is investigated by means of a multi-factorstochastic vola...
The majority of asset pricing theories relate expected returns on assets to their conditional varian...
A volatility model must be able to forecast volatility; this is the central requirement in almost al...
This paper explains in non-technical terms various techniques used to measure volatility ranging fro...
We use high frequency financial data to proxy, via the realised variance, each day's financial varia...
This paper provides empirical evidence that continuous time models with one factor of volatility are...
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...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
This paper evaluates the forecasting performance of a continuous stochastic volatility model with tw...
In this paper we fit the main features of financial returns by means of a two factor long memory sto...
This paper compares empirically the forecasting performance of a continuous time stochastic volatili...
The persistent nature of equity volatility is investigated by means of a multi-factor stochastic vol...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
The persistent nature of equity volatility is investigated by means of a multi-factorstochastic vola...
The majority of asset pricing theories relate expected returns on assets to their conditional varian...
A volatility model must be able to forecast volatility; this is the central requirement in almost al...
This paper explains in non-technical terms various techniques used to measure volatility ranging fro...
We use high frequency financial data to proxy, via the realised variance, each day's financial varia...
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