Analysing the cross correlation between the four European markets in light of the Brexit situation

Abstract

The aim of this paper is to use the Dynamic Conditional Correlation (DCC) Multivariate Generalised Autoregressive Conditional Heteroscedasticity (MGARCH) to model the volatility spillover that exists between the four European market indices in Europe (i.e. the most frequently used benchmarks across Europe). Daily prices of the major European indices over the past 10 years are used, to calculate the daily returns of the following indices: FTSE, DAX, CAC and IBEX to analyse the regression that exists amongst the different markets, with the intention of computing the daily realised conditional correlations that exist between their returns. The study analyses the short and long term volatility transmission that exists between the four markets, with the aim of understanding and quantifying the impact of the conditional cross correlation during the past ten years (sample period). The paper investigates the time evolution of the dynamic cross correlation coefficients during financial instability and compares these results with periods of stability. For this particular investigation, this paper analyses the impact of the cross correlation coefficients during the subprime crisis in 2008 and how this varies during periods of stability, testing whether the arguments raised by Yarovaya et al (2015); that conditional cross correlation increases during financial instability hold to be true. In the financial world, to quantify the performance of any portfolio, one would need to take into consideration the risk taken by the investor in achieving such return. Understanding and identifying the risk accepted for mitigation of uncertainty is sometimes referred to as Risk Management; whereby its purpose is to solve and calculate the true value of VaR (Value at Risk). The VaR is derived by analysing the volatility of the returns of the assets and also by deriving the variance-covariance matrix (i.e. statistical derivation of potential co-movements in return of two different assets), which can be calculated and forecasted using MGARCH. There are various MGARCH techniques which can be used to calculate the variance-covariance matrix, namely: the VECH model, BEKK model, CCC MGARCH model and the DCC-MGARCH model amongst others. For this paper, the main focus will be to implement the DCC-MGARCH model in order to analyse and interpret the results of the dynamic conditional cross correlation that exist across the major European markets. Whenever an investor is building a portfolio, the probability of co-movements that exists between the stock prices needs to be taken into consideration since this is necessary for diversification (aimed to reduce VaR). Historical observations, illustrate how markets are financially interconnected through asset and liability management strategies of their sovereigns, financial institutions and corporations which was brought by financial globalization across the years Lane and Milesi-Ferretti (2007). Financial globalisation can be defined as institutions finding new financial opportunities with the aim to trade goods and services into new horizons. A perfect example of what globalization can lead to is the creation of the European Union (EU). Through globalisation, EU countries benefit from easier access to new markets across the European continent, while also gaining access to new sources of finance and technology. Globalisation provides numerous benefits for countries across the globe, yet, it also creates vulnerabilities across the financial markets such as: new external competition (offering same products and services) and over-interdependencies between countries and markets. During the past twelve month, the European Union (EU) has experienced a threat to its financial situation, where 51.9% of the British people voted for the UK to Leave Europe (CNBC, 2016). The UK during the past years has consolidated its position as one of the strongest financial countries in the EU and this can be illustrated with the strong reliability of the FTSE. The Financial Times debated the effect that Brexit would have on France, Germany and the rest of Europe and whether the interconnectedness that exists across the globalised financial world, especially between the Eurozone markets can be effected by such decision taken by the UK to leave the EU (Financial Times, 2016). Upon implementing Article 50 (on the 29 March 2017) the behaviour of the UK index (FTSE) was relatively volatile, yet the British index managed to achieve an all-time high, while most of the European markets dropped. Brexit has directed much interest across Europe, especially amongst investors, shareholders, stakeholders and researchers, who are becoming gradually more interested in understanding how the conceivable effects of Brexit might be perceived across the globe and which country/sector/market is highly likely to suffer/benefit the most. Brexit has been the centre of focus in the European media, who are concerned about the economic and financial effects of the repercussions that might arise out of the U.K.’s decision to leave the EU. This concern is mainly due to the high level of interdependencies that exists among major European countries across the financial world.