# step by step calculation of the sharpe ratio

I am trying to calculate the Sharpe ratio. Suppose I have:

$$x_t = \alpha + \beta y_{t} + \epsilon_{t}$$

$$E[x_{t}] = \alpha + \beta E[y_{t}]$$

$$var[x_{t}] = \beta^2var[y_t] + \sigma^2$$

The Sharpe ratio is:

$$\dfrac{E[x_{t}]}{\sqrt{var[x_{t}]}}$$

I am trying to go from the above Sharpe ratio to the following output (but step-by-step showing everything I am doing):

$$\dfrac{\alpha + \beta E[y_{t}]}{\sqrt{\beta^2 var[y_t] + \sigma^2}}$$

Does anybody have some step-by-step solution? Or if there is somewhere online where I can see step-by-step how its calculated.

• The mystery is why $\sigma$ disappeared, the rest is a trivial substitution. – Alex C Nov 15 '19 at 22:05
• Added $\sigma$ to the model. – user8959427 Nov 15 '19 at 22:23
• is there a source about the Sharpe ratio that shows the formulas written above? – develarist Nov 15 '19 at 22:24
• Thanks. too bad the blog writer didn't give his sources since that is an interesting (but probably well-known) way of representing the Sharpe ratio as a factor regression model – develarist Nov 15 '19 at 22:54

The formulas given already explain the substitution you're wondering about, based on standard statistical laws surrounding the distribution of random variables, often focused on the first and second moments of that distribution: the mean $$E(\cdot)$$ and variance $$Var(\cdot)$$. You can find these rules being followed in the derivations of many economic models that are probabilistic.
Taking the expected value of random variable $$x_t$$ as $$E(x_t)$$ is simply due to the regression coefficient $$\beta$$ coming out as a scalar coefficient since it doesn't have an expected value being a scalar, whereas random variable $$y_t$$ is not deterministic and does have an expected value, therefore $$E(y_t)$$ is there. Intercept term $$\alpha$$ is intact when taking expectations, but not in the variance shown next.
• Variance of the scalar $$\beta$$ is the same scalar but squared, thus $$Var(\beta)=\beta^2$$ comes out in front of the random variable it is connected to
• Variance of a random variable is just as it is shown, $$Var(y_t)$$
• The $$\sigma^2$$ always comes out at the end as a residue of sorts from taking the variance of a random variable and intercept term $$\alpha$$ (having alot to do with the covariance of the model) again fully derived in the back of many econometrics textbooks.
Since the Sharpe ratio's numerator is the expected value of random variable $$x_t$$ and the denominator is the variance of the same random variable $$x_t$$ consisting of a deterministic and stochastic term, the $$E(x_t)$$ and $$Var(x_t)$$ are just put into place.