In this paper we introduce a multivariate generalized autoregressive conditional heteroskedastic (GARCH) class of models with time-varying eigenvalues. The dynamics the eigenvalues is derived for cases underlying Gaussian and Student’s t-distributed innovations based on general theory dynamic score by Creal, Koopman Lucas (2013) Harvey (2013). resulting eigenvalue GARCH – labeled ‘?-GARCH’ diff...

This paper investigates whether currency risk is priced differently in the different sectors (industrial, financial, and basic materials) of equity markets a sample developed United States America (USA) developing economies (Brazil, India, Poland, South Africa). The makes use following techniques: (i) Univariate Autoregressive Fractionally Integrated Moving Average Exponential General Condition...

Models for heteroskedastic data are relevant in a variety of applications ranging from financial time series to environmental statistics. However, the topic modelling variance function conditionally has not seen as much attention mean. Volatility models have been used specific applications, but these can be difficult fit Bayesian setting due posterior distributions that challenging sample effic...

We consider multivariate stationary processes ( X t ) satisfying a stochastic recurrence equation of the form = ???? ? 1 + Q , where are i.i.d. random vectors and Diag b c M … d diagonal matrices (Mt) variables. obtain full characterization vector scaling regular variation properties ), proving that some coordinates Xt, i j asymptotically independent even though all rely on same input (Mt). pro...

The Covid-19 global pandemic has caused trouble for labour and financial markets worldwide, health crises resulted. This makes policy makers get confused. study is carried out with the aim of investigating impacts on both mean conditional volatility Vietnamese stock market returns, using a simple Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model, spanning period 01 January ...

In this paper I first define the regime-switching lognormal model. Monthly data from the Standard and Poor’s 500 and the Toronto Stock Exchange 300 indices are used to fit the model parameters, using maximum likelihood estimation. The fit of the regime-switching model to the data is compared with other common econometric models, including the generalized autoregressive conditionally heteroskeda...

We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common an...

In this paper we put forward a new method to estimate value at risk (VaR), autoregressive conditional heteroskedastic (ARCH) factor, which combines multivariate analysis with ARCH models. Firstly, from a set of correlated portfolio risk factors, we derive a smaller uncorrelated risk factors set, by applying multivariate analysis. Secondly, we use ARCH schemes to model uncorrelated factors histo...