FA-61711 / Options payoff and settlement / Open access

Cliquet payoff with local caps and a global floor: the global floor is replaced by zero · case 01

Guaranteed minimum coupons above zero are not honored.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The payoff floors at zero instead of the stated global floor.

THE FAILURE

The payoff floors at zero instead of the stated global floor.

Unsuccessful approach: Paying zero when below the global floor inverts the guarantee.

Case contract

Inputs reset prices (first is the initial level), local cap, local floor, global floor and notional. Each period return is S_i/S_{i-1} - 1 clipped to [local floor, local cap]. Payoff = notional * max(global floor, sum of clipped returns), in exact fractions, rounded to 6.

Why this case matters

Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(resets, cap, floor, global_floor, notional):
    px = [Fraction(str(v)) for v in resets]
    c = Fraction(str(cap))
    f = Fraction(str(floor))
    total = Fraction(0)
    for prev, cur in zip(px, px[1:]):
        r = cur / prev - 1
        total += min(max(r, f), c)
    pay = notional * max(Fraction(0), total)
    return float(round(pay, 6))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression global floor 1', [[100.0, 92.0, 92.0, 78.2, 71.94, 82.73, 76.11], 0.08, -0.05, 0.01, 1000], 10.0], ['regression global floor 2', [[100.0, 85.0, 78.2, 78.2, 76.64], 0.08, -0.05, 0.01, 1000000], 10000.0], ['partial repair probe 1', [[100.0, 103.0, 94.76], 0.08, -0.02, 0.01, 1000000], 10000.0], ['partial repair probe 2', [[100.0, 92.0, 98.44, 98.44, 83.67], 0.05, -0.02, 0.01, 1000], 10.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 107.0], 0.08, -0.02, 0.02, 1000000], 70000.0], ['normal control 2', [[100.0, 115.0, 105.8], 0.08, -0.02, 0.01, 1000], 60.0], ['normal control 3', [[100.0, 115.0], 0.05, -0.02, 0.0, 1000000], 50000.0]], [['regression global floor 1', [[100.0, 98.0, 96.04], 0.05, 0.0, 0.02, 1000], 20.0], ['regression global floor 2', [[100.0, 98.0, 83.3, 70.8], 0.08, -0.02, 0.02, 1000000], 20000.0], ['partial repair probe 1', [[100.0, 107.0, 90.95], 0.08, -0.05, 0.02, 1000000], 20000.0], ['partial repair probe 2', [[100.0, 103.0, 100.94], 0.08, -0.05, 0.01, 1000], 10.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 100.0, 115.0, 123.05, 113.21], 0.05, 0.0, 0.01, 1000], 100.0], ['normal control 2', [[100.0, 107.0, 104.86, 120.59, 102.5], 0.08, 0.0, 0.02, 1000], 150.0], ['normal control 3', [[100.0, 107.0, 104.86, 96.47], 0.05, 0.0, 0.02, 1000], 50.0]], [['regression global floor 1', [[100.0, 103.0, 87.55, 80.55, 78.94, 81.31], 0.05, -0.02, 0.01, 1000000], 10000.0], ['regression global floor 2', [[100.0, 107.0, 90.95, 83.67], 0.08, -0.05, 0.02, 1000000], 20000.0], ['partial repair probe 1', [[100.0, 103.0, 100.94], 0.05, -0.05, 0.01, 1000000], 10000.0], ['partial repair probe 2', [[100.0, 92.0, 105.8, 89.93, 89.93, 82.74], 0.08, -0.02, 0.02, 1000000], 20000.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 103.0, 118.45, 122.0, 122.0, 112.24, 120.1], 0.05, 0.0, 0.01, 1000000], 159970.451667], ['normal control 2', [[100.0, 107.0, 123.05, 123.05, 123.05, 141.51, 145.76], 0.08, -0.05, 0.01, 1000000], 260033.2132], ['normal control 3', [[100.0, 100.0, 92.0, 84.64, 90.56, 104.14, 104.14], 0.05, 0.0, 0.01, 1000000], 100000.0]], [['regression global floor 1', [[100.0, 92.0], 0.08, -0.02, 0.01, 1000000], 10000.0], ['regression global floor 2', [[100.0, 92.0, 94.76, 80.55, 80.55], 0.08, -0.05, 0.02, 1000000], 20000.0], ['partial repair probe 1', [[100.0, 103.0, 100.94, 98.92, 98.92, 84.08, 89.97], 0.05, -0.02, 0.02, 1000000], 20000.0], ['partial repair probe 2', [[100.0, 103.0, 106.09, 106.09, 90.18, 76.65], 0.08, -0.02, 0.02, 1000000], 20000.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 100.0, 115.0, 97.75, 100.68], 0.08, -0.02, 0.0, 1000], 89.974425], ['normal control 2', [[100.0, 107.0, 123.05, 126.74, 130.54, 120.1], 0.05, -0.02, 0.0, 1000000], 139970.451462], ['normal control 3', [[100.0, 100.0, 115.0, 123.05], 0.08, -0.02, 0.01, 1000], 150.0]], [['regression global floor 1', [[100.0, 85.0, 85.0, 83.3, 83.3, 83.3], 0.05, -0.02, 0.01, 1000000], 10000.0], ['regression global floor 2', [[100.0, 92.0, 94.76, 92.86, 99.36], 0.05, -0.05, 0.02, 1000], 20.0], ['partial repair probe 1', [[100.0, 92.0, 94.76], 0.05, -0.02, 0.01, 1000], 10.0], ['partial repair probe 2', [[100.0, 92.0, 94.76], 0.05, -0.02, 0.01, 1000000], 10000.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 103.0], 0.05, 0.0, 0.02, 1000], 30.0], ['normal control 2', [[100.0, 107.0, 104.86, 120.59, 124.21, 105.58, 112.97], 0.08, -0.02, 0.0, 1000000], 210013.389997], ['normal control 3', [[100.0, 100.0, 107.0], 0.08, -0.02, 0.0, 1000000], 70000.0]]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression global floor 1	`0.0`	`10.0`	Failed
regression global floor 2	`0.0`	`10000.0`	Failed
partial repair probe 1	`10000.0`	`10000.0`	Passed
partial repair probe 2	`10.0`	`10.0`	Passed
boundary control 1	`30.0`	`30.0`	Passed
normal control 1	`70000.0`	`70000.0`	Passed
normal control 2	`60.0`	`60.0`	Passed
normal control 3	`50000.0`	`50000.0`	Passed

SHA-256 / 15002c1f0642646ef3cd2fe7314577d93fc0a0e55f983af9af305687616dd63c

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(resets, cap, floor, global_floor, notional):
    px = [Fraction(str(v)) for v in resets]
    c = Fraction(str(cap))
    f = Fraction(str(floor))
    total = Fraction(0)
    for prev, cur in zip(px, px[1:]):
        r = cur / prev - 1
        total += min(max(r, f), c)
    pay = notional * (total if total > Fraction(str(global_floor)) else 0)
    return float(round(pay, 6))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression global floor 1', [[100.0, 92.0, 92.0, 78.2, 71.94, 82.73, 76.11], 0.08, -0.05, 0.01, 1000], 10.0], ['regression global floor 2', [[100.0, 85.0, 78.2, 78.2, 76.64], 0.08, -0.05, 0.01, 1000000], 10000.0], ['partial repair probe 1', [[100.0, 103.0, 94.76], 0.08, -0.02, 0.01, 1000000], 10000.0], ['partial repair probe 2', [[100.0, 92.0, 98.44, 98.44, 83.67], 0.05, -0.02, 0.01, 1000], 10.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 107.0], 0.08, -0.02, 0.02, 1000000], 70000.0], ['normal control 2', [[100.0, 115.0, 105.8], 0.08, -0.02, 0.01, 1000], 60.0], ['normal control 3', [[100.0, 115.0], 0.05, -0.02, 0.0, 1000000], 50000.0]], [['regression global floor 1', [[100.0, 98.0, 96.04], 0.05, 0.0, 0.02, 1000], 20.0], ['regression global floor 2', [[100.0, 98.0, 83.3, 70.8], 0.08, -0.02, 0.02, 1000000], 20000.0], ['partial repair probe 1', [[100.0, 107.0, 90.95], 0.08, -0.05, 0.02, 1000000], 20000.0], ['partial repair probe 2', [[100.0, 103.0, 100.94], 0.08, -0.05, 0.01, 1000], 10.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 100.0, 115.0, 123.05, 113.21], 0.05, 0.0, 0.01, 1000], 100.0], ['normal control 2', [[100.0, 107.0, 104.86, 120.59, 102.5], 0.08, 0.0, 0.02, 1000], 150.0], ['normal control 3', [[100.0, 107.0, 104.86, 96.47], 0.05, 0.0, 0.02, 1000], 50.0]], [['regression global floor 1', [[100.0, 103.0, 87.55, 80.55, 78.94, 81.31], 0.05, -0.02, 0.01, 1000000], 10000.0], ['regression global floor 2', [[100.0, 107.0, 90.95, 83.67], 0.08, -0.05, 0.02, 1000000], 20000.0], ['partial repair probe 1', [[100.0, 103.0, 100.94], 0.05, -0.05, 0.01, 1000000], 10000.0], ['partial repair probe 2', [[100.0, 92.0, 105.8, 89.93, 89.93, 82.74], 0.08, -0.02, 0.02, 1000000], 20000.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 103.0, 118.45, 122.0, 122.0, 112.24, 120.1], 0.05, 0.0, 0.01, 1000000], 159970.451667], ['normal control 2', [[100.0, 107.0, 123.05, 123.05, 123.05, 141.51, 145.76], 0.08, -0.05, 0.01, 1000000], 260033.2132], ['normal control 3', [[100.0, 100.0, 92.0, 84.64, 90.56, 104.14, 104.14], 0.05, 0.0, 0.01, 1000000], 100000.0]], [['regression global floor 1', [[100.0, 92.0], 0.08, -0.02, 0.01, 1000000], 10000.0], ['regression global floor 2', [[100.0, 92.0, 94.76, 80.55, 80.55], 0.08, -0.05, 0.02, 1000000], 20000.0], ['partial repair probe 1', [[100.0, 103.0, 100.94, 98.92, 98.92, 84.08, 89.97], 0.05, -0.02, 0.02, 1000000], 20000.0], ['partial repair probe 2', [[100.0, 103.0, 106.09, 106.09, 90.18, 76.65], 0.08, -0.02, 0.02, 1000000], 20000.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 100.0, 115.0, 97.75, 100.68], 0.08, -0.02, 0.0, 1000], 89.974425], ['normal control 2', [[100.0, 107.0, 123.05, 126.74, 130.54, 120.1], 0.05, -0.02, 0.0, 1000000], 139970.451462], ['normal control 3', [[100.0, 100.0, 115.0, 123.05], 0.08, -0.02, 0.01, 1000], 150.0]], [['regression global floor 1', [[100.0, 85.0, 85.0, 83.3, 83.3, 83.3], 0.05, -0.02, 0.01, 1000000], 10000.0], ['regression global floor 2', [[100.0, 92.0, 94.76, 92.86, 99.36], 0.05, -0.05, 0.02, 1000], 20.0], ['partial repair probe 1', [[100.0, 92.0, 94.76], 0.05, -0.02, 0.01, 1000], 10.0], ['partial repair probe 2', [[100.0, 92.0, 94.76], 0.05, -0.02, 0.01, 1000000], 10000.0], ['boundary control 1', [[100.0, 110.0, 99.0], 0.05, -0.02, 0.01, 1000], 30.0], ['normal control 1', [[100.0, 103.0], 0.05, 0.0, 0.02, 1000], 30.0], ['normal control 2', [[100.0, 107.0, 104.86, 120.59, 124.21, 105.58, 112.97], 0.08, -0.02, 0.0, 1000000], 210013.389997], ['normal control 3', [[100.0, 100.0, 107.0], 0.08, -0.02, 0.0, 1000000], 70000.0]]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression global floor 1	`0.0`	`10.0`	Failed
regression global floor 2	`0.0`	`10000.0`	Failed
partial repair probe 1	`0.0`	`10000.0`	Failed
partial repair probe 2	`0.0`	`10.0`	Failed
boundary control 1	`30.0`	`30.0`	Passed
normal control 1	`70000.0`	`70000.0`	Passed
normal control 2	`60.0`	`60.0`	Passed
normal control 3	`50000.0`	`50000.0`	Passed

SHA-256 / 15673a0ac784d53ad95785b095343f161d511af571349c57249cc70306d2ff16

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Verification & scope

A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any exchange or clearing rulebook. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:46:57.783981+00:00.

Case digest / e35a19b0d1be5bd6f66081cf11b01b42fa1b77c3bf39dfa027e1713903ee3792