We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and ...

The paper investigates persistence, returns and volatility spillovers from the bitcoin market to gold silver markets using daily datasets January 2, 2018 July 31, 2020 by employing fractional persistence framework. results show strong price with posing highest while poses lowest persistence. of multivariate GARCH modelling, CCC-VARMA-GARCH model other lower variants indicate impossibility spill...

A new multilayer preceptor initialization method is proposed and compared experimentally with a traditional random initialization method. An operator maps training-set vectors into a two-variate space, inspects bi-variate training-set vectors and controls the complexity of the decision boundary. Simulations with sixteen real-world pattern classi®cation tasks have shown that in small-scale patte...

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...

A general-purpose and useful mobility model must be able to describe complex movement dynamics, correlate movement dynamics with the nodes geographic position and be sufficiently generic to map the characteristics of the movement dynamics to general geographic regions. Moreover, it should also be possible to infer the mobility model parameters from empirical data and predict the location of any...

In this paper, we introduce a two−dimensional Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for clutter modeling and anomaly detection. The one−dimensional GARCH model is widely used for modeling financial time series. Extending the one−dimensional GARCH model into two dimensions yields a novel clutter model which is capable of taking into account important characteris...

This paper develops a closed-form option pricing formula for a spot asset whose variance follows a GARCH process. The model allows for correlation between returns of the spot asset and variance and also admits multiple lags in the dynamics of the GARCH process. The single-factor (one-lag) version of this model contains Heston’s (1993) stochastic volatility model as a diffusion limit and therefo...

Knowledge of traditional medicine is important for the formation a person's activity in using medicine. Preliminary studies conducted show level public knowledge about less category. This can be caused by lack desire to seek information and education from health workers. For this reason, educational media such as e-booklets are needed. study aims see after being given an e-booklet. uses preexpe...

In the light of regime switching and volatility clustering in the dynamics of SHIBOR, regime-switching CIR model (RSCIR) and regime-switching GARCH CIR model (RSCIR-GARCH) are established by introducing regime-switching and GARCH specifications into CIR model successively. Then, a contrast study among CIR, RSCIR and RSCIR-GARCH models is performed based on SHIBOR sample data, which indicates th...

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the dependent extremes may not necessarily be in the domain of attraction of the classical generalised ex...