Heteroskedastic

DEFINITION of Heteroskedastic

Heteroskedastic alludes to a condition wherein the variance of the residual term, or mistake term, in a regression model fluctuates widely. Assuming this is true, it might change in a systematic way, and there might be some factor that can make sense of this. Provided that this is true, then, at that point, the model might be ineffectively defined and ought to be modified so this systematic variance is made sense of by at least one extra predictor variables.

Something contrary to heteroskedastic is homoskedastic. Homoskedasticity alludes to a condition where the variance of the residual term is steady or almost so. Homoskedasticity (likewise spelled "homoscedasticity") is one assumption of linear regression modeling. Homoskedasticity proposes that the regression model might be obvious, implying that it gives a decent clarification of the performance of the dependent variable.

BREAKING DOWN Heteroskedastic

Heteroskedasticity is an important concept in regression modeling, and in the investment world, regression models are utilized to make sense of the performance of securities and investment portfolios. The most notable of these is the Capital Asset Pricing Model (CAPM), which makes sense of the performance of a stock in terms of its volatility relative to the market as a whole. Extensions of this model have added other predictor variables like size, momentum, quality, and style (value versus growth).

These predictor variables have been added in light of the fact that they make sense of or account for variance in the dependent variable, portfolio performance, then, at that point, is made sense of by CAPM. For instance, designers of the CAPM model knew that their model failed to make sense of an intriguing anomaly: excellent stocks, which were less unpredictable than low-quality stocks, would in general perform better than the CAPM model anticipated. CAPM says that higher-risk stocks ought to outperform lower-risk stocks. As such, high-volatility stocks ought to beat lower-volatility stocks. In any case, top notch stocks, which are less unstable, would in general perform better than anticipated by CAPM.

Afterward, different specialists extended the CAPM model (which had proactively been extended to incorporate other predictor variables like size, style, and momentum) to incorporate quality as an extra predictor variable, otherwise called a "factor." With this factor currently remembered for the model, the performance anomaly of low volatility stocks was accounted for. These models, known as multi-factor models, form the basis of factor investing and smart beta.