Abstract This article aims to examine the transmission of return and volatility spillover from banking industry other industries in Pakistan. The study uses daily stock prices 2005 2018 financial non‐financial sectors listed at Pakistan exchange. KSE‐100 index is used as a basis for selection he companies. ARMA‐GARCH mean model measure spillover. time‐varying conditional correlation asymmetric ...

We estimate the risk spillover among European banks from equity log-return data via Conditional Value at Risk (CoVaR). The joint dynamic of returns is modeled with a spatial DCC-GARCH which allows conditional variance log-returns each bank to depend on past volatility shocks other and their squared in parsimonious way. backtesting resulting measures provides evidence that (i) multivariate GARCH...

This study aims to analyze the volatility spillover between bond and commodity markets in terms of global liquidity risk. The data covers daily closing prices specified countries - Brazil, Russia, India, China, Turkey certain commodities gold oil for period January 2008 2022. We utilized DCC-GARCH model these Copula DCC-GACRH determine dependence structures them. Additionally, we applied Hong C...

The study examines the dynamics between oil price, exchange rate, and stock market performance in South Africa using DCC-GARCH, time-varying VAR, multivariate Markov regime switching models. Monthly data on capitalization as a measure of from 2003(01) to 2019(7) were employed. results DCC-GARCH model show that dynamic conditional correlation among variable was stable with few exceptionalities. ...

Article history: Received 7 October 2014 Received in revised form 28 January 2015 Accepted 28 January 2015 Available online 7 February 2015 The objectives of this study are two-fold: i) to derive time-varying exchange rate pass-through (ERPT) degree and ii) investigate the macroeconomic determinants of the degree of ERPT. For this purpose, the study adopts a distinct methodology combining Dynam...

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our ne...

Motivated by recent developments in light of the sub-prime and subsequent financial crisis we fit two different vector autoregressive generalized conditional heteroscedastic (VAR-GARCH) models to three financial indices with the aim of understanding the development of dependency structures between credit spreads and other macroeconomic variables. Our analysis includes daily quotes from June 200...

This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and c...

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be esti...