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Exam prep 10 min read

FRM Exam: Essential BA II Plus Functions You Must Know

The FRM leans on the statistics worksheet rather than the TVM row: regression for beta, root-252 for volatility, LN for log returns, numerical duration.

The BA II Plus Calculator Team

The short answer

The FRM uses a different half of the BA II Plus than the CFA does. The keys that matter are the two-variable statistics worksheet in LIN mode, which returns the intercept a, the slope b and the correlation r for a beta or hedge-ratio question; the square-root key for scaling volatility across time; and LN and e^x for continuously compounded returns. Duration and convexity are not registers on the standard model, but both come out of two extra BOND computations. The TVM row still matters, it just carries less of the exam.

Candidates arriving at the FRM from the CFA usually bring the wrong half of the calculator with them. The CFA is a valuation exam and it lives in the TVM row. The FRM is a risk exam, and risk is dispersion — so it lives in the statistics worksheet, the square-root key and the natural log.

The TVM row still earns its place. It just carries a much smaller share of the paper than you are used to, and the functions that replace it are the ones most candidates have never opened.

What the FRM actually asks the calculator to do

Topic The keys
Volatility of a return series 2ND DATA, 2ND STAT — read Sx
Annualising or scaling volatility and ×
Beta, hedge ratio, factor loading 2ND STAT in LIN mode — read b
Correlation and covariance r, then r × Sx × Sy
Parametric VaR and expected shortfall one multiplication each
EWMA and GARCH volatility updates , STO, RCL
Continuously compounded returns LN and e^x
Bond price and yield 2ND BOND
Duration and convexity two extra BOND computations
Expected loss, discounted exposure CF and NPV
Mapped to the function that answers it, not the formula that defines it.

GARP’s approved calculator list has historically been wider than the CFA’s — the BA II Plus and Professional, the HP 12C family and the HP 10B II family — but the list is GARP’s to change, so confirm it on their site rather than trusting this page.

The regression worksheet is the FRM function

If you learn one worksheet for this exam, learn this one. Two-variable statistics in LIN mode returns a slope, an intercept and a correlation coefficient from paired data, which covers beta, hedge ratios, factor loadings and every “estimate the relationship” item on the paper.

Six months of returns: a benchmark index, and a stock you are estimating beta for.

Month Index (X) Stock (Y)
1 2.1 4.6
2 −1.4 −1.2
3 3.6 4.9
4 0.8 1.1
5 −2.3 −5.3
6 4.2 7.9
X is the independent variable — the index. Y is the dependent one.
Pairs go in alternating X, Y
  1. 2ND
  2. DATA
  3. 2ND
  4. CLR WORK
  5. 2.1
  6. ENTER
  7. 4.6
  8. ENTER
  9. 1.4
  10. +|−
  11. ENTER
  12. 1.2
  13. +|−
  14. ENTER
  15. 4.2
  16. ENTER
  17. 7.9
  18. ENTER
  19. 2ND
  20. STAT

If 2ND STAT does not show LIN, press 2ND SET until it does. The Ln, EXP and PWR models fit the same data to curves and will quietly give you a different slope.

Register Value What it is
n 6 Pairs entered — check first
1.1667 Mean index return
Sx 2.6357 Index volatility, sample
Ȳ 2.0000 Mean stock return
Sy 4.7816 Stock volatility, sample
a −0.0567 Intercept — alpha, in this framing
b 1.7629 Slope — beta
r 0.9717 Correlation coefficient
Arrowing down through the STAT worksheet in LIN mode.

Two numbers you will be asked for and will not find on the display:

Derived from r with the x² key and two multiplications
  1. R² = r × r = 0.9717²= 0.9442 Cov(X,Y) = r × Sx × Sy = 0.9717 × 2.6357 × 4.7816 ≈ 12.2460

There is no covariance register on either model. The identity is on the formula sheet and it is two keystrokes here.

So the stock has a beta of 1.76 against this index, 94.4% of its variance is explained by the index, and the covariance is 12.2460 in percent-squared units.

Scaling volatility: the key does most of the work

Risk is quoted at whatever horizon the question wants, and moving between horizons is the square-root-of- time rule.

Scaling volatility
  1. σ over T periods = σ per period × √T

Only valid when returns are independent and identically distributed — which is the assumption the exam is testing, not a fact about markets.

Horizon Multiplier Volatility
1 day 1 1.2000%
10 days √10 = 3.1623 3.7947%
1 month (21 days) √21 = 4.5826 5.4991%
1 year (252 days) √252 = 15.8745 19.0494%
A daily standard deviation of 1.2%, scaled.

Two hundred and fifty-two is the convention for trading days and the number the FRM uses unless the question says otherwise. Note what the rule does not say: it does not scale returns, which grow linearly, only volatility, which grows with the square root. Getting those two the wrong way round is a standard distractor.

VaR and expected shortfall are multiplications

Neither has a function, and neither needs one.

Parametric VaR and expected shortfall, normal distribution
  1. VaR = position × z × σ ES = position × σ × φ(z) ÷ (1 − c)

z is 1.645 at 95% and 2.326 at 99%. φ is the standard normal density at z, which the exam supplies or you compute with e^x.

A £10m position with a 1.2% daily volatility.

Measure Working Result
1-day 95% VaR 10,000,000 × 1.645 × 0.012 £197,400
10-day 95% VaR 197,400 × √10 £624,234
1-day 99% VaR 10,000,000 × 2.326 × 0.012 £279,120
1-day 95% expected shortfall 10,000,000 × 0.012 × 2.0622 £247,466
One position, four measures. Every row is one multiplication chain.

The expected shortfall multiplier is φ(1.645) ÷ 0.05 = 0.103111 ÷ 0.05 = 2.0622, against a VaR multiplier of 1.645. Expected shortfall is always the larger, because it averages the tail rather than cutting it off, and the ratio 2.0622 ÷ 1.645 = 1.2536 is worth memorising as a sanity check at 95%.

EWMA in one line, and why STO matters

The exponentially weighted update is arithmetic, but it is arithmetic you may have to run several times on one item.

EWMA variance update, λ typically 0.94
  1. σ²ₙ = λ σ²ₙ₋₁ + (1 − λ) r²ₙ₋₁

Work in variance, not volatility. Square first, update, then take the root at the end — squaring and rooting on every step is where the errors come in.

Yesterday’s volatility estimate was 1.2%; today’s return was 2.3%. λ = 0.94.

One update, with the first term parked in memory
  1. 0.012
  2. ×
  3. 0.94
  4. =
  5. STO
  6. 1
  7. 0.023
  8. ×
  9. 0.06
  10. =
  11. +
  12. RCL
  13. 1
  14. =

New volatility = 1.2927%. A 2.3% shock — nearly twice the prevailing volatility — moves the estimate by less than a tenth of a percentage point, which is the point of λ = 0.94 and a common conceptual question. Store the variance rather than the volatility if the item asks for two consecutive updates.

Continuous compounding: LN and e^x

The FRM uses continuously compounded returns far more than the CFA does, because they are additive across time. Two conversions cover almost everything.

Question Keys Result
Continuous rate equivalent to 6% effective 1.06 LN 5.826891%
Effective rate equivalent to 6% continuous 0.06 e^x then − 1 6.183655%
Log return when a price goes 100 → 105 1.05 LN 4.8790%
Price after a 6% continuous return 0.06 e^x then × 100 106.1837
Moving between annual and continuous, in one keystroke each way.

The 6% pair is worth holding in memory as an anchor: 6% continuous is 6.183655% effective, and 6% effective is 5.826891% continuous. Continuous is always the smaller number for the same growth, and if your answer is the other way round you have used the wrong key.

The ICONV worksheet handles discrete frequencies — 6% nominal is 6.09% effective semiannually and 6.167781% monthly — but it has no continuous setting, so LN and e^x are the route. The interest conversion calculator runs the whole progression, continuous compounding included.

Duration is not enough: adding convexity

Neither duration nor convexity is a register on the standard BA II Plus, and both are two BOND computations away. Price the bond either side of its yield and read the slope and the curvature.

Numerical duration and convexity from three prices
  1. ModD ≈ (P₋ − P₊) ÷ (2 P₀ Δy) Convexity ≈ (P₋ + P₊ − 2P₀) ÷ (P₀ Δy²)

Δy of 25 basis points is a good compromise: small enough to be local, large enough that rounding in PRI does not dominate.

The 4.5% of February 2034 at a 5.25% yield: P₋ = 96.9047 at 5.00%, P₀ = 95.3994, P₊ = 93.9217 at 5.50%.

ModD = 6.2537 and convexity = 46.16. Now the reason the FRM insists on the second term:

Yield move Duration only Duration + convexity Actual Error with convexity
+100 bp 89.4334 89.6536 89.6481 +0.0055
+200 bp 83.4674 84.3481 84.3034 +0.0447
Predicting the price of the same bond after a yield shock, two ways.

At 200 basis points, duration alone is 0.84 of a point wrong — enough to change a hedge. Adding the convexity term cuts that to 0.04. Every duration-only error is in the same direction, understating the price, because a straight line drawn against a convex curve always falls below it. The bond pricing tutorial has the full error table.

Four settings that break an FRM answer

  1. The regression model. LIN is one of four. Ln, EXP and PWR fit the same pairs to curves and return a slope that is not a beta.
  2. Sx against σx. A sample of returns takes Sx. The choice moves a VaR figure by several percent and there is an answer choice waiting for the wrong one.
  3. Leftover pairs in 2ND DATA. 2ND CLR WORK inside the worksheet. Check n before reading anything else.
  4. Day-count basis and coupon frequency in 2ND BOND. Both survive 2ND CLR WORK visibly only if you arrow onto them, and both change a duration estimate.

What to drill

Task Target
Sx from eight returns 45 s
Annualise a daily volatility 10 s
Beta and r from six pairs 70 s
Covariance from r, Sx, Sy 10 s
VaR at two confidence levels 20 s
One EWMA update 25 s
Numerical duration from three prices 90 s
Target times once practised.

The regression row is the one to time yourself on, because it is the longest sequence on the paper and the one candidates have practised least. Seventy seconds of keying for a question worth the same as a ten-second one is a poor trade, which is exactly why it is worth getting fast at.

If the keypad itself is still unfamiliar, how to use the BA II Plus covers the layout and the four different clears, and CFA exam calculator tips shares most of the setup discipline — the settings that ruin an answer are the same on both exams.

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Do it without the keystrokes

The calculators used in this guide

Same arithmetic, same sign convention, with every substitution written out underneath the answer.

FAQ

Frequently asked questions

Which calculators are allowed in the FRM exam?

GARP's approved list is wider than the CFA's and has historically included the Texas Instruments BA II Plus and BA II Plus Professional, the HP 12C and its variants, and the HP 10B II family. Nothing programmable and nothing with a graphing display. Check GARP's current published list before test day, because it is the exam body's list that governs, not a forum thread.

Can the BA II Plus calculate volatility?

It gives you the standard deviation of a return series from the DATA and STAT worksheets, which is volatility for the period the returns cover. Scaling it to another horizon is the square-root rule and one keystroke: a daily standard deviation of 1.2% annualises to 1.2 × the square root of 252 = 19.05%.

How do you calculate beta on the BA II Plus?

Enter the index returns as X and the asset returns as Y in the DATA worksheet, then read b in the STAT worksheet with LIN selected. b is the slope of the regression of Y on X, which is the beta. Getting X and Y the wrong way round produces a different number that looks entirely plausible.

Does the BA II Plus compute VaR?

Not as a function, but parametric VaR is one multiplication once you have the volatility: position value × z × σ. At 95% confidence z is 1.645, so £10m at a 1.2% daily volatility has a one-day VaR of £197,400. Scale to ten days by multiplying by the square root of ten.

How do I get convexity on a BA II Plus?

Price the bond at the current yield and at 25 basis points either side, then apply the numerical formula: (P₋ + P₊ − 2P₀) ÷ (P₀ × Δy²). For a 4.5% bond maturing in 2034 priced at a 5.25% yield that gives 46.16, and adding the convexity term cuts the error in a 200 basis point price estimate from 0.84 to 0.04.

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