New Measure Of Risk Research Articles

Measuring Downside Risk - Realised Semivariance

We propose a new measure of risk, based entirely on downward moves measured using high frequency data. Realised semivariances are shown to have important predictive qualities for future market volatility. The theory of these new measures is spelt out, drawing on some new results from probability theory.

Partial quantile regression

Partial least squares regression (PLSR) is a method of finding a reliable predictor of the response variable when there are more regressors than observations. It does so by eliciting a small number of components from the regressors that are inherently informative about the response. Quantile regression (QR) estimates the quantiles of the response distribution by regression functions of the covariates, and so gives a fuller description of the response than does the usual regression for the mean value of the response. We extend QR to partial quantile regression (PQR) when there are more regressors than observations. For each percentile the method provides a low dimensional approximation to the joint distribution of the covariates and response with a given coverage probability and which, under further linearity assumptions, estimates the corresponding quantile of the conditional distribution. The methodology parallels the procedure for PLSR using a quantile covariance that is appropriate for predicting a quantile rather than the usual covariance which is appropriate for predicting a mean value. The analysis suggests a new measure of risk associated with the quantile regressions. Examples are given that illustrate the methodology and the benefits accrued, based on simulated data and the analysis of spectrometer data.

Alternative measures to value at risk

PurposeThe purpose of this article is to introduce a new method of estimating risk as an alternative to value at risk (VaR), drawing on the risk assessment literature in environmental science.Design/methodology/approachA commonly used and accepted measure of market risk is VaR, defined as the difference between initial portfolio value and a probabilistic lower bound B on the portfolio value at time T. To take account of situations where the portfolio value may fall below B prior to time T, an an alternative to VaR is proposed based on first passage time distributions.FindingsIt is argued that the resulting expected minimum portfolio value over the time frame T provides a clear alternative measure of market risk. Analytical expressions are obtained and numerical comparisons given when the distribution of portfolio returns is lognormal.Research limitations/implicationsAnalytical results are presented for lognormal distributions for returns. Results for other models can be easily obtained from simulation.Practical implicationsThe new measure of risk recognizes that investors might be sensitive to risks of decline in the value of a portfolio at any time within a given time horizon, not just at the end of the anticipated period of investment. The expected minimum portfolio value measures the largest loss that is expected at some stage over that period.Originality/valueA new measure of risk is presented that arises from literature on risk assessment in environmental science. It is complementary to VaR for assessing risk.

Strategic Risk: An Ordinal Approach

Strategic management researchers have shown increasing awareness of the importance of the concept of risk at the strategic level. In recognition of this interest, this research adopts a commonly used conceptualization of risk and shows how this conceptualization leads to a new measure of risk, based upon the chance of loss of relative position within a set of firms. This framework is shown to be particularly appropriate for strategy research. Information contained in probability distributions describing the relative positions of sets of firms over time is shown to be partitionable on the basis of gain or loss in position as well as on firm identity, leading to the derivation of a new measure of strategic risk for an individual firm. This development also provides a hierarchical quantification of risk at the levels of the industry, group and individual firm. The ordinal risk measure is applied to both hypothetical data for illustration and to data on the U.S. airline industry.

A New Measure of Risk

A New Measure of Risk