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Tracking error

Tracking error is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Tracking error rather than just read about it. In short: In finance, tracking error or active risk is a measure of the risk in an investment portfolio that is due to active management decisions made by the portfolio manager; it indicates how closely a portfolio follows the index to which it is benchmarked. The best measure is the standard deviation of the difference between the portfolio and index returns.

Key takeaways

  • Tracking error belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Tracking error to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Tracking error from memory before moving on to harder problems.

Reference excerpt

In finance, tracking error or active risk is a measure of the risk in an investment portfolio that is due to active management decisions made by the portfolio manager; it indicates how closely a portfolio follows the index to which it is benchmarked. The best measure is the standard deviation of the difference between the portfolio and index returns. Many portfolios are managed to a benchmark, typically an index. Some portfolios, notably index funds, are expected to replicate, before trading and other costs, the returns of an index exactly, while others 'actively manage' the portfolio by deviating from the index in order to generate active returns. Tracking error measures the deviation from the benchmark: an index fund has a near-zero tracking error, while an actively managed portfolio would normally have a higher tracking error. Thus the tracking error does not include any risk (return) that is merely a function of the market's movement. In addition to risk (return) from specific stock selection or industry and factor "betas", it can also include risk (return) from market timing decisions. Dividing portfolio active return by portfolio tracking error gives the information ratio, which is a risk adjusted performance measure.

Definition If tracking error is measured historically, it is called 'realized' or 'ex post' tracking error. If a model is used to predict tracking error, it is called 'ex ante' tracking error. Ex-post tracking error is more useful for reporting performance, whereas ex-ante tracking error is generally used by portfolio managers to control risk. Various types of ex-ante tracking error models exist, from simple equity models which use beta as a primary determinant to more complicated multi-factor fixed income models. In a factor model of a portfolio, the non-systematic risk (i.e., the standard deviation of the residuals) is called "tracking error" in the investment field. The latter way to compute the tracking error complements the formulas below but results can vary (sometimes by a factor of 2).

Formulas The ex-post tracking error formula is the standard deviation of the active returns, given by:

T E = ω = Var ⁡ ( r p − r b ) = E [ ( r p − r b ) 2 ] − ( E [ r p − r b ] ) 2 = ( w p − w b ) T Σ ( w p − w b ) {\displaystyle TE=\omega ={\sqrt {\operatorname {Var} (r_{p}-r_{b})}}={\sqrt {{E}[(r_{p}-r_{b})^{2}]-({E}[r_{p}-r_{b}])^{2}}}={\sqrt {(w_{p}-w_{b})^{T}\Sigma (w_{p}-w_{b})}}}

where r p − r b {\displaystyle r_{p}-r_{b}} is the active return, i.e., the difference between the portfolio return and the benchmark return and ( w p − w b ) {\displaystyle (w_{p}-w_{b})} is the vector of active portfolio weights relative to the benchmark. The optimization problem of maximizing the return, subject to tracking error and linear constraints, may be solved using second-order cone programming: argmax w μ T ( w − w b ) , s.t. ( w − w b ) T Σ ( w − w b ) ≤ ω 2 , A x ≤ b , C x = d {\displaystyle {\underset {w}{\operatorname {argmax} }}\;\mu ^{T}(w-w_{b}),\quad {\text{s.t.}}\;(w-w_{b})^{T}\Sigma (w-w_{b})\leq \omega ^{2},\;Ax\leq b,\;Cx=d}

Interpretation Under the assumption of normality of returns, an active risk of x per cent would mean that approximately 2/3 of the portfolio's active returns (one standard deviation from the mean) can be expected to fall between +x and -x per cent of the mean excess return and about 95% of the portfolio's active returns (two standard deviations from the mean) can be expected to fall between +2x and -2x per cent of the mean excess return.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Tracking error

Start with the simplest possible case. Write down what Tracking error claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Tracking error before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Tracking error ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Tracking error

In research
Tracking error appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Tracking error in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Tracking error is common in secondary-school and first-year university syllabi. It links to neighbouring topics Convex optimization, Financial risk management, Investment fund indicators, so understanding it makes those chapters shorter.
In everyday life
Look for Tracking error outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Tracking error in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Tracking error means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Tracking error out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Tracking error in simple terms?

In finance, tracking error or active risk is a measure of the risk in an investment portfolio that is due to active management decisions made by the portfolio manager; it indicates how closely a portfolio follows the index to which it is benchmarked. The best measure is the standard deviation of th…

Why does Tracking error matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Tracking error?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Tracking error.

Tags

  • Convex optimization
  • Financial risk management
  • Investment fund indicators

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