Nicola Spagnolo

Global and Regional Spillovers in Emerging Stock Markets: A Multivariate Garch-in-Mean Analysis

This paper examines global (mature market) and regional (emerging market) spillovers in local emerging stock markets. Tri-variate VAR-GARCH(1,1)-in-mean models are estimated for 41 emerging market economies (EMEs) in Asia, Europe, Latin America, and the Middle East. The models capture a range of possible transmission channels: spillovers in mean returns, volatility, and cross-market GARCH-in-mean effects. Hypotheses about the importance of different channels are tested. The results suggest that spillovers from regional and global markets are present in the vast majority of EMEs. However, the nature of cross-market linkages varies across countries and regions. While spillovers in mean returns dominate in emerging Asia and Latin America, spillovers in variance appear to play a key role in emerging Europe. There is also some evidence of cross-market GARCH-in-mean effects. The relative importance of regional and global spillovers varies too, with global spillovers dominating in Asia, and regional spillovers in Latin America and the Middle East.

ON THE DETERMINATION OF THE NUMBER OF REGIMES IN MARKOV‐SWITCHING AUTOREGRESSIVE MODELS

Dynamic models with parameters that are allowed to depend on the state of a hidden Markov chain have become a popular tool for modelling time series subject to changes in regime. An important question that arises in applications involving such models is how to determine the number of states required for the model to be an adequate characterization of the observed data. In this paper, we investigate the properties of alternative procedures that can be used to determine the state dimension of a Markov-switching autoregressive model. These include procedures that exploit the ARMA representation which Markov-switching processes admit, as well as procedures that are based on optimization of complexity-penalized likelihood measures. Our Monte Carlo analysis reveals that such procedures estimate the state dimension correctly, provided that the parameter changes are not too small and the hidden Markov chain is fairly persistent. The use of the various methods is also illustrated by means of empirical examples. Copyright 2003 Blackwell Publishing Ltd.

Volatility transmission and financial crises

In this paper we examine the international transmission of the 1997 South East Asia financial crisis. We estimate a bivariate GARCH-BEKK model, and carry out LR tests for causality-in-variance with bootstrapped critical values. Three pairwise models are estimated for US, European, Japanese and South East Asian daily stock market returns. Volatility spillovers are found in all cases. The dynamics of the conditional volatilities differ, but causality links in the variance are found to be strong and bidirectional in normal periods, and unidirectional (from the markets in turmoil to the others) following the onset of the crisis, consistently with crisiscontingent models.

Stock Market Integration between Three CEECs, Russia and the UK

This paper estimates a tri-variate VAR-GARCH(1,1)-in-mean model to examine linkages between the stock markets of three Central and Eastern European countries (CEECs), specifically the Czech Republic, Hungary, and Poland, and both the UK and Russia. The adopted framework allows to analyse interdependence by estimating volatility spillovers, and also contagion by testing for possible shifts in the transmission of volatility following the introduction of the euro and EU accession. Further evidence on possible changes in the transmission mechanism (namely, on whether there is contagion) can be obtained by examining the conditional correlations implied by the estimated model over different time periods. The empirical findings suggest that there is significant co-movement (interdependence) of these CEEC markets with both the Russian and the UK ones. Furthermore, whilst the introduction of the euro has had mixed effects, EU accession has resulted in an increase in volatility spillovers between the three CEECs considered and the UK (contagion).

A test for volatility spillovers

This paper proposes a new procedure for analyzing volatility links between different markets based on a bivariate Markov switching model. An empirical application of this procedure to three emerging markets is examined and discussed.

Liquidity Risk, Credit Risk and the Overnight Interest Rate Spread: A Stochastic Volatility Modelling Approach

In this paper we model the volatility of the spread between the overnight interest rate and the central bank policy rate (the policy spread) for the euro area and the UK during the two main phases of the financial crisis that began in late 2007. During the crisis, the policy spread exhibited signs of volatility, owing to the breakdown in interbank market activity. The determinants of this volatility are assessed using Stochastic Volatility models to gauge the role played by liquidity risk, credit risk (financial and sovereign), and interest rate expectations. Our results suggest that liquidity risk is the main determinant of the volatility of the policy spread, but also that private bank credit risk has become more apparent in the post-Lehman collapse phase of the crisis for the euro area as financial CDS premia rose due to possible default fears. In addition, the ECB appears to have been more effective in addressing liquidity risk since the onset of the crisis, and this may be related to its greater direct access to a broader range of counterparties and its acceptance of a broader range of eligible collateral. The main implication is that, in crisis times, a sufficiently flexible operational framework for monetary policy implementation produces the most timely response to market tensions.

Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching

This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov-switching parameters. A model selection procedure is proposed which is based on optimization of complexity-penalized likelihood criteria. The efficacy of the procedure is evaluated by means of Monte Carlo experiments. Copyright 2006 The Authors Journal compilation 2006 Blackwell Publishing Ltd.

Macro News and Commodity Returns

This paper adopts a VAR-GARCH approach to model the dynamic linkages between both the mean and the variance of macro news and commodity returns (Gold, Corn, Wheat, Soybeans, Silver, Platinum, Palladium, Copper, Aluminium and Crude Oil) over the period 01/01/2001-26/09/2014. The chosen specification also controls for the effect of the exchange rate. The results can be summarised as follows. Mean spillovers running from news to commodity returns are positive with the exception of Gold and Silver. Volatility spillovers are bigger in size and affect most commodity returns. Both first and second moment linkages are stronger in the post-September 2008 period. Overall, our findings confirm that commodities, despite not being financial assets, are sensitive to macro news (especially their volatility), and also suggest that the global financial crisis has strengthened such linkages.

Volatility Spillovers and Contagion from Mature to Emerging Stock Markets: Volatility Spillovers From Mature Stock Markets

This paper examines volatility spillovers from mature to emerging stock markets and tests for changes in the transmission mechanism-contagion-during turbulences in mature markets. Tri-variate GARCH-BEKK models of returns in global (mature), regional, and local markets are estimated for 41 emerging market economies (EMEs), with a dummy capturing parameter shifts during turbulent episodes. LR tests suggest that mature markets influence conditional variances in many emerging markets. Moreover, spillover parameters change during turbulent episodes. Conditional variances in most EMEs rise during these episodes, but there is only limited evidence of shifts in conditional correlations between mature and emerging markets.

Modelling East Asian exchange rates: a Markov-switching approach

This paper compares the ability of nonlinear and standard linear models to capture the dynamics of foreign exchanges rates in the presence of structural breaks. The analysis is conducted for three East Asian countries, namely Indonesia, South Korea and Thailand. It is shown that a Markov regime-switching model with shifts in the mean and variance (rather than a STAR model) is well suited to capture the nonlinearities in exchange rates. Such a model is found to outperform a random walk specification in terms of both in-sample fitting and out-of-sample forecasting. In order to evaluate competing forecasts, accuracy measures based on both the forecast errors and the variance forecast are used.