Robust Ranking of Multivariate GARCH Models by Problem Dimension

During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinking, using historical data for 89 US equities. We contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC and covariance shrinking models. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Model Confidence Set. Third, we examine how the robust model rankings are influenced by the cross-sectional dimension of the problem

Insights of energy and its trade networking impacts on sustainable economic development

Oil and gold are considered as the most vigor influencer for any economy. In this research volatility spillover networking among the oil (energy), gold, and Asian leading emerging stock markets are constructed by the coalescence of two approaches BEKK-GARCH and complex networking. The data consists of the daily return of sixteen Asian countries’ stock markets, an index of the Asia Pacific, and future prices of oil and gold contracts for the period of 01–01-2010 to 31-05-2020. It covers all the recent shocks of the current decade to study the impact of these crises. The data is further divided into four sub-periods as well for this research. The results of our total period detect that commodities of oil and gold receive more volatility spillover than transfer it to stock indices of Asia Pacific countries. Moreover, it is also observed that, as compared to oil, gold had more strong significant spillover linkages. Among all Asian economies, the Chinese stock markets had more influence on the price movement of oil and gold. Whereas, India had more significant correlations with other neighboring stock indices. The results of this research not only provide the facts about the interconnection of oil, gold, and Asian Pacific countries in the current scenarios but also give very useful directions for future researchers, investors, and hedgers, as well as for policymakers interested in the Asian region

Will the crisis "tear us apart"? Evidence from the EU

We examine the synchronisation of the European Union (EU) financial markets before and during the 2007 global financial crisis. We use an Asymmetric Dynamic Conditional Correlation (ADCC)-GARCH framework to control for the time-varying correlations and a Markov-Switching model to identify regime changes. Our sample considers 27 EU nations for the period 2000-2011. For each nation we formulate several characteristics of the crisis such as, synchronicity, duration and intensity measures. We find that the more recent EU members had a lagged entry to the crisis regime, were less adversely affected, show higher correlation between their stock markets and have their credit scores being revised more frequently relative to established EU members. We also find that higher levels of sovereign debt and lower levels of industrialisation positively impact crisis duration and intensity. Our results refute the notion of uniform integration of EU financial markets as evident from the highly non-synchronised observed crisis experience among the EU members. As such, one-size fits all policies are likely to be ineffective

Sectoral dynamics of financial contagion in Europe - The cases of the recent crises episodes

In this paper, we investigate the existence of financial contagion in the European Union during the recent Global Financial Crisis (GFC) of 2007-2009 and the European Sovereign Debt Crisis (ESDC) that started in 2009. Our sample includes sectorial equity indices for 15 countries from 2004 to 2014. We adopt an ADCC-GJR-GARCH model for the time-varying correlations and a Markov-Switching model to identify the lead/lag relationship in crisis transition dates across the countries and the sectors. We assess the patterns of financial contagion by sector and by country. Our results support the existence of financial contagion in all business sectors under the GFC and the ESDC. Financials and Telecommunications are the most affected, while the Industrials and the Consumer Goods the least in each crisis respectively. Stock markets in the Core EU are the most affected in both crises. We find evidence of a non-synchronized transition of all countries to the crisis regime, in both crises. We believe that our results may provide useful insights for investors and policy makers

A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies

This paper proposes a set of models which can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously to estimate also their credit risk using the Zero Price Probability (ZPP) model by Fantazzini et al (2008), which is a methodology to compute the probabilities of default using only market prices. For this purpose, both univariate and multivariate models with different specifications are employed. Two special cases of the ZPP with closed-form formulas in case of normally distributed errors are also developed using recent results from barrier option theory. A backtesting exercise using two datasets of 5 and 15 coins for market risk forecasting and a dataset of 42 coins for credit risk forecasting was performed. The Value-at-Risk and the Expected Shortfall for single coins and for an equally weighted portfolio were calculated and evaluated with several tests. The ZPP approach was used for the estimation of the probability of default/death of the single coins and compared to classical credit scoring models (logit and probit) and to a machine learning algorithm (Random Forest). Our results reveal the superiority of the t-copula/skewed-t GARCH model for market risk, and the ZPP-based models for credit risk

A tale of two perspectives: Australian risk management in theory and in practice

The objective of this thesis is to identify the gap between academic research in risk management and its implementation in practice. The thesis focuses on the application of Value-at-Risk (VaR) in managing market risk for Australian Authorised Deposit-taking Institutions (ADIs). It is found that the current regulation does not provide regulator enough information to assess the information content of VaR forecasts reported by ADIs and, therefore, its role to monitor risk-taking behaviours in ADI is limited

A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies

This paper proposes a set of models which can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously to estimate also their credit risk using the Zero Price Probability (ZPP) model by Fantazzini et al (2008), which is a methodology to compute the probabilities of default using only market prices. For this purpose, both univariate and multivariate models with different specifications are employed. Two special cases of the ZPP with closed-form formulas in case of normally distributed errors are also developed using recent results from barrier option theory. A backtesting exercise using two datasets of 5 and 15 coins for market risk forecasting and a dataset of 42 coins for credit risk forecasting was performed. The Value-at-Risk and the Expected Shortfall for single coins and for an equally weighted portfolio were calculated and evaluated with several tests. The ZPP approach was used for the estimation of the probability of default/death of the single coins and compared to classical credit scoring models (logit and probit) and to a machine learning algorithm (Random Forest). Our results reveal the superiority of the t-copula/skewed-t GARCH model for market risk, and the ZPP-based models for credit risk

Robust Ranking of Multivariate GARCH Models by Problem Dimension

During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinking, using historical data for 89 US equities. We contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC and covariance shrinking models. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Model Confidence Set. Third, we examine how the robust model rankings are influenced by the cross- sectional dimension of the problem

Robust ranking of multivariate GARCH models by problem dimension

Several Multivariate GARCH (MGARCH) models have been proposed, and recently such MGARCH specifications have been examined in terms of their out-of-sample forecasting performance. An empirical comparison of alternative MGARCH models is provided, which focuses on the BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH models, Exponentially Weighted Moving Average, and covariance shrinking, all fitted to historical data for 89 US equities. Notably, a wide range of models, including the recent cDCC model and the covariance shrinking method, are used. Several tests and approaches for direct and indirect model comparison, including the Model Confidence Set, are considered. Furthermore, the robustness of model rankings to the cross-sectional dimension of the problem is analyzed