Abstract
There has been much discussion in the literature about how central measures of equity risk such as standard deviation fail to account for extreme tail risk of equities. Similarly, parametric measures of value at risk (VaR) may also fail to account for extreme risk as they assume a normal distribution which is often not the case in practice. Nonparametric measures of extreme risk such as nonparametric VaR and conditional value at risk (CVaR) have often been found to overcome this problem by measuring actual tail risk without applying any predetermined assumptions. However, this article argues that it is not just the actual risk of equites that is important to investor choices, but also the relative (ordinal) risk of equities compared to each other. Using an applied setting of industry portfolios in a variety of Asian countries (benchmarked to the United States), over crisis and non-crisis periods, this article finds that nonparametric measures of VaR and CVaR may provide only limited new information to investors about relative risk in the portfolios examined as there is a high degree of similarity found in relative industry risk when using nonparametric metrics as compared to central or parametric measures such as standard deviation and parametric VaR.
Highlights
Background and RationaleThis study focuses on differences between parametric and nonparametric measures of relative industry risk among equities in Asia
The literature abounds with studies which show nonparametric extreme measures of risk, such as conditional value at risk (CVaR), to better account for tail risk than central measures such as standard deviation or parametric measures which assume a normal distribution such as parametric value at risk (VaR)
We generally found very strong association which held across time periods and countries. This means that whether using VaR or CVaR, we will arrive at similar conclusions about the relative risk of industries
Summary
Background and RationaleThis study focuses on differences between parametric and nonparametric measures of relative industry risk among equities in Asia. The literature abounds with studies which show nonparametric extreme measures of risk, such as conditional value at risk (CVaR), to better account for tail risk than central measures such as standard deviation or parametric measures which assume a normal distribution such as parametric value at risk (VaR). This is especially during crisis periods when there may be outliers in the tail of a distribution. These studies predominantly focus on the US or other developed regions, which tended to suffer extreme risk during the Global Financial Crisis (GFC). While actual measures of extreme risk such as CVaR can be very useful in understanding the extreme risk in the tail of a distribution, ordinal measures are useful in
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