How Financial Crises Affect FDI and Related Macroeconomic Variables: AMOS Analysis for Selected Mediterranean Countries

How Financial Crises Affect FDI and Related Macroeconomic Variables: AMOS Analysis for Selected Mediterranean Countries

Rukiye Ceyda Üvez (Marmara University, Kadiköy, Turkey) and Asli Aybars (Marmara University, Kadiköy, Turkey)
Copyright: © 2013 |Pages: 10
DOI: 10.4018/ijsem.2013040102


The new literature on the benefits and costs of financial globalization has increased in recent years because of the massive negative effects of the global financial crisis of 2008. While evidence based on microeconomic data shows some benefit of financial integration and the distortionary effects of capital controls, the macroeconomic evidence generally remains inconclusive. Also, some papers argue that financial globalization enhances macroeconomic stability especially in emerging economies, but others argue the opposite. The authors try to argue the effects of financial globalization and global financial crises on macroeconomic stability. This paper probes the effect of the financial crises since 2000 on specific Mediterranean economies including Greece and Turkey, comparatively. The main question to be answered in this paper is how selected macroeconomic variables affected the financial crises in these economies. Another question to be addressed is how macroeconomic variables have been affected by the last global financial crisis of 2008. Moreover, the research is focused on FDI (foreign direct investment) in these selected economies and examines the relationship between the macroeconomic variables and the financial crises. The relationship between relative macroeconomic variables, FDI and financial crises is determined using annual data from 2000 to 2010. The relationships between the indicators are analyzed using AMOS (Analysis of Moment Structures), a structural equation modeling (SEM) software where the model is presented in an intuitive path diagram to show hypothesized relationships among variables easier than just using standard multivariate statistics or multiple regression models alone.
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1. Introduction

Due to the recurring nature of banking and currency crises during the last decade, recent academic studies have focused extensively on the interrelationship among macroeconomic fundamentals and chosen indicators of financial crises. Various forms of the exchange market pressure index (EMPI), which can also be referred to as financial pressure index (FPI), has mainly been utilized as a proxy for periods of financial turmoil (Sachs et al., 1996, Pentecost et al., 2000, Baig et al., 2003, Feridun, 2009). Kahraman et al., (2009) construct a currency crises index to be used as proxy for currency crises on a panel dataset of 15 emerging economies during the period of 1987-2007. Domestic credit provided by the banking sector as a percentage of GDP, growth in M2 and the ratio of current account balance to reserves have been identified as the significant determinants of currency crises. Katırcıoğlu and Feridun (2011) focus on the long run relationship between selected macroeconomic variables and EMPI and define fiscal and current account balances, domestic credit to GDP ratio, excess real M1 balances and reserves to Granger cause EMPI. Another study, which evaluates the relationship of selected emerging market crises during the 1990s with the movements in asset prices as measured by stock returns and macroeconomic fundamentals, finds all crises to be linked to effective depreciations (Aguiar & Broner, 2006).

Another array of literature regarding financial crises and swings in macroeconomic indicators focuses on developing early warning systems (EWS) to mitigate the devastating consequences of those crises on the economy. Some of the seminal papers focused on constructing EWS can be named as those of Kaminsky et al. (1998), Berg and Pattillo (1999a, 1999b). A recent research, which is applied to a dataset of 12 countries during the period between 1980 and 2010, develops a model free methodology to assess the alternative EWS models developed for any kind of crises and find yield spread to be a significant determinant of currency crises specifically for South-Asian countries (Candelon et al., 2012). Kim et al. (2009) uses the stability-oriented approach to generate a stock market stability index by a three step methodology on the Korean stock market. As an initial step, they determine the variables to be used to develop the index and define the past stable period. Then, they establish a model that fits the selected variables and, finally, they determine the discrepancies between the observed and expected values. They argue their model to be more advanced than the conventional ones that aim to determine similarities between the conditions of the crises markets and those of current markets under consideration. Prominent analyses that investigate the Turkish economy to construct EWS can be considered as those of Tosuner (2005), Öztürk (2008), Çeşmeci and Önder (2008), Kiliclar and Taş (2009). Çeşmeci and Önder (2008) utilize three different methodologies, namely Signaling Method, Structural Model and Markov Switching Model, to evaluate the determinants of Turkish currency crises during the 1992-2004 periods. The results of their analyses show the significant impact of government debt and budget balance to GDP ratio on crises in Turkey. While the study of Öztürk (2008) successfully forecasted the recent global financial crises in 2008 utilizing FPI as a parameter for financial crises with an estimation period between January 1998 and November 2006, that of Kılıçlar and Taş (2009) provided warnings 3 months before the 1994, 1999 and 2001 crises using a dataset relating to the period between 1994 and 2006.

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