The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a...

We study training and generalization for multi-variate time series processing. It is suggested to used a quasi maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead to improved generalization performance. Further, we extend the Optimal Brain Damage pruning technique to the m...

It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold autoregression with GARCH errors, threshold GARCH and Double threshold heteroskedastic model with auxiliar...

We study training and generalization for multi-variate time series processing. It is suggested to used a quasi maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead to improved generalization performance. Further, we extend the Optimal Brain Damage pruning technique to the m...

A class of semiparametric fractional autoregressive GARCH models (SEMIFARGARCH), which includes deterministic trends, difference stationarity and stationarity with shortand long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper discusses the model fitting, including an efficient algorithm and parameter estimation of GARCH error ...

This paper proposes a simple test for threshold nonlinearity in either the mean or volatility equation, or both, of a heteroskedastic time series. Our proposal adopts existing Bayesian Markov chain Monte Carlo methods to fit a general double threshold GARCH model, which may have an explosive regime, then forms posterior credible intervals on model parameters to detect and specify threshold nonl...

This paper attempts to model the behavior of 1-minute high frequency exchange rate data of 5 currencies : the Japanese Yen, the Australian Dollar, the Canadian Dollar, the Euro, the Pound sterling against the US Dollar, on 21 July 2005 when the Chinese Yuan was revaluated. The data shows the following distinctive features: (1) There is a large jump at the time of the Yuan revaluation, (2) Large...

The classification of software systems into types has been achieved in the past by observing both their specifications and behavioral patterns: the SPE classification, for instance, and its further supplements and refinements, has identified the S-type (i.e., fully specified), the P-type (i.e., specified but dependent on the context) and the E-type (i.e., addressing evolving problems) among the...

this paper investigates the nature of volatility characteristics of stock returns in the bangladesh stock markets employing daily all share price index return data of dhaka stock exchange (dse) and chittagong stock exchange (cse) from 02 january 1993 to 27 january 2013 and 01 january 2004 to 20 august 2015 respectively.  furthermore, the study explores the adequate volatility model for the stoc...

Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news. As a result, we assume that European stock market returns are affected by endogenous and exogenous ...