# Wonder Bars Cost of Capital or Required Return

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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…
According to the three researchers, a more specific form of the non-linear model is given by the following equation: (2) where g is a function of past error terms, and σ is the variance term. Campbell, Lo and MacKinlay characterize models with non-linear g as being non-linear in mean and those with non-linear σ 2 as being non-linear in variance. Models can be linear in mean and variance (the classic regression model, ARMA models) or linear in mean, but non-linear in variance (GARCH models) (Brooks, 2010, pp. 380). The most commonly used financial models to measure volatility are the non-linear ARCH and GARCH models. 2 y t = f (e t , et −1 , et − 2 ,...) y t = g (et −1 , et −2 ,...) + et σ 2 (et −1 , et − 2 ,...) 2.1. The autoregressive conditional heteroscedasticity model (ARCH) One of the fundamental hypotheses of the classical regression model is the homoscedasticity or the hypothesis of constant error variance: var(et ) = σ 2 (et ) , where et ~ N (0, σ 2 ) . The opposite case is known as heteroscedasticity. In the case of financial time series it is unlikely that the variance of the errors will be constant over time and hence it is preferred to consider a model that does not assume constant variance and which can describe how the variance of the errors evolves. As we mentioned earlier another important feature of financial series is known as volatility clustering or volatility pooling. This characteristic shows that the current level of volatility tends to