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...

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

t he relationship between the price of oil and the level of economic activity is a fundamental empirical issue in macroeconomics. in this research, by using a multivariate garch-in-mean var, we try to investigate direct effects of uncertainty of oil price on macroeconomics of iran by using annually data from 1965 to 2013.results show that uncertainty about oil prices had a negative and signific...

In this paper, we propose a new entropy-optimized bivariate empirical mode decomposition (BEMD)-based model for estimating portfolio value at risk (PVaR). It reveals and analyzes different components of the price fluctuation. These components are decomposed and distinguished by their different behavioral patterns and fluctuation range, by the BEMD model. The entropy theory has been introduced f...

This paper develops an approximate closed-form optimal portfolio allocation formula for a spot asset whose variance follows GARCH(1,1) process. We consider investor with constant relative risk aversion (CRRA) utility who wants to maximize the expected from terminal wealth under Heston and Nandi (2000) GARCH (HN-GARCH) model. Based on approximation of log returns Campbell Viceira (1999), we obta...

We provide a feasible generalized least squares estimator for (unrestricted) multivariate GARCH(1,1) models. We show that the estimator is consistent and asymptotically normally distributed under mild assumptions. Unlike the (quasi) maximum likelihood method, the feasible GLS is considerably fast to implement and does not require any complex optimization routine. We present numerical experiment...

Purpose The study's purpose is to investigate the price volatility of four dairy commodities (skim milk powder [SMP], whole [WMP], butter and cheddar cheese) in three most significant regional markets (EU, Oceania US) international market. Design/methodology/approach study uses a panel-Generalized Autoregressive Conditional Heteroskedastic (panel-GARCH) modeling technique data from January 12, ...

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowin...

Predicting the “Value at Risk” of a portfolio of stocks is of great significance in quantitative finance. We introduce a new class models, “dynamical products of experts” that treats the latent process over volatilities as an inverse Gamma process. We show that our multivariate volatility models significantly outperform all related Garch and stochastic volatility models which are in popular use...

This dissertation presents some new results in stationary multivariate time series. The asymptotic properties of the sample autocovariance are established, that is, we derive a multivariate version of Bartlett’s Classic Formula. The estimation of the autocovariance function plays a crucial role in time series analysis, in particular for the identification problem. Explicit formula for vector au...