CAN YOU CALCULATE VOLATILITY, R-SQUARED, SHARPE RATIO, ALPHA, and CVaR for STOCKS and ETFs?
Yes you can, and here are the definitions and calculations from Zoonova.
Volatility. Standard deviation is a statistical term that measures the amount of variability or dispersion around an average. Standard deviation is also a measure of volatility. Generally speaking, dispersion is the difference between the actual value and the average value. The square root of the variance equals the standard deviation. You can see the calculated Variance and Std Deviation for various stocks in the above image under Analytics.
R Squared. R-squared is a statistical measure that represents the percentage of a fund or security's movements that can be explained by movements in a benchmark index. For example, an R-squared for a fixed-income security versus the Barclays Aggregate Index identifies the security's proportion of variance that is predictable from the variance of the Barclays Aggregate Index. The same can be applied to an equity security versus the Standard and Poor's 500 or any other relevant index. R squared is measured from 0 – 1 or 0% - 100%. A level of 1, or 100%, means a perfect correlation to the benchmark. Calculated in the above screen image under analytics.
Sharpe Ratio. a measure that indicates the average return minus the risk-free return divided by the standard deviation of return on an investment. The Sharpe Ratio is a measure for calculating risk-adjusted return, and this ratio has become the industry standard for such calculations. The higher the Sharpe Ratio the better. Calculated and shown in the above image under Analytics.
Bmrk Alpha or Jensen Alpha. Alpha, often considered the active return on an investment, gauges the performance of an investment against a market index used as a benchmark since they are often considered to represent the market’s movement as a whole. The excess returns of a fund relative to the return of a benchmark index are the fund's alpha. ZOONOVA also calculates the Alpha against the CAPM Expected Return (Capital Asset Pricing Model). See image under Analytics.
CVaR. Conditional Value at Risk. CVAR. CVaR is also known as mean excess loss, mean shortfall, tail Var, average value at risk or expected shortfall. CVaR was created to serve as an extension of value at risk (VaR). The VaR model allows managers to limit the likelihood of incurring losses caused by certain types of risk, but not all risks. The problem with relying solely on the VaR model is that the scope of risk assessed is limited, since the tail end of the distribution of loss is not typically assessed. Therefore, if losses are incurred, the amount of the losses will be substantial in value. Zoonova calculates CVAR for both 95% and 99% confidence levels.
Attached are the calculations.
