In this paper we propose a sequential Monte Carlo algorithm to esti- mate a stochastic volatility... more In this paper we propose a sequential Monte Carlo algorithm to esti- mate a stochastic volatility model with leverage efiects and non constant conditional mean and jumps. We are interested in estimating the time invariant parameters and the non-observable dynamics involved in the model. Our idea relies on the auxiliary particle fllter algorithm mixed to- gether with Markov Chain Monte
... as will be seen in Section 4. It should also be added that, to simplify the exposition, we sh... more ... as will be seen in Section 4. It should also be added that, to simplify the exposition, we shall al-ways refer to the final values as true values-ie, as observations free of measurement error.) If, for expository convenience, we restrict ourselves to a simplified situation in which the ...
In the present paper we compare some stochastic volatility models recently pro- posed in financia... more In the present paper we compare some stochastic volatility models recently pro- posed in financial literature by using a Bayesian criterion. The models considered for this anal- ysis have been estimated through an adaptive Markov chain Monte Carlo procedure, while the marginal likelihood necessary to evaluate the Bayes Factor is computed by using an auxiliary
In this paper we present a technique to obtain prediction intervals for chaotic time series. In t... more In this paper we present a technique to obtain prediction intervals for chaotic time series. In the context of the nearest-neigbour’s method we give an estimate of the local variance and of the percentiles of the prediction error-distribution. This makes it possible to define variable lower and upper bounds which contain a future value with a given probability. This method does not require any distributional assumption on the prediction error. Its effectiveness is shown with (clean and noisy) data generated by well-known chaotic maps and with real data. In the latter case, a hydrological time series and data generated by an electrical circuit are considered as these series are known to have chaotic features.
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility m... more In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effect. Our idea relies on the auxiliary particle filter method that allows to sequentially evaluate the parameters and the latent processes involved in the dynamic. An empirical application on simulated data is presented to study some empirical properties of the algorithm implemented.
Stochastic-variance models are important in describing and forecasting time-varying volatilities ... more Stochastic-variance models are important in describing and forecasting time-varying volatilities of financial time series. The introduction of jump components, in both the returns and the volatility process, improves the fit to the data. The goal of this paper is to examine the effectiveness of Markov Chain Monte Carlo methods in making inferences on different stochastic volatility models. We consider models of the affine-jump diffusion family and the log-variance specification popular in the econometric literature. We conduct inference within various stochastic volatility models, eventually with jumps, using an efficient adaptive Markov-chain Monte-Carlo procedure, thus generalizing solutions previously proposed in the literature. This methodology effects a sensible reduction in the autocorrelation observed in the Markov chain generated by the volatility-process updating scheme. To rank the competing models, we use the Bayes factor. Because there are many latent components (volatil...
... Fany Nan1, Silvano Bordignon1, Derek W. Bunn2, Francesco Lisi1 1 Department of Statistical Sc... more ... Fany Nan1, Silvano Bordignon1, Derek W. Bunn2, Francesco Lisi1 1 Department of Statistical Sciences, University of Padua, Via Cesare Battisti ... Power prices often show heteroskedasticity (Guirguis and Felder, 2004; Garcia et al., 2005; Knittel and Roberts, 2005; Misiorek et al ...
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility m... more In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage eect, non constant conditional mean and jumps. Our idea relies on the auxiliary particle lter algorithm together with the Markov Chain Monte Carlo (MCMC) method- ology. Our method allows to sequentially evaluate the parameters and the latent processes involved in the dynamic of interest. An empirical applica- tion on simulated data and on the Standard & Poor's 500 index is presented to study the performance of the algorithm implemented.
In this work we consider the problem of deriving the mean square prediction error of ARFIMA(p, d,... more In this work we consider the problem of deriving the mean square prediction error of ARFIMA(p, d, q) processes. In particular we obtain the (asymptotic) mean square prediction error when the parameters of the process are either known or estimated in the cases both of correct and misspecified model. Some Monte Carlo experiments confirm the validity of the asymptotic results.
ABSTRACT Process capability indices (PCIs) have been widely used in manufacturing industries to p... more ABSTRACT Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement errors effects on the performance of PCIs. The analysis is illustrated with reference to Cp, i.e. the simplest and most common measure suggested to evaluate process capability.
Abstract One of the main concerns in air pollution is excessive tropospheric ozone concentration.... more Abstract One of the main concerns in air pollution is excessive tropospheric ozone concentration. The aim of this work is to develop statistical models giving shortterm forecasts of future ground-level ozone concentrations. Since there are few physical insights about ...
... as will be seen in Section 4. It should also be added that, to simplify the exposition, we sh... more ... as will be seen in Section 4. It should also be added that, to simplify the exposition, we shall al-ways refer to the final values as true values-ie, as observations free of measurement error.) If, for expository convenience, we restrict ourselves to a simplified situation in which the ...
In this work we present a technique to obtain prediction intervals for chaotic data. Using neares... more In this work we present a technique to obtain prediction intervals for chaotic data. Using nearest neighbors method we give estimates of local variance and percentiles of the prediction error distribution. This allows to define an interval containing a future value with ...
In this paper we propose a sequential Monte Carlo algorithm to esti- mate a stochastic volatility... more In this paper we propose a sequential Monte Carlo algorithm to esti- mate a stochastic volatility model with leverage efiects and non constant conditional mean and jumps. We are interested in estimating the time invariant parameters and the non-observable dynamics involved in the model. Our idea relies on the auxiliary particle fllter algorithm mixed to- gether with Markov Chain Monte
... as will be seen in Section 4. It should also be added that, to simplify the exposition, we sh... more ... as will be seen in Section 4. It should also be added that, to simplify the exposition, we shall al-ways refer to the final values as true values-ie, as observations free of measurement error.) If, for expository convenience, we restrict ourselves to a simplified situation in which the ...
In the present paper we compare some stochastic volatility models recently pro- posed in financia... more In the present paper we compare some stochastic volatility models recently pro- posed in financial literature by using a Bayesian criterion. The models considered for this anal- ysis have been estimated through an adaptive Markov chain Monte Carlo procedure, while the marginal likelihood necessary to evaluate the Bayes Factor is computed by using an auxiliary
In this paper we present a technique to obtain prediction intervals for chaotic time series. In t... more In this paper we present a technique to obtain prediction intervals for chaotic time series. In the context of the nearest-neigbour’s method we give an estimate of the local variance and of the percentiles of the prediction error-distribution. This makes it possible to define variable lower and upper bounds which contain a future value with a given probability. This method does not require any distributional assumption on the prediction error. Its effectiveness is shown with (clean and noisy) data generated by well-known chaotic maps and with real data. In the latter case, a hydrological time series and data generated by an electrical circuit are considered as these series are known to have chaotic features.
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility m... more In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effect. Our idea relies on the auxiliary particle filter method that allows to sequentially evaluate the parameters and the latent processes involved in the dynamic. An empirical application on simulated data is presented to study some empirical properties of the algorithm implemented.
Stochastic-variance models are important in describing and forecasting time-varying volatilities ... more Stochastic-variance models are important in describing and forecasting time-varying volatilities of financial time series. The introduction of jump components, in both the returns and the volatility process, improves the fit to the data. The goal of this paper is to examine the effectiveness of Markov Chain Monte Carlo methods in making inferences on different stochastic volatility models. We consider models of the affine-jump diffusion family and the log-variance specification popular in the econometric literature. We conduct inference within various stochastic volatility models, eventually with jumps, using an efficient adaptive Markov-chain Monte-Carlo procedure, thus generalizing solutions previously proposed in the literature. This methodology effects a sensible reduction in the autocorrelation observed in the Markov chain generated by the volatility-process updating scheme. To rank the competing models, we use the Bayes factor. Because there are many latent components (volatil...
... Fany Nan1, Silvano Bordignon1, Derek W. Bunn2, Francesco Lisi1 1 Department of Statistical Sc... more ... Fany Nan1, Silvano Bordignon1, Derek W. Bunn2, Francesco Lisi1 1 Department of Statistical Sciences, University of Padua, Via Cesare Battisti ... Power prices often show heteroskedasticity (Guirguis and Felder, 2004; Garcia et al., 2005; Knittel and Roberts, 2005; Misiorek et al ...
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility m... more In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage eect, non constant conditional mean and jumps. Our idea relies on the auxiliary particle lter algorithm together with the Markov Chain Monte Carlo (MCMC) method- ology. Our method allows to sequentially evaluate the parameters and the latent processes involved in the dynamic of interest. An empirical applica- tion on simulated data and on the Standard & Poor's 500 index is presented to study the performance of the algorithm implemented.
In this work we consider the problem of deriving the mean square prediction error of ARFIMA(p, d,... more In this work we consider the problem of deriving the mean square prediction error of ARFIMA(p, d, q) processes. In particular we obtain the (asymptotic) mean square prediction error when the parameters of the process are either known or estimated in the cases both of correct and misspecified model. Some Monte Carlo experiments confirm the validity of the asymptotic results.
ABSTRACT Process capability indices (PCIs) have been widely used in manufacturing industries to p... more ABSTRACT Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement errors effects on the performance of PCIs. The analysis is illustrated with reference to Cp, i.e. the simplest and most common measure suggested to evaluate process capability.
Abstract One of the main concerns in air pollution is excessive tropospheric ozone concentration.... more Abstract One of the main concerns in air pollution is excessive tropospheric ozone concentration. The aim of this work is to develop statistical models giving shortterm forecasts of future ground-level ozone concentrations. Since there are few physical insights about ...
... as will be seen in Section 4. It should also be added that, to simplify the exposition, we sh... more ... as will be seen in Section 4. It should also be added that, to simplify the exposition, we shall al-ways refer to the final values as true values-ie, as observations free of measurement error.) If, for expository convenience, we restrict ourselves to a simplified situation in which the ...
In this work we present a technique to obtain prediction intervals for chaotic data. Using neares... more In this work we present a technique to obtain prediction intervals for chaotic data. Using nearest neighbors method we give estimates of local variance and percentiles of the prediction error distribution. This allows to define an interval containing a future value with ...
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