Correlated Event Exposure: Are Your Positions Really One Bet?

To measure correlated event exposure, group the positions that depend on the same driver, define one scenario in which that driver goes against you, and calculate the combined profit or loss of the whole group under that scenario. Then compare the result with a written limit before you add another position. Every position can pass its own size limit while the group, in that one scenario, loses more than you meant to risk on a single driver. Counting positions alone can give a misleading picture of diversification, because positions may share loss drivers or may offset one another. Positions that all lose in the same scenario add up, in that scenario, like one larger position.

This article covers the aggregation step: identifying shared drivers, calculating the loss in a defined scenario, and checking it against a limit before entry. It does not cover how to size a single position, which position sizing in trading and prediction market position sizing address, or the account-level structure of a risk plan, which trading risk management owns. The examples mix general trading positions and prediction-market contracts, because the aggregation problem is the same and only the way positions are linked differs. The worked example below shows the check failing at the second position, before it is bought.

How do you measure exposure across positions that share a driver?

1. Name the driver. The event, release, price level, decision, or assumption that must hold for the proposed position to pay.
2. Define the adverse scenario for that driver, and the baseline you measure from: purchase cost or current marked value.
3. List the affected positions, noting any that also depend on other drivers.
4. Add working orders as possible positions, using only fill combinations that can actually happen.
5. Calculate each position’s P&L in the scenario: scenario value minus baseline value, with fees treated the same way throughout.
6. Aggregate once. Total the cluster’s raw net scenario loss (and its gross adverse loss where hedges are involved), apply your written offset-recognition policy to get the limit-eligible scenario loss, keeping both figures visible when they differ, and separately total the whole book’s portfolio scenario loss under the same joint scenario, counting each position once. Show existing exposure and the incremental change (net loss after entry minus net loss before entry) on separate lines.
7. Compare the post-entry limit-eligible loss with your written limits for the driver and for the portfolio before placing the order. It equals the raw net scenario loss when your policy credits every offset in full.
8. Record the decision and its assumptions: within the limit, reduced, skipped, or a pre-authorized exception.

The limits you choose in step 7 are your own rules. Nothing here recommends a particular percentage.

What is correlated event exposure?

Correlated event exposure is the combined profit-and-loss exposure of positions that depend on the same event or assumption, measured under a defined scenario. The terms used in this article:

• Shared driver: the event, release, price level, policy decision, or assumption that several positions need in order to pay or to avoid a loss.
• Statistical correlation: a standardized measure, from −1 to +1, of the linear association between two variables, such as two positions’ returns over a sample period. It does not establish causation, a correlation of zero does not establish independence, and a shared driver does not guarantee any particular value.
• Concentration risk: the amount of exposure tied to one instrument, event, sector, factor, or thesis.
• Common-cause dependency: a shared underlying condition that can affect several positions at once, whether or not enough price history exists to measure a correlation. Positions that share a driver can still respond differently because of their direction, payout structure, size, or timing.
• Scenario loss: the loss under one specified scenario, from a stated baseline. It is not a position’s notional size, the capital committed, or the collateral posted.
• Cluster exposure: the positions under one driver, measured in a defined scenario as cluster gross adverse loss (the sum of the losing legs) or cluster net scenario loss (their combined P&L as a nonnegative loss).
• Portfolio exposure: the portfolio scenario loss, meaning the nonnegative loss of the whole book under one coherent joint scenario, each position counted once.
• Limit-eligible scenario loss: the scenario loss after applying your predefined offset-recognition policy, which credits each offsetting gain in full, in part, or not at all. The unadjusted figure is the raw net scenario loss.

A common cause is one reason positions can be statistically correlated, not the only one, and a shared driver can exist where no correlation has ever been measured. A shared driver is not harmful in itself, and different drivers do not guarantee independence: what matters is the size and direction of the combined exposure under the scenarios you test.

Portfolio theory explains why the relationships among holdings matter: in Markowitz’s framework, a portfolio’s variance depends on the covariances among its holdings as well as on each holding’s own variance.¹ That concerns variance, not worst-case loss, and it does not address event contracts. A per-position rule controls each holding on its own and leaves the relationships among them unmeasured. The AI risk management article draws a related line for tool output: a flag for “correlated” positions shows statistical co-movement, not a confirmed shared driver.

Why can counting positions mislead you about diversification?

Take N positions with equal weights, equal return variances, and one common pairwise correlation ρ, which is always a valid structure for ρ ≥ 0. The variance of their average return is one position’s variance times [1 + (N − 1)ρ] ÷ N. Matching that to the variance of independent positions gives an effective number of independent bets, N_eff = N ÷ [1 + (N − 1)ρ]. This restates the standard equal-weight variance identity to show the direction of the effect. It is not a measurement of your book.

Pairwise correlation ρ	N_eff, N = 4	N_eff, N = 10	Ceiling as N grows
0	4.00	10.00	no ceiling
0.3	2.11	2.70	3.33 (that is, 1 ÷ ρ)
0.6	1.43	1.56	1.67
1.0	1.00	1.00	1.00

At ρ = 0.6, four positions carry the return variance of about 1.4 independent ones, and at ρ = 0.3, ten carry that of about 2.7. For any fixed positive ρ, N_eff cannot pass 1 ÷ ρ however many similar positions you add. At ρ = 0 the formula has no ceiling, but zero correlation alone does not imply independence. The table covers only ρ ≥ 0; negative correlations push N_eff above N, the case where positions offset one another. In variance terms, diversification depends on the covariances among positions, not their number. For event contracts, the equivalent question is state-contingent: what does each position pay under each possible outcome?

This is a variance equivalence. It is not a literal count of independent events, a measure of maximum loss, a description of tail dependence, or a sizing formula. Real books have unequal stakes and mixed, changing relationships, so use the table for direction, not as a calculation to reuse.

What does a growth-optimal betting view say about linked bets?

The Kelly criterion picks the stake that maximizes long-run growth, given a known edge and known probabilities. Thorp’s Example 6.2 applies it to two identical favorable even-money bets with correlated outcomes.² Each bet has edge m, so it wins with probability (1 + m) ÷ 2. The joint distribution has one free parameter, the probability c that both bets lose, and it sets the correlation: ρ = [4c − (1 − m)²] ÷ (1 − m²). With equal stakes, the growth-optimal stake on each bet is m ÷ [2(2c + m)], which falls from m ÷ (1 + m²) at zero correlation to m ÷ 2 at perfect correlation. For m = 0.10, a 55% chance of winning each bet:

Correlation between the two bets	Optimal stake on each	Total across both
0	9.9%	19.8%
0.5	6.6%	13.3%
1.0	5.0%	10.0%

Totals are computed before rounding. At correlation 1 the two bets are one bet: the optimal total is 10%, the same as for a single bet with this edge, so the second offers no independent diversification. That does not make it harmful in itself. Splitting an unchanged 10% between two identical, perfectly correlated bets leaves total exposure and modeled growth unchanged. Adding further net exposure in the same direction, whether a partial or a full stake, raises the portfolio’s exposure to that outcome, which is the case an aggregate limit exists to catch.

These are outputs of a stylized model, not recommended stakes. The model assumes a known edge, known probabilities, a specified joint distribution, and idealized execution. Thorp notes that for an exact answer correlation alone is not enough and the full joint distribution is needed, so the same correlation can imply different optimal stakes under another distribution. The prediction market position sizing article covers what estimation error does to Kelly-style stakes.

How do you find the shared driver?

Statistical correlation estimated from past prices measures co-movement in a sample, and many event contracts lack the price history to estimate it. Writing down what each position needs to be true finds structural dependencies without price data: state each driver, then look for overlap.

Type of link	Illustrative example	Question to ask
Same event	Two contracts on the same outcome, or several on nested thresholds of one number	If this event goes the other way, which positions lose together?
Same data release or source	Contracts and trades that all pay off on one report	Do these settle or move on the same publication?
Same underlying factor	Long EUR/USD, long GBP/USD, and short USD/JPY	Would a broad rise in the US dollar hurt all three?
Same thesis	Positions in different instruments that all rely on one view of the economy	What single sentence, if false, hurts all of them?
Same settlement source or timing	Contracts whose rules name the same verification source	Could a delay, revision, or dispute at that source reach all of them, even if their payouts differ?

The currency row is true by construction: EUR/USD and GBP/USD quote the dollar as the second currency, so a long position in either is short the dollar, and so is short USD/JPY. How much each loses in a dollar rally is a separate estimate, because each pair also responds to other drivers, and whether they move together in a given period is an empirical matter to check against your own data. The other rows are hypothetical. A shared driver tells you where to look, not the size or sign of each position’s response.

Can you rely on a correlation measured in calm markets?

Historical correlations can be unstable across market regimes, so stress analysis should not rely only on estimates from tranquil periods. Longin and Solnik studied extreme co-movement among international equity markets and reported that correlation is not related to volatility itself but to the market trend: it rises in bear markets, not in bull markets.³ That finding concerns international equity indexes. It sets no general floor for correlations and should not be carried over to prediction markets, other asset classes, or any particular instrument.

That is empirical dependence, estimated from prices. Event contracts add contractual dependence: positions that need the same outcome are linked by the contracts’ terms whatever the price history says, so their stress scenario is built from what each contract pays, not from an estimated correlation.

How do prediction-market contracts get linked?

Read the venue’s own documents before deciding that two contracts are independent or interchangeable. Four mechanics matter.

Mutually exclusive outcome sets. Polymarket’s documentation separates ordinary multi-market events, which group related binary questions, from events with mutually exclusive outcomes, which it links as a negative-risk group where exactly one market resolves Yes and the rest resolve No.⁴ Markets in an ordinary event have no conversion between them, so confirm a specific event’s structure from its own rules. A shared event creates a common dependency, but not necessarily a common loss: that depends on which outcomes you hold, in what quantities, at what prices, and under which resolution rules. Positions that share an event can relate in four ways:

• Same-direction concentration: positions that lose under the same adverse outcome, so their losses add.
• Partial outcome coverage: positions on different exclusive outcomes that offset in some outcomes and leave others uncovered. In a three-outcome event, YES on A and YES on B both lose if C wins.
• Complete outcome coverage: every outcome of an exhaustive, mutually exclusive set held in suitable quantities can give an outcome-independent terminal payoff, subject to verified contract rules. A fixed payout is not a fixed profit: with one $1-paying outcome and one YES share of each, terminal P&L is $1 minus the total acquisition cost and other costs. Before other costs it is positive when the acquisition cost is below $1, zero at $1, and negative above $1.
• Imperfect hedging: positions that look complementary but leave residual basis, quantity, timing, fee, liquidity, or settlement exposure.

In all four, calculate the whole basket’s payout under each possible outcome. Do not infer exposure from contract count or a shared event label.

Here is a mathematical illustration with invented prices, not a claim about any market or a recommended strategy. An event has three mutually exclusive, exhaustive outcomes, A, B, and C, and exactly one pays $1 per YES share. A trader buys one YES share of each at $0.25, $0.35, and $0.40, for $1.00 in total, with fees excluded and no unusual settlement outcome.

Winning outcome	A share ($0.25)	B share ($0.35)	C share ($0.40)	Basket payout	Basket P&L
A	+$0.75	−$0.35	−$0.40	$1.00	$0.00
B	−$0.25	+$0.65	−$0.40	$1.00	$0.00
C	−$0.25	−$0.35	+$0.60	$1.00	$0.00

The basket pays $1.00 whichever outcome wins, so under these assumptions complete coverage removes outcome-dependent variation in the terminal payout. The $0.00 P&L comes from prices that sum to exactly $1.00, not from holding every outcome. Coverage also leaves fees, execution risk, interim liquidity and funding needs, settlement-process risk, and contract-definition risk in place, and it holds only if the set is truly exhaustive and the contract rules allow no other settlement outcome. Negative-risk markets use specific adapter and conversion mechanics, so settlement rules from ordinary binary markets should not be applied to them without checking the relevant contract structure.

Negative-risk conversion. Within a negative-risk group, a No share in any market can be converted into one Yes share in every other market, and the documentation calls betting against one outcome economically equivalent to betting for all the others. The conversion applies within that group only; it does not make unrelated contracts, or the same question on another venue, interchangeable. In augmented negative-risk events the outcome list is not fixed at creation: named outcomes sit beside placeholders and an explicit Other, Polymarket’s interface does not display unnamed outcomes, and the market resolves to Other if the winner was never named. Never assume a visible outcome list is exhaustive; confirm completeness from the event’s rules and documentation. The prediction market price disagreement article covers how linked contracts can price inconsistently.

Shared settlement information. Kalshi says each market operates under its own rules, and that a contract’s terms state how its outcome is determined, what information is used, and where it comes from.⁵ Polymarket likewise says a market’s rules, not its title, define how it resolves. Contracts that name the same source can share an information dependency: a delay or an unexpected result there can reach both. Their payoffs still need not match, because thresholds, directions, and definitions can differ.

Settlement timing and venue process. Market closure, outcome determination, settlement, and the availability of funds are separate steps that need not coincide. Kalshi’s help page describes determination taking one hour to more than twelve hours after a market closes, usually depending on when the source agency’s data arrives. Polymarket’s documentation describes roughly two hours after an undisputed proposal and, if disputed, a longer process it puts at four to six days, with redemption of winning tokens as a further step. These are documented general processes, not guaranteed completion times, and neither page says when funds become available for reuse. Positions on one venue share its process, so splitting across venues changes process exposure, not the event dependency.

None of this says positions on one event always move together or always offset. A contract’s rules, not its title or its place on an event page, decide what it pays.

How do you measure total exposure in one scenario?

Ask what one scenario does to the whole book, not what each position can lose. First fix the baseline, because several numbers get called a position’s “risk” and they are not interchangeable:

Quantity	What it measures	Note
Purchase cost	What you paid to enter	For a fully paid long contract, can equal the maximum loss from entry, before fees
Current marked value	What the position is worth now	Baseline for a current-equity stress test
Capital committed	Cash tied up, including any margin	Not a loss measure
Margin or collateral posted	Funds pledged for a leveraged position	Not the maximum loss
Maximum contractual loss	The worst outcome the contract allows	Equals cost only when fully paid; under leverage it can exceed the capital posted
Planned stop-based loss	The loss if you exit at your stop level	Not guaranteed
Scenario loss	The loss under one specified scenario	Depends on the scenario and the baseline

A stop order becomes a market order once its stop price is reached and may execute at a different price, especially in a fast-moving market, so a planned stop loss is a plan, not a ceiling.⁶ In a margin account an investor can lose more than the amount invested, so margin posted is not the maximum loss.⁷

Choose one baseline per calculation and state it. Purchase cost, or the intended cost for a proposed position, answers “how much of what I put in is at risk?” A current-equity stress test starts from each position’s current marked value, because a position that has moved has more or less left to lose. Do not mix the two in one figure.

For a coherent scenario s, estimate each position’s value under it, then:

• Position P&L = scenario value − baseline value, with fees and other costs treated the same way for every position.
• Portfolio P&L = the sum of every position’s P&L in that scenario.
• Net scenario loss = max(0, −portfolio P&L).
• Gross adverse loss = the sum of max(0, −P&L) across positions: the losing legs only, with no credit for gains elsewhere.

Apply the same formulas to a subset. For one driver group, the sum of max(0, −P&L) across its positions is the cluster gross adverse loss, and max(0, −(the group’s summed P&L)) is the cluster net scenario loss. Applied to the whole book under one coherent scenario, the latter is the portfolio scenario loss. All three are loss magnitudes, not gross notional exposure, capital committed, or collateral posted.

Net scenario loss credits positions that gain in the same scenario, so calculate both legs’ P&L under the same coherent scenario and judge the offset by each position’s actual state-contingent payoff. Contracts need not share a title or an identical outcome definition to offset one another. Three cases differ:

• Exact contractual hedge: an offset that can be established from the contract terms across all the relevant outcomes.
• Scenario-dependent hedge: an offset that works in the specified scenario but may fail in another.
• Imperfect hedge: an offset with residual basis, timing, quantity, liquidity, or settlement risk.

An opposite-direction position is not automatically any of these. A $500 YES on a rate cut and a $400 NO on the same market, both bought at 50 cents, still leave a $100 net loss if there is no cut, because the quantities differ (1,000 shares against 800). The calculated net scenario P&L is also a separate question from whether a projected gain is reliable and realizable enough to receive full offset credit under a risk limit. Your written rule can credit it in full, in part, or not at all, and should say which for each kind of offset. Keep two measures apart:

• Raw net scenario loss: max(0, −sum of scenario P&L), the contractual or modeled result with every gain counted in full.
• Limit-eligible scenario loss: max(0, −(sum of losses plus each gain at the credit your policy allows)). With full credit for every offset it equals the raw figure, and with none it equals gross adverse loss.

Compare the limit-eligible figure with any limit written on that basis, and do not silently test the raw figure against a limit meant for the adjusted one, or the reverse. Show both when they differ. This article sets no universal credit percentage. Gross adverse loss stays informative even when offsets shrink the net loss, because it shows how much depends on the offsets working as intended. In the three-outcome basket above, two legs lose in every outcome (gross $0.75, $0.65, or $0.60) while the net loss is zero at those prices.

For a hold-to-settlement calculation, offsetting legs need not settle at the same moment. What matters is that both payoffs are fixed by the same outcome and will actually pay. Mark-to-market swings, margin or cash needs, and capital tied up in the slower leg are separate risks that the terminal calculation does not show.

Incremental scenario risk. For the same scenario and valuation baseline, incremental net scenario loss = net scenario loss after entry − net scenario loss before entry, using the measure your limit is written on (raw, or limit-eligible under your offset policy). Do not equate it with the proposed position’s standalone loss, because net scenario loss is floored at zero. If existing positions show +$300 in a scenario and the proposed position shows −$400, the combined P&L is −$100: net loss rises from $0 to $100, an increment of $100, not $400. When the existing positions are already losing in the scenario, as in the worked example below, the increment does equal the new position’s loss. Before entry:

1. Calculate the scenario loss before entry.
2. Recalculate it under the same scenario with the proposed position added.
3. Take the difference.
4. Check the post-entry loss against the written limit.

Step 4 is an absolute check, not an incremental one: an increment can be small or negative while total remaining exposure still breaches the limit. If the position touches more than one driver, repeat the calculation for each relevant scenario.

Worked example: every position passes, the cluster does not

Here is an illustration with invented numbers. A trader with a $10,000 bankroll writes a rule that caps any single position at 5% ($500) and any single driver at 8% ($800). All four positions are fully funded long YES contracts that pay only if the same assumption holds. The example measures from purchase cost, excludes fees, uses no leverage, and takes the adverse scenario to be that the assumption fails and each contract settles at zero. Because every position loses in this scenario and no offset is disallowed or discounted, cluster gross adverse loss, cluster net scenario loss, and the limit-eligible loss are the same figure. The limits are hypothetical, not recommendations.

Position	Cost	Share of bankroll	Running cluster loss if the assumption fails	Against the 8% driver limit
A	$500	5.0%	$500 (5.0%)	Within
B	$400	4.0%	$900 (9.0%)	Breaches
C	$300	3.0%	$1,200 (12.0%)	Breaches
D	$300	3.0%	$1,500 (15.0%)	Breaches

Each position clears the 5% cap on its own, so judged one at a time nothing looks wrong. Judged as a group, the four put 15% of the bankroll on one assumption. The driver limit is first breached when B is proposed: A alone is $500 (5.0%), but A plus B would be $900 (9.0%). The check has to happen before B is executed, because once it fills the only choices left are to hold the breach or trade out of it.

Under this hypothetical rule the options are to skip B, cut it to $300 or less, which brings the cluster to exactly $800, or reduce A first. Rows C and D show what happens if the signal is ignored. Suppose a further $400 contract on an unrelated driver is assumed to have zero P&L in this particular scenario. It would raise total committed capital to $1,900 without adding to this cluster’s measured loss under those assumptions, though it could still contribute to portfolio loss under other scenarios.

Behavioral research offers only a loose analogy for why the per-position view persists. Rabin and Weizsäcker found that 28 percent of participants in a real-stakes laboratory experiment on narrow bracketing, meaning evaluating decisions separately, chose a dominated combination.⁸ Their decisions were independent binary choices, not correlated positions, so this is not evidence about trading books.

How do overlapping drivers change the portfolio total?

A position can depend on several drivers, and the adverse scenarios for different drivers cannot always happen together, so cluster totals do not simply add. Keep three figures apart:

• Cluster loss: the gross adverse loss, the raw net scenario loss, or the limit-eligible loss of the positions under one driver, in the scenario where that driver goes against you. State which one you mean.
• Portfolio scenario loss: the P&L of the whole book under one coherent joint scenario, with each position counted once.
• Upper bound: a ceiling that is mathematically justified, not just a larger number. For fully funded long contracts, total acquisition cost, counting each position once, bounds the loss measured from purchase cost, before fees and other costs, provided each contract’s payout or scenario value cannot be negative and no position carries a further liability. It is not the loss in any one scenario. For leveraged, short, or other nonstandard positions, capital committed or margin posted is not a universal loss ceiling, and any bound needs its own justification.

Do not automatically treat the sum of cluster losses as portfolio scenario loss or as a justified upper bound. A valid bound requires explicit assumptions and complete, consistent position accounting. When the clusters form a complete, nonoverlapping partition of the portfolio’s positions, measured from one baseline, and each cluster’s loss bound holds across the relevant joint scenarios, adding those bounds gives a conservative portfolio loss bound. An arbitrary sum does not qualify: a position in two clusters is counted twice, a position in no cluster is left out, baselines can differ, and scenario definitions can be incompatible, since the scenario that hurts a YES on an outcome is the one that helps a NO on it.

Here is a small example. A $10,000 bankroll holds three fully funded YES contracts, valued at purchase cost, fees excluded. E ($500) pays only if a central bank cuts rates, F ($400) pays only if payrolls come in below a threshold, and G ($300) pays only if both happen. E and G share the rate driver, and F and G share the jobs driver. The matrix uses one stylized valuation horizon: a single hypothetical date on which each contract is marked under the scenario. A contract whose required condition has failed is worth $0. A contract that does not depend on the failed driver is assumed to stay at its baseline value, which is a mark-to-scenario assumption, not a terminal payoff: at settlement it would pay $1 or $0. A terminal matrix would instead list every outcome state and each contract’s payout quantity.

Scenario	E	F	G	Portfolio P&L versus baseline, each position once
1. Rate cut fails; payrolls held at baseline	−$500	$0	−$300	−$800
2. Payrolls fail; rate cut held at baseline	$0	−$400	−$300	−$700
3. Both fail	−$500	−$400	−$300	−$1,200

The rate cluster’s loss is $800 (scenario 1) and the jobs cluster’s is $700 (scenario 2); no position gains, so gross and net coincide. Their sum, $1,500, is a double-counted tally, not the portfolio scenario loss: it counts G twice and is the outcome of no scenario in the table. The coherent joint scenario loss is $1,200. That $1,500 is larger than $1,200 in this illustration does not, by itself, establish a valid portfolio bound: the excess comes from counting G twice. The acquisition-cost bound above, $1,200 here, is the justified one, under its stated assumptions. Scenario 3 is one joint scenario in which every required condition fails, so each contract is worth $0 and the portfolio P&L is computed position by position (−$500 − $400 − $300 = −$1,200), not by adding cluster totals. It equals the total acquisition cost, the upper bound above, because every contract loses its full cost in this scenario.

Compare each cluster’s limit-eligible figure (identical to the raw one here, since no gain is discounted) with its driver limit, and the largest portfolio scenario you examined with the portfolio limit. That is the largest among the scenarios you chose, not a proven maximum, and scenario 3 belongs in the set only if both drivers can fail together. If you cannot rule that out, include it.

How do working orders count?

A resting order is not a position, so do not count it as one, but it can become one without a new decision, so do not leave it out. Evaluate the proposed order together with every other working order that could remain live, under each fill combination the account can actually reach: open positions only, open positions plus each order that could fill, and the largest combination allowed. Feasibility depends on the real account and venue: available funds and buying power, order types and quantities, partial-fill mechanics, cancellation behavior, and execution rules. Exclude an order only when an enforceable control or a confirmed account state justifies it, such as an order type that cancels its partner automatically, funds that cannot cover both full fills, or a confirmed cancellation. A cancellation you intend to make is not a confirmed one. Check the worst feasible combination against the limit, counting a partial fill only for the filled quantity.

How a written driver rule and decision record look in practice

Write the rule before the session, when nothing is open and nothing has gone wrong.

Field	Record before entry
Driver definition	The event, release, factor, or assumption that counts as one driver, worded so it can be checked
Scenario and baseline	The adverse scenario for each driver, and whether losses are measured from purchase cost or current marked value
Limits	The largest cluster limit-eligible scenario loss allowed for one driver, and the largest portfolio limit-eligible scenario loss allowed in any one joint scenario you evaluate, each as a share of the account or bankroll
Offsets and working orders	What may be netted and on what conditions, the credit given to each kind of offset (full, partial, or none), which fill combinations are counted, and what confirmation lets you exclude an order
Check timing	When the grouping is redone, which should be before every entry
On a breach	Skip, reduce, or exit. If exceptions are allowed at all, their conditions, size, and approver are fixed here in advance

The first entry deserves care. If “driver” is defined loosely afterward, any breach can be argued away. “Positions whose payoff depends on the same reported figure” is easier to apply consistently than “positions that feel related.”

At each entry, complete a short record that keeps existing exposure and the new position’s incremental exposure separate. The last column fills it in for position B.

Field	What to record	Example: position B
Position and driver	The order and the driver it depends on	$400 long YES; the shared assumption
Scenario and baseline	The adverse scenario, baseline, and cost treatment	Assumption fails, cluster settles at zero; purchase cost, no fees, no leverage
Existing and incremental exposure	Cluster limit-eligible scenario loss before entry (and the raw net figure if it differs), and what this position adds	$500 before entry (A); $400 added, because A already loses in this scenario
Cluster limit-eligible scenario loss after entry	Against the driver limit, with the raw net figure alongside if it differs	$900 (9.0%) against 8%: breach; no offsets, so raw and limit-eligible are equal
Portfolio scenario loss	Same joint scenario, each position once, against the portfolio limit	$900 (9.0%) if no other position is affected; this example sets no portfolio limit
Working orders and offsets	Fill combinations counted, and any gain credited, in full or in part, with the reason	None
Decision	Enter, reduce, skip, or exception, and why	Not entered at $400: skip, cut to $300 or less, or reduce A first

A knowingly accepted breach is still a breach. If your written policy provides for exceptions, an approved exception is recorded as one under that policy, separately from compliant entries. Without such a policy, the answer to a breach is to skip, reduce, or exit.

Common ways aggregate exposure goes wrong

Counting contracts instead of payoffs. Six contracts on one event can lose together, offset one another, or leave an outcome uncovered. Evaluate the whole group’s payoff under each outcome.

Treating a different venue as a different risk. Splitting across platforms or brokers changes execution, counterparty, and settlement-process details, not the underlying event dependency.

Mixing baselines. An entry-cost loss for one position added to a current-marked loss for another belongs to neither calculation.

Adding cluster totals into one portfolio number without checking the accounting. A position in two clusters is counted twice, one in no cluster is left out, and the adverse scenarios for different drivers may not be able to happen together. Do not automatically treat the sum as portfolio scenario loss or as a justified upper bound; it can be a conservative bound only under the explicit conditions above.

Calling something a hedge without checking the payoffs. An offset helps only if it actually pays in the scenario you fear, in enough quantity, on settlement terms that line up, after fees. It need not share a title or outcome definition with the position it offsets, but you have to calculate both legs’ P&L under the same scenario and know whether the offset is exact, scenario-dependent, or imperfect. A near-match leaves a gap where you needed cover, and even a valid terminal offset leaves mark-to-market and cash needs while both legs are open. Test limits against the limit-eligible loss your written policy defines, and keep the raw net figure visible when the two differ.

Treating a calm-period correlation as fixed. Correlations estimated in quiet markets can change, so do not rely on them alone for stress analysis.

Adding the same thesis after a win. Conviction rises after a good result. If the new position shares the driver, the cluster limit is what should decide, not the mood.

Leaving working orders out. Counting only open positions, or dropping an order because you plan to cancel it, lets several resting orders fill together and breach the limit without any new decision.

Recalculating the limit afterward, or excusing a breach. Setting the limit after seeing the loss turns the rule into a justification, and so does logging a breach as “accepted.” Only an exception your written policy allowed in advance is an exception.

Turn it into a reviewable decision

At a scheduled review, use the decision records rather than memory. Ask whether each new entry had a driver written before it was placed, whether the cluster and portfolio scenario losses were calculated before the entry, whether any entry breached a limit and, if so, whether it was recorded as a pre-authorized exception or as a plain breach, and how often the same driver appeared across positions in the period.

A large loss on one cluster is not proof of a process failure, and a run of small gains inside a heavily stacked cluster is not proof of a sound one. A cluster that resolves against you once is one observation. Judge the process, the written driver and the checked limit, rather than the result of one event.

Where Costante fits

Costante’s relevance here is limited, and it sits in the process around the decision, not the exposure arithmetic. It is a behavioral performance system for discretionary traders who want a process observable before, during, and after a decision. It supports session planning, self-defined behavioral guardrails, pre-trade and in-session checks, low-friction trade and behavioral logging, and structured review. Guardrails make a trader’s own boundaries visible and reviewable, and the trader remains responsible for acting on them. Grouping positions by driver, defining scenarios, calculating cluster and portfolio scenario loss, comparing the result with a driver limit, and keeping the decision record are manual steps outside Costante.

Costante does not connect to brokers or exchanges, execute or route orders, or block trades, and it does not decide whether a trade should be placed. Calculating correlation or scenario loss, identifying which positions share a driver, aggregating exposure across positions or venues, and monitoring working orders are not among its described functions, so this article does not assume it does any of them. The driver definition, the limits, and every entry decision remain the trader’s own.

Frequently asked questions

What is correlated event exposure?

The combined loss exposure of positions that depend on the same event, data release, factor, or assumption, measured under one defined scenario. Each position may fit its own size rule while one shared outcome can hurt several of them at once, so the cluster’s limit-eligible scenario loss (the raw net scenario loss when every offset is credited in full) is the number to check against a limit. Positions on one event can also offset one another.

How do I know whether two positions share a driver?

Write down what each needs to be true in order to pay, then compare the statements. For event contracts, read each contract’s rules for its outcome, verification source, and timing instead of inferring them from titles. For traded instruments, check whether the positions win under the same condition, and confirm any statistical relationship against your own data rather than assuming it.

Can I add each cluster’s loss to get my portfolio loss?

Not automatically, either as the portfolio scenario loss or as a bound. A position can belong to several clusters, some may belong to none, and the adverse scenarios for different drivers may not be able to happen together. Calculate the whole book’s P&L under one joint scenario at a time, counting each position once. A justified upper bound needs explicit assumptions and complete, consistent position accounting. If the clusters completely partition the positions, use one baseline, and each cluster’s loss bound holds across the relevant joint scenarios, adding those bounds gives a conservative portfolio bound. For fully funded long contracts with nonnegative payouts and no further liability, the total acquisition cost, counting each position once, bounds the loss from purchase cost before fees.

Does a position on the other side cancel the exposure?

Only if it actually pays in the scenario you fear, in enough quantity, on settlement terms that line up, after fees. It need not share a title or outcome definition with the position it offsets, and an opposite-direction position is not automatically a hedge. Calculate both legs’ P&L in the same scenario, then classify the offset as exact (established by contract terms across the relevant outcomes), scenario-dependent (works in that scenario, may fail elsewhere), or imperfect (residual basis, timing, quantity, liquidity, or settlement risk). For a risk limit, your written policy decides how much of a projected gain counts, so check the limit-eligible loss and keep the raw net loss visible when they differ. A basket covering every outcome of an exhaustive, mutually exclusive set can fix the terminal payout, but a fixed payout is not a fixed profit: the result is that payout minus the total acquisition cost and other costs. Execution, funding, fee, settlement-process, and mark-to-market risks also remain while the legs are open.

Does spreading positions across platforms diversify the risk?

Not by itself. What matters is how the positions’ payoffs relate across outcomes. Positions on different platforms that need the same event to resolve the same way share that exposure whatever the venue, and each platform adds its own operational risks.

How many positions are too many?

There is no fixed count. What matters is what the positions’ combined payoff is under the scenarios you test, and how much you would lose in the one where a shared driver fails. The effective-bets table shows why, under its equal-weight, common-correlation assumptions, many similar positions can carry the variance of only a few. It is not a count of independent events.

Sources

Costante provides educational workflow tools, not financial advice. Trading involves risk.

Footnotes

1. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. Peer-reviewed journal article. Cited only for the point that a portfolio’s variance depends on the covariances among its holdings as well as on their individual variances. It does not address event contracts or loss limits. The same relation is stated, and credited to this paper, in Rubinstein, M. (2002). Markowitz’s “Portfolio Selection”: A Fifty-Year Retrospective. The Journal of Finance, 57(3), 1041–1045. ↩

2. Thorp, E. O. (2006). The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market. In S. A. Zenios & W. Ziemba (Eds.), Handbook of Asset and Liability Management, Volume 1, Chapter 9, pp. 385–428. Elsevier. See section 6, Example 6.2. The table was computed from the chapter’s closed-form result with m = 0.10. It depends on that stylized model, a known edge, and known probabilities, and the chapter notes that correlation alone does not determine the answer in general. ↩

3. Longin, F., & Solnik, B. (2001). Extreme Correlation of International Equity Markets. The Journal of Finance, 56(2), 649–676. Peer-reviewed journal article. The finding concerns international equity markets: correlation increases in bear markets but not in bull markets, and is not related to volatility per se. It is not a universal lower bound and not evidence about prediction markets. ↩

4. Polymarket Documentation: Negative Risk Markets, Markets & Events, and Resolution. Platform documentation, read September 19, 2026. It describes mechanics of one venue and can change. ↩

5. Kalshi Help Center. Market Outcomes. Platform help page, read September 19, 2026, supplemented by the Market Rules page. It describes mechanics of one venue and can change. The pages do not state payout timing or when funds become available. ↩

6. U.S. Securities and Exchange Commission, Investor.gov. Stop Order. Glossary entry, read September 19, 2026. Cited only for the point that a stop order becomes a market order once the stop price is reached and may execute at a different price, especially in a fast-moving market. The entry describes stock orders, so check the rules of the venue and order type you use. ↩

7. U.S. Securities and Exchange Commission, Investor.gov. Investor Bulletin: Understanding Margin Accounts. Investor bulletin, read September 19, 2026. Cited only for the point that in a margin account an investor can lose more than the amount invested. It concerns securities margin accounts, and other leveraged products have their own terms. ↩

8. Rabin, M., & Weizsäcker, G. (2009). Narrow Bracketing and Dominated Choices. American Economic Review, 99(4), 1508–1543. Peer-reviewed journal article. The theoretical result concerns pairs of independent binary decisions, and the 28 percent figure comes from a real-stakes laboratory experiment that replicates Tversky and Kahneman’s (1981) experiment. It is not evidence about correlated trading positions. ↩