1 Missing stock price data
When downloading historic stock price data it happens quite frequently that some observations in the middle of the sample are missing. Hence the question: how should we cope with this? There are several ways how we could process the data, each approach coming with its own advantages and disadvantages, and we want to compare some of the most common approaches in this post.
In any case, however, we want to treat missing values as
NA and not as Julia’s built-in
NaN (a short justification on why
NA is more suitable can be found here). Hence, data best should be treated through
DataFrames or – if the data comes with time information – through type
Timenum from the TimeData package. In the following, we will use these packages in order to show some common approaches to deal with missing stock price data using an artificially made up data set that shall represent logarthmic prices.
The reason why we are looking at logarthmic prices and returns instead of normal prices and net returns is just that logarithmic returns are defined as simple difference between logarithmic prices of successive days:
This way, our calculations simply involve nicer numbers, and all results equally hold for normal prices and returns as well.
We will be using the following exemplary data set of logarthmic prices for the comparison of different approaches:
using TimeData using Dates using Econometrics df = DataFrame() df[:stock1] = @data([100, 120, 140, 170, 200]) df[:stock2] = @data([110, 120, NA, 130, 150]) dats = [Date(2010, 1, 1):Date(2010, 1, 5)] originalPrices = Timenum(df, dats)
One possible explanation for such a pattern in the data could be that the two stocks are from different countries, and only the country of the second stock has a holiday at January the 3rd.
Quite often in such a situation, people just refrain from any deviations from the basic calculation formula of logarithmic returns and calculate the associated returns as simple differences. This way, however, each missing observation
NA will lead to two
NAs in the return series:
simpleDiffRets = originalPrices[2:end, :] .- originalPrices[1:(end-1), :]
For example, this also is the approach followed by the
PerformanceAnalytics package in
library(tseries) library(PerformanceAnalytics) stockPrices1 <- c(100, 120, 140, 170, 200) stockPrices2 <- c(110, 120, NA, 130, 150) ## combine in matrix and name columns and rows stockPrices <- cbind(stockPrices1, stockPrices2) dates <- seq(as.Date('2010-1-1'),by='days',length=5) colnames(stockPrices) <- c("A", "B") rownames(stockPrices) <- as.character(dates) (stockPrices) returns = Return.calculate(exp(stockPrices), method="compound")
When we calculate returns as the difference between successive closing prices and , a single return simply represents all price movements that happened at day , including the opening auction that determines the very first price at this day.
Thinking about returns this way, it obviously makes sense to assign a value of
NA to each day of the return series where a stock exchange was closed due to holiday, since there simply are no stock price movements on that day. But why would we set the next day’s return to
NA as well?
In other words, we should distinguish between two different cases of
NA values for our prices:
NAoccurs because the stock exchange was closed this day and hence there never were any price movements
- the stock exchange was open that day, and in reality there were some price changes. However, due to a deficiency of our data set, we do not know the price of the respective day.
For the second case, we really would like to have two consecutive values of
NA in our return series. Knowing only the prices in and , there is no way how we could reasonably deduce the value that the price did take on in . Hence, there are infinitely many possibilities to allocate a certain two-day price increase to two returns.
For the first case, however, it seems unnecessarily rigid to force the return series to have two
NA values: allocating all of the two-day price increase to the one day where the stock exchange was open, and a missing value
NA to the day that the stock exchange was closed doesn’t seem to be too arbitrary.
This is how returns are calculated by default in the (not yet registered) Econometrics package.
logRet = price2ret(originalPrices, log = true)
And, the other way round, aggregating the return series again will also keep
NAs for the respective days, but otherwise perform the desired aggregation. Without specified initial prices, aggregated prices will all start with value 0 for logarithmic prices, and hence express something like normalized prices that allow a nice comparison of different stock price evolutions.
normedPrices = ret2price(logRet, log = true)
To regain the complete price series (together with a definitely correct starting date), one simply needs to additionally specify the original starting prices.
truePrices = ret2price(logRet, originalPrices, log = true)
In some cases, however, missing values
NA may not be allowed. This could be a likelihood function that requires real values only, or some plotting function. For these cases
NAs easily can be removed through imputation. For log price plots, a meaningful way would be:
However, for log returns, the associated transformation then would artificially incorporate values of 0:
As an alternative to replacing
NA values, one could also simply remove the respective dates from the data set. Again, there are two options how this could be done.
First, one could remove any missing observations directly in the price series:
originalPrices2 = deepcopy(originalPrices) noNAprices = convert(TimeData.Timematr, originalPrices2, true)
For the return series, however, we then have a – probably large – multi-day price jump that seems to be a single-day return. In our example, we suddenly observe a return of 50 for the first stock.
logRet = price2ret(noNAprices)
A second way to eliminate
NAs would be to remove them from the return series.
logRet = price2ret(originalPrices) noNAlogRet = convert(TimeData.Timematr, logRet, true)
However, deriving the associated price series for this processed return series will then lead to deviating end prices:
noNAprices = ret2price(noNAlogRet, originalPrices)
as opposed to the real end prices
The first stock now suddenly ends with a price of only 180 instead of 200.
The first step when facing a missing price observation is to think whether it might make sense to treat only one return as missing, assigning the complete price movement to the other return. This is perfectly reasonable for days where the stock market really was closed. In all other cases, however, it might make more sense to calculate logarithmic returns as simple differences, leading to two
NAs in the return series.
Once there are
NA values present, we can chose between three options.
NA values might be cumbersome in some situations, since some functions might only be working for data without
In cases where
NAs may not be present, there sometimes might exist ways of replacing them that perfectly make sense. However, manually replacing observations in some way means messing with the original data, and one should be cautious to not incorporate any artificial patterns this way.
Obviously, when dates with
NA values for only some variables are eliminated completely, we simultaneously lose data for those variables where observations originally were present. Furthermore, eliminating cases with
NAs for returns will lead to price evolutions that are different to the original prices.
|idx||simple diffs||single NA||replace \w 0||rm NA price||rm NA return|
|2010-01-01||100, 110||100, 110||100, 100||100, 110||100, 110|
|2010-01-02||120, 120||120, 120||120, 120||120, 120||120, 120|
|2010-01-03||140, NA||140, NA||140, 120|
|2010-01-04||170, 130||170, 130||170, 130||170, 130||150, 130|
|2010-01-05||200, 150||200, 150||200, 150||200, 150||180, 150|
|idx||simple diffs||single NA||replace \w 0||rm NA price||rm NA return|
|2010-01-02||20, 10||20, 10||20, 10||20, 10||20, 10|
|2010-01-03||20, NA||20, NA||20, 0|
|2010-01-04||30, NA||30, 10||30, 10||50, 10||30, 10|
|2010-01-05||30, 20||30, 20||30, 20||30, 20||30, 20|
In risk management, most traditional approaches are built around a fundamental desire for analytical tractability. The reason for this is obvious: with only minor computational power of computers the only way to guarantee quick solutions was through mathematics. And in practice, when risk assessment and portfolio optimization becomes part of your business, you wouldn’t want to have a model that needs hours or days in order to come up with some insights into your risks.
However, in reality the challenges on your analytical risk management model are rather high. It is almost sure that your model will not have to deal with one-dimensional risks only. Whether high-dimensionality arises through multiple assets or multi-period risk assessment of one asset only: both cases force you to deal with interactions of multiple random variables, thereby complicating an analytical solution enormously. And, of course: in most real world situations, you will encounter both cases. Life just ain’t easy…
In this post we will try to work out some circumstances that need to prevail in order that we could still hope to find an analytical solution. Still, whether such convenient circumstances also do occur in reality, is something that we will need to put some further thought on afterwards.