Volatility Spillover and Multivariate Volatility Impulse Response Analysis of GFC News Events

This paper applies two measures to assess spillovers across markets: the Diebold Yilmaz (2012) Spillover Index and the Hafner and Herwartz (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility estimates taken from the Oxford Man RV library, running from the beginning of 2000 to October 2016, for the S&P500 and the FTSE, plus ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index, from 3 January 2005 to 31 January 2015. Both data sets capture both the Global Financial Crisis (GFC) and the subsequent European Sovereign Debt Crisis (ESDC). The spillover index captures the transmission of volatility to and from markets, plus net spillovers. The key difference between the measures is that the spillover index captures an average of spillovers over a period, whilst volatility impulse responses (VIRF) have to be calibrated to conditional volatility estimated at a particular point in time. The VIRF provide information about the impact of independent shocks on volatility. In the latter analysis, we explore the impact of three different shocks, the onset of the GFC, which we date as 9 August 2007 (GFC1). It took a year for the financial crisis to come to a head, but it did so on 15 September 2008, (GFC2). The third shock is 9 May 2010. Our modelling includes leverage and asymmetric effects undertaken in the context of a multivariate GARCH model, which are then analysed using both BEKK and diagonal BEKK (DBEKK) models. A key result is that the impact of negative shocks is larger, in terms of the effects on variances and covariances, but shorter in duration, in this case a difference between three and six months.

The Spanish equity market (2000-2014): stylized facts

Introduction. This thesis is framed in the last 15 years of history of the Spanish equity market (years 2000 to 2014). In this sense and, as an introduction of this work, in the first chapter the main features of the background of the electronic market for shares and the implications that the migration from out-c1y circles to this automatic system is explained. The main changes of this electronic system (Spanish stock exchange interconnection system) are detailed in this part. Also in this first chapter is explained the important European meeting, in December 1999, of eight stock exchanges which foresee, in a first step, to design a single market model for, lately, try to merge, final step that did not take place. After this initial moment in December 1999, in this work the main features of the market model of the main European markets (London, Paris, Germany and Italy) are generally described, given that it is important to consider the European context of the Spanish equity market, specially during these last fifteen years. Along chapter two, the thesis is supported with the theoretic frame explaining here the nature of markets and their important role in the economy, detailing afte1wards the Spanish case from the point of view of its institutional structure and legal framework. Besides, in this chapter, a deep review of initial public offerings (main concepts and calendar steps) is done as well as take-over bids (typology and key-concepts)...

Volatility transmission between stock and exchange-rate markets: A connectedness analysis

This paper empirically investigates volatility transmission among stock and foreign exchange markets in seven major world economies during the period July 1988 to January 2015. To this end, we first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period (the system-wide approach) using a framework recently proposed by Diebold and Yilmaz (2014). Second, we make use of a dynamic analysis to evaluate the net directional connectedness for each market. To gain further insights, we examine the time-varying behaviour of net pair-wise directional connectedness during the financial turmoil periods experienced in the sample period Our results suggest that slightly more than half of the total variance of the forecast errors is explained by shocks across markets rather than by idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability.