Testing the Asymmetric Effect in Stock Returns from Warsaw Stock Exchange
Among many approaches to volatility modeling, so-called "asymmetric" models of volatility were proposed. These models are based on a notion that in some cases there may be the need to distinguish between "good news" and "bad news", because of possibly different impact on predictable volatility. One reason for this may be explained by so-called 'leverage' effect: if there is bad news, which decreases the asset price, this in turn decreases the equity value of the firm and so increases the debt-to-equity ratio. The raise in the ratio may in turn make the firm riskier (i.e. more leveraged) and expected variance of returns may increase in the future. One problem with plain ARCH (or GARCH) models of volatility is that they impose symmetric effects on predictable volatility, so good news and bad news have both the same impact on conditional variance. This assumption may be sometimes quite unrealistic. We can easily point out periods in which for example nervous reactions to bad news make stock prices more volatile. These reactions may be far stronger when compared with reactions to good news. It is thus relatively often observed that volatility is higher in a falling market. (fragment of text)
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