Portfolio Return Variance and Time-Varying Weights
Summary
The document explains how portfolio return variance relates to the covariance of constituent returns. For a portfolio holding fixed share counts, each period’s return is a weighted sum of the assets’ returns, with weights determined by each holding’s market value relative to the portfolio’s total value. The weights therefore change as prices change.
To estimate the covariance inputs, calculate each security’s period returns, accounting for distributions, then compute sample means and pairwise sample covariances across the observation period. These covariances can be combined with portfolio weights to describe return variance for a given set of weights. The answer stresses that return data must handle details such as distributions and delisting returns correctly. The discussion does not provide a worked numerical example or specify a forecasting model; historical sample covariance is an estimate, and portfolio weights must match the holdings and time period being analyzed.
Key ideas
- Portfolio returns are weighted sums of constituent returns, with weights based on each holding’s market value.
- Fixed share counts generally cause portfolio weights to change as security prices move.
- Estimate return covariance from paired observations after computing period returns and sample means.
- Return calculations should account for distributions and delisting returns.
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Full text
# Variance of returns on a portfolio
# Variance of returns on a portfolio
This must be very basic, but I don't seem to be able to express the variance of returns on a portfolio in terms of variances-covariance sum of returns of its constituents, which seems to be what is used everywhere. Say $Y(t)=\sum{n_i X_i(t)}$ where $n_i$ is the number of shares of stock $i$ with price $X_i$. Define a relative return on $i$-th constituent over a period $T$ as
$$r_i(t,t+T) = \frac{X_i(t+T)-X_i(t)}{X_i(t)}$$
Then the relative return on the portfolio is
$$R_T = R(t,t+T) = \frac{Y(t+T)-Y(t)}{Y(t)} = \frac{1}{Y(t)}\sum{n_i X_i(t) r_i(t,t+T)}$$
Then
$$Var(R_T) = Var \left( \frac{1}{Y(t)}\sum{n_i X_i(t) r_i(t,t+T)} \right) $$
This is often expressed as
$$ \sum w_iw_jCov(r_i,r_j)$$
but since both $X(t)$ and $Y(t)$ also vary with time I don't see how this is done.
## Answer by Matthew Gunn (score 2)
https://quant.stackexchange.com/a/34911
## To estimate covariance between returns of security $i$ and $j$ using a sample from time $t=1$ to $T$:
#### Step 1: Compute returns for every security $i$ and every period $t$
The return from time $t$ to $t+1$ for security $i$ is given by:
$$ R_{i,t+1} = \frac{P_{t+1} + D_{t+1}}{P_t}$$
where $P_t$ denotes the price at time $t$ and $D_t$ is the time $t$ value of any distributions (eg. dividends, stock distributions, etc...).
Typically data providers will do this for you. Getting everything perfectly correct (eg. delisting returns, distributions etc...) is hugely important.
#### Step 2: Compute sample mean of returns
For security $i$, the sample mean is given by:
$$\bar{r}_i = \frac{1}{T} \sum_t r_{i,t}$$
#### Step 3: Compute sample covariance of returns:
For security $i$ and $j$, the sample covariance is given by:
$$ \hat{Cov}(r_i, r_j) = \frac{1}{T-1} \sum_t \left(r_{i,t} - \bar{r}_i \right) \left( r_{j,t} - \bar{r}_j \right)$$
#### A note on share counts and weights:
Holding a constant number of shares implies portfolio weights almost certainly vary period to period.
Let $P_{i,t}$ be the price of security $i$ and $n_i$ be the number of shares of security $i$. The portfolio weight on security $i$ at time $t$ is given by:
$$ w_{i,t} = \frac{n_i P_{i,t}}{\sum_j n_j P_{j,t}}$$
Since the prices almost certainly change, the portfolio weights almost certainly change. (An exception is if the portfolio is value weighted, in which case the weights will basically remain the same.)Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.