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- Published: 16th September 2026
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Personal statement example
My interest in risk came from a statistics module in my second year, where we were asked to fit distributions to daily returns on a share index. The normal distribution looked reasonable until we examined the tails, and the mismatch was larger than I expected. That gap between a convenient model and observed behaviour has shaped what I have chosen to study since, and it is why I now want to specialise in financial risk management rather than general finance.
My degree was BSc Economics with Statistics, and I deliberately weighted my options towards quantitative work: econometrics, stochastic processes, and a financial markets module that introduced value at risk and the basic structure of capital requirements. For my final-year project I estimated one-day value at risk for an equal-weighted portfolio of ten FTSE 100 shares, comparing a simple variance-covariance approach with historical simulation and a GARCH(1,1) model, all implemented in R. Backtesting over an out-of-sample period showed the variance-covariance estimates were breached more often than the confidence level implied, particularly in clustered periods of high volatility, which the GARCH specification handled more sensibly. The project taught me as much about data handling as about modelling: cleaning dividend adjustments, dealing with missing trading days, and documenting choices so that a marker could reproduce my results. It also made me cautious about presenting a single number as an answer, and I have since read Nassim Nicholas Taleb's discussion of the limits of models built on normal assumptions, which sharpened rather than replaced my interest in quantitative methods.
Alongside my studies I have worked part-time for two and a half years as a customer service adviser at a building society call centre, handling savings account queries and, more recently, first-line questions about mortgage payment changes. The role is not a risk placement, but it has given me a practical view of how policy decisions land on customers. I have explained interest rate changes to people trying to plan a household budget, and I have followed procedures on identifying possible fraud and on referring vulnerable customers, which showed me that operational and conduct risk are matters of process discipline as much as measurement. I passed the internal assessment for handling mortgage enquiries and was asked to help coach two new starters through their first fortnight on the phones, which involved listening to calls and giving feedback clearly and kindly.
I am treasurer of a community running club with around eighty members. I took over records kept in a single spreadsheet with no reconciliation and rebuilt them so that subscriptions, race entry fees and kit orders are tracked separately and checked against the bank statement each month. When we planned an open 10k last spring I prepared a simple cash forecast with best and worst cases for entry numbers, and recommended we cap early spending on medals and signage until entries reached a break-even figure. We finished with a small surplus, which the committee agreed to put towards junior coaching. It was a modest piece of work, but it is the closest I have come to weighing an exposure against a budget with other people's money.
On a master's I want to build proper foundations in credit and market risk modelling, the regulatory framework, and derivatives pricing, and to work with larger and messier datasets than my dissertation allowed. In the longer term I would like to work in a risk function at a bank or insurer, ideally where modelling sits close to the business rather than apart from it. My aim is to be someone who can produce the numbers and also explain honestly what they do not capture.
Why this example works — strengths and ways to improve
This is a strong, coherent statement for a taught master's. Your motivation grows from a concrete statistical observation, your dissertation shows real methods and honest limits, and ordinary roles are used intelligently rather than inflated. The main gaps are thin engagement with what you would research or question next, and a Taleb reference that gestures at reflection more than it demonstrates it.
Subject motivation
The normal distribution looked reasonable until we examined the tails
This works because the motivation is an observed mismatch between model and data, not a vague fascination. It also explains why you chose risk over general finance. The idea of tail behaviour recurs through the statement, so the opening earns its place rather than serving as a hook.
Academic preparation
econometrics, stochastic processes, and a financial markets module that introduced value at risk
These modules map credibly onto the demands of a quantitative risk master's. Naming the BSc and deliberate option choices shows planning. You could briefly signal your comfort with programming or mathematics beyond R. Do not assume this meets any particular programme's entry requirements; check the specific prerequisites yourself.
Evidence and reflection
made me cautious about presenting a single number as an answer
The backtesting result and the data-cleaning detail are excellent, specific evidence. The reflection about single numbers is genuine. The Taleb sentence is weaker: it says the reading sharpened your interest without showing how. One concrete idea you took from it, or one disagreement with it, would turn name-checking into thinking.
Relevant experience
operational and conduct risk are matters of process discipline as much as measurement
You handle a non-risk job honestly and extract a precise, defensible lesson from it. The treasurer example is also strong because the best and worst case forecast and the spending cap show judgement about exposure. Your closing line about that work is appropriately modest.
Credibility and voice
It was a modest piece of work
Your tone is measured and plausible for a recent graduate. You avoid exaggerated authority, and the claims match your stage: a call-centre adviser coaching new starters, a club treasurer, an undergraduate VaR project. This restraint makes the stronger claims more believable.
Structure and format
The structure is clear: origin, academic work, employment, volunteering, then aims. Paragraphs are economical. The final paragraph lists broad course areas but gives no question or problem you want to pursue. When tailoring for a real application, connect your aims to that programme's actual content without inventing fit.
What you’ve done well
- Your dissertation paragraph gives methods, comparison, a backtesting result and practical data lessons, which is exactly the evidence of readiness a quantitative master's needs.
- You draw a distinct, non-generic lesson from each ordinary role instead of listing transferable skills.
How this draft could improve
- Replace the general Taleb reference with one specific argument you accepted or questioned, and say how it changed your modelling view.
- In your aims, name one concrete question you want to explore, such as how your GARCH findings would extend to stressed periods or to credit risk.
- For a real application, check the programme's mathematical prerequisites and prompt, and address any expected background explicitly.