Add to ORKGWe analyze the problem of estimating nonparametrically the volatility function of a financial time series. For such estimator, we consider two different nonparametric tools: the Local polynomial estimator and the Neural Network estimator. The two nonparametric methods are compared by means of a simulation study. In the framework analyzed, it is evident that the Local Polynomial procedure outperforms the Neural Network procedure for the estimation of the unknown function, provided that the bandwidth parameter of the kernel estimator is chosen correctly
An autoregressive-ARCH model with possible exogeneous variables is treated. We estimate the conditio...
We consider a vector conditional heteroskedastic autoregressive nonlinear (CHARN) model in which bot...
Time series analysis and prediction are major scientific challenges that find their applications in ...
We analyze the problem of estimating nonparametrically the volatility function of a financial time s...
We consider nonparametric generalization of various well-known financial time series models and stud...
In this paper we consider a class of dynamic models in which both the conditional mean and the condi...
Abstract. A novel type of higher order pipelined neural network, the polynomial pipelined neural net...
AbstractIn this paper, we investigate the volatility dynamics of EUR/GBP currency using statistical ...
The solution of nonparametric regression problems is addressed via polynomial approximators and one-...
Kernel smoothing techniques free the traditional parametric estimators of volatility from the constr...
The problem of automatic bandwidth selection in nonparametric regression is considered when a local ...
In this paper we focus on nonparametric analysis of the volatility function for mixing processes. Ou...
This master thesis aims at estimating state price densities (SPD) via a nonparametric fit of the imp...
Many researchers are interesting in applying the neural networks methods to financial data. In fact ...
This paper is concerned with approximating nonlinear time series by an artificial neural network bas...
An autoregressive-ARCH model with possible exogeneous variables is treated. We estimate the conditio...
We consider a vector conditional heteroskedastic autoregressive nonlinear (CHARN) model in which bot...
Time series analysis and prediction are major scientific challenges that find their applications in ...
We analyze the problem of estimating nonparametrically the volatility function of a financial time s...
We consider nonparametric generalization of various well-known financial time series models and stud...
In this paper we consider a class of dynamic models in which both the conditional mean and the condi...
Abstract. A novel type of higher order pipelined neural network, the polynomial pipelined neural net...
AbstractIn this paper, we investigate the volatility dynamics of EUR/GBP currency using statistical ...
The solution of nonparametric regression problems is addressed via polynomial approximators and one-...
Kernel smoothing techniques free the traditional parametric estimators of volatility from the constr...
The problem of automatic bandwidth selection in nonparametric regression is considered when a local ...
In this paper we focus on nonparametric analysis of the volatility function for mixing processes. Ou...
This master thesis aims at estimating state price densities (SPD) via a nonparametric fit of the imp...
Many researchers are interesting in applying the neural networks methods to financial data. In fact ...
This paper is concerned with approximating nonlinear time series by an artificial neural network bas...
An autoregressive-ARCH model with possible exogeneous variables is treated. We estimate the conditio...
We consider a vector conditional heteroskedastic autoregressive nonlinear (CHARN) model in which bot...
Time series analysis and prediction are major scientific challenges that find their applications in ...