science

Historical simulation (finance)

Historical simulation (finance) 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 Historical simulation (finance) rather than just read about it. In short: Historical simulation in finance's value at risk (VaR) analysis is a procedure for predicting the value at risk by 'simulating' or constructing the cumulative distribution function (CDF) of assets returns over time assuming that future returns will be directly sampled from past returns. Unlike parametric VaR models, historical simulation does not assume a particular distribution of the asset returns.

Key takeaways

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

Reference excerpt

Historical simulation in finance's value at risk (VaR) analysis is a procedure for predicting the value at risk by 'simulating' or constructing the cumulative distribution function (CDF) of assets returns over time assuming that future returns will be directly sampled from past returns. Unlike parametric VaR models, historical simulation does not assume a particular distribution of the asset returns. Also, it is relatively easy to implement. However, there are a couple of shortcomings of historical simulation. Traditional historical simulation applies equal weight to all returns of the whole period; this is inconsistent with the diminishing predictability of data that are further away from the present.

Weighted historical simulation Weighted historical simulation applies decreasing weights to returns that are further away from the present, which overcomes the inconsistency of historical simulation with diminishing predictability of data that are further away from the present. However, weighted historical simulation still assumes independent and identically distributed random variables (IID) asset returns.

Filtered historical simulation Filtered historical simulation tries to capture volatility which is one of the causes for violation of IID.

See also Monte Carlo methods in finance Quasi-Monte Carlo methods in finance Financial modeling

References

External links Filtered Historical Simulation Giovanni Barone-Adesi, Frederick Bourgoin, Kostas Giannopoulos (1998) Do Not Look Back

Worked examples

Example 1 — a first encounter with Historical simulation (finance)

Start with the simplest possible case. Write down what Historical simulation (finance) 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 Historical simulation (finance) 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 Historical simulation (finance) 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 Historical simulation (finance)

In research: Historical simulation (finance) 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 Historical simulation (finance) 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: Historical simulation (finance) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Financial risk modeling, Monte Carlo methods in finance, so understanding it makes those chapters shorter.
In everyday life: Look for Historical simulation (finance) 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 Historical simulation (finance) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Historical simulation (finance) means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Historical simulation (finance) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Historical simulation (finance) in simple terms?

Why does Historical simulation (finance) 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 Historical simulation (finance)?

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 Historical simulation (finance).