Extreme contagion in emerging stock markets using extreme value copulas

This paper uses multivariate extreme value theory to model the tail dependence as a measure of extreme contagion in emerging stock markets. Using the extreme value copulas, we determine the joint probabilities of simultaneous crashes between two emerging markets. The study is performed on a number of East-Asian emerging markets that have lived the well-known Asian flu. The results revealed the existence of a high contagion of crisis in the Asian emerging markets with a continuous high risk dependence structure in the aftermath of the Asian flu

A market risk model for asymmetric distributed series of return

In this paper we propose to model short-term interest rates by taking into consideration both the asymmetric properties of returns, using Pearson’s type IV distribution, and the time-varying volatility, using GARCH models. We show that conditional skewness is negatively related to spot price interest rates and that negative conditional skewness can lead the process to generate steady returns

Volatility spillovers among the Gulf Arab emerging markets

This paper examines the volatility spillovers among Gulf Arab emerging markets. Multivariate VAR-GARCH model of daily returns, with BEKK specification based on the conditional variances and conditional correlations, is estimated for all six GCC equity markets of Saudi Arabia, Kuwait, UAE, Qatar, Oman, and Bahrain. The results show high own-volatility spillovers and a high degree of own-volatility persistence in all GCC markets. Moreover, there are significant cross-volatility spillovers and cross-volatitlity persistence among all GCC equity markets, with stronger evidence from all GCC markets to the Saudi market. Such evidence could be explained by the existence of uncertainties surrounding various Gulf bank exposures to certain Saudi business groups as well as the downward movement of oil prices

Volatility spillovers among the Gulf Arab emerging markets

This paper examines the volatility spillovers among Gulf Arab emerging markets. Multivariate VAR-GARCH model of daily returns, with BEKK specification based on the conditional variances and conditional correlations, is estimated for all six GCC equity markets of Saudi Arabia, Kuwait, UAE, Qatar, Oman, and Bahrain. The results show high own-volatility spillovers and a high degree of own-volatility persistence in all GCC markets. Moreover, there are significant cross-volatility spillovers and cross-volatitlity persistence among all GCC equity markets, with stronger evidence from all GCC markets to the Saudi market. Such evidence could be explained by the existence of uncertainties surrounding various Gulf bank exposures to certain Saudi business groups as well as the downward movement of oil prices

Amerikan doları - Alman markı döviz kuru getirilerinin değişik zaman aralıklarında incelenmesi ve bunların risk

Cataloged from PDF version of article.Thesis (Ph.D.): Bilkent University, Department of Business Administration , İhsan Doğramacı Bilkent University, 2017.Includes bibliographical references (leaves 131-136).by Ramzi Nekhili.Ph.D

Volatility spillovers and price interdependencies; a dynamic non parametric approach

This paper investigates the volatility spillovers of four major equity markets using a new approach namely, the Filtered Historical Simulation approach (FHS). The FHS captures very effectively the changes and interactions in the first and second moments. A dynamic system based on Filtered Historical Simulation (FHS) and nonparametric regression is used to obtain estimates of the variance-covariance of the set of standardised residuals. This system is then used to examine dependencies in covariance changes and to carry an impulse response analysis to investigate the dynamic responses to volatility shocks

Jump Driven Risk Model Performance in Cryptocurrency Market

This paper aims at identifying a validated risk model for the cryptocurrency market. We propose a stochastic volatility model with co-jumps in return and volatility (SVCJ) to highlight the role of jumps in returns and volatility in affecting Value-at-Risk (VaR) and Expected Shortfall (ES) in cryptocurrency market. Validation results based on backtesting show that SVCJ model is superior in terms of statistical accuracy of VaR and ES estimates, compared to alternative models such as TGARCH (Threshold GARCH) volatility and RiskMetrics models. The results imply that for the cryptocurrency market, the best performing model is a stochastic process that accounts for both jumps in returns and volatility