Theoretical and Applied Economics Volume XVIII (2011), No. 2(555), pp. 75-88

Portfolio Risk Analysis using ARCH and GARCH Models in the Context of the Global Financial Crisis*
Oana Mădălina PREDESCU Bucharest Academy of Economic Studies predescu_oana85@yahoo.com Stelian STANCU Bucharest Academy of Economic Studies stelian_stancu@yahoo.com Abstract. This paper examines both the benefits of choosing an internationally diversified portfolio and the evolution of the portfolio risk in the context of the current global financial crisis. The portfolio is comprised of three benchmark indexes from Romania, UK and USA. Study results show that on the background of a global economic climate eroded strongly by the effects of the current financial …show more content…

Given that news can lead to various interpretations, and also given that specific economic events like an oil crisis can last for some time, we often observe that large positive and large negative observations in financial time series tend to appear in clusters” (Gujarati, 2004, p. 856). In practice, linear time series models are incapable to explain a number of important features common to financial data, such as: Leptokurtosis – the tendency for financial asset returns to have distributions that exhibit fat tails; Volatility clustering or volatility pooling – the tendency for volatility to appear in bunches on financial markets. Thus large returns (of either sign) are expected to follow large returns, and small returns (of either sign) to follow small returns. One of the explanations for this phenomenon, which seems to characterize financial return series, would be the fact that the information arrivals which drive price changes occur in bunches. Leverage effects – the tendency for volatility to rise more following a large price fall, rather than following a price rise of the same magnitude.

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Oana Mădălina Predescu, Stelian Stancu

Campbell, Lo and MacKinlay (1997) defined a non-linear data generating process as one where the current value is related non-linearly to current and previous values of the error term: (1)