Will Natural gas price on Oct 29, 2026 at 5:00 PM EDT close above $2.950?

Natural gas closed 2026-10-28 at $2.817 (d1). To resolve YES, the 10/29 5PM EDT 1-min candle must print above $2.950, requiring a one-day log-return of +4.59%. Daily return history (d2, n=123) has mean≈0, sd=2.70%, with empirical 95th percentile at +4.29% and max observed +8.68% — so the required move sits just above the historical 95th percentile, implying a base-rate probability near 4-5% from the empirical distribution. Two most recent sessions were unusually strong (+3.59%, +4.35%), consistent with early-season withdrawal/cold-weather demand tightening (d6 shows storage draws deepening to -242,693 by 10/20, the most negative in the 8-week sample), which supports some upside skew. But return autocorrelation is essentially zero (0.02), so yesterday's rally carries little statistical predictive power for today's move — the curve/returns series behaves close to a random walk at daily frequency. I therefore anchor near the empirical tail probability (~4-5%) and nudge modestly higher for the physical tightening backdrop and the slight positive skew (0.19) in the return distribution, landing at 8%.

Own objection: If a cold-weather forecast update or a supply disruption hit overnight/pre-market on 10/29 (unobservable in this data snapshot taken at 11:42 UTC, before the NYMEX settlement and before the Kalshi 5PM EDT window), the two prior days' sharp rallies (+3.6%, +4.35%) could be the start of a genuine volatility regime shift driven by an early cold snap, not noise - in which case the random-walk/autocorrelation argument understates the true probability of a third consecutive large up-day, and 8% could be too low.

Natural gas settled 2.817 on 10/28 (d1 last). To resolve YES, the 5PM EDT close on 10/29 must exceed 2.950, a +4.72% move from the prior settle within roughly one trading day. Historical 1-day return distribution (s2/s3, n=61) has sd=2.77%, mean≈0, and the empirical 95th percentile of returns is +4.45% — the required move (+4.72%) sits just beyond the observed 95th percentile, placing it in the extreme right tail. Momentum exists at the price level (d1 autocorr=0.89, trivial for a trending series) but NOT in returns themselves (s2 autocorr_1≈-0.0008, s5 corr=-0.117 CI crossing zero, not significant), so the two prior consecutive +3.6%/+4.3% daily gains (d2 last) do not statistically predict a third big up-day. No HDD/CDD or weather-anomaly data was available (d5/d6 failed — empty after seasonal anomaly transform), so I cannot support a cold-snap/demand-shock catalyst with data; absent such a catalyst, a one-day move of this magnitude is a tail event.

Own objection: Natural gas is a famously fat-tailed, news-driven commodity, especially entering the Nov-Mar heating season; a single cold-weather forecast revision, supply disruption, or storage report surprise can produce >5% intraday moves that a 61-day historical window (which includes calmer stretches) will underestimate. The two most recent days already show +3.6% and +4.3% moves — back-to-back near-tail events — suggesting the market may already be in an elevated-volatility regime (e.g., early cold snap repricing) rather than the average regime reflected in s2/s3's full-sample sd of 2.77%. If realized vol has shifted to something like 4-5% daily, the required move becomes far more plausible (roughly 1 sd), and probability could reasonably be 15-20% rather than 5-7%.

Base rate for a daily NG return ≥4.6% (the move required to clear $2.950 from the last close of $2.817) is approximately 5% under the unconditional, near-Gaussian historical return distribution (sd≈2.79%). I update modestly upward to ~13% because the two most recent days (Oct 27, 28) already realized back-to-back large up-moves (+3.65%, +4.45%), consistent with either weather-driven momentum or short-covering off extreme net-short COT positioning — but this update is capped because (a) lag-1 return autocorrelation is statistically zero in the full sample, (b) the COT-positioning regression is insignificant (p=0.819), and (c) no conditional-volatility (GARCH) model was available to properly quantify the vol-clustering effect visible in the last 3 observations. This remains primarily a base-rate-anchored, low-probability NO lean with a modest upward momentum/positioning adjustment.

Own objection: The unconditional-normal base rate (~5%) assumes i.i.d. Gaussian-ish returns, but the data show the last two days delivered +3.65% and +4.45% moves — already 1.3–1.6 sigma events back-to-back. This is classic ARCH/vol-clustering behavior that a static full-sample sd underestimates for 'today'. If realized vol has regime-shifted upward (e.g., to 3.5–4% daily sd, consistent with a weather-driven NatGas rally), the true conditional probability of another +4.6% day could be 2-3x the unconditional estimate, meaning my 0.13 could still be too low. I cannot test this directly since a GARCH/conditional-vol model was not run (only unconditional moments and a failed COT regression, s6, which was insignificant and uninformative either way).

Natural gas closed Oct 28 at $2.817, so reaching above $2.950 by 5PM EDT Oct 29 requires a ~4.7% one-day rally. Historical daily log-return distribution (502-day sample) shows this magnitude sits around the 83rd-85th percentile of up-moves, giving a base rate near 14-16%. Recent two-day momentum (+3.65%, +4.45% log returns) is the main bullish fact, but autocorrelation of daily returns is essentially zero (-0.076), so momentum carries little statistical persistence. Mean next-day return is statistically indistinguishable from zero (bootstrap CI95 [-0.31%, +0.58%]), and the return distribution is negatively skewed (-0.87), meaning large up-moves are structurally less common than large down-moves despite high kurtosis (19.5) fat tails on both sides.

Own objection: Natural gas is notorious for sharp single-day moves driven by weather forecast revisions (cold outbreak forecasts, storage surprises) that aren't captured in the backward-looking return distribution; if a cold-snap forecast update hits markets intraday on Oct 29, a >4.7% rally is entirely plausible and has happened before in this very dataset (max observed daily return +28.9%). The two most recent days already show momentum building (+3.65%, +4.45%), and if that accelerates even slightly further, $2.95 is within reach without needing a historically extreme move.

The $2.950 strike sits roughly 4.7% above the last observed settle (2.817 on 10/28), a move that is large relative to the market's own daily volatility. Treating the daily log-return distribution as roughly normal (mean ~0, sd ~2.77%, near-zero autocorrelation per d2/s2), a one-day move of +4.7% is about a 1.6-1.7 sigma event, implying a tail probability in the mid-single digits. There is no evidence of return momentum (autocorr_1 ≈ -0.0008) that would make continuation of the last two up-days (+3.6%, +4.35%) more likely than a random walk would predict, so I do not add a momentum premium beyond the base statistical tail probability.

Own objection: Natural gas is notoriously fat-tailed and headline-driven (weather forecasts, storage reports, cold snaps) — a single overnight weather revision or storage surprise can produce 5-10% single-day moves that a Gaussian sd-based model will systematically underprice; the two most recent sessions (+3.6%, +4.35%) already show the kind of acceleration that a normal-distribution tail estimate understates, and if this is driven by an emerging cold-weather narrative, the true probability could be materially higher than my ~6% estimate, perhaps 12-18%.

Starting from the causal-skeptic position: none of the available evidence establishes a mechanism (weather variable d4, or the n=16 correlate in s7) that reliably drives natural gas returns — both relationships are statistically insignificant or fragile (small-n, high autocorrelation). The two-day rally preceding 2026-10-28 is consistent with noise/mean-reversion from an oversold low rather than a persistent causal trend, since return autocorrelation is ~0. Treating the market as close to a random walk with sd≈2.7% daily, the ~4.72% gain needed to go from the last known close (2.817) to above 2.950 by 5pm EDT today is a tail event (above the empirical 95th percentile of daily moves), and we lack any evidence of a specific new catalyst that would justify assigning it higher-than-tail probability. The biggest risk to this view is that we are missing intraday price action already in progress as of 11:42 UTC.

Own objection: The last two observed sessions (10/27 +3.59%, 10/28 +4.35%) already show a strong rally that could reflect a genuine fundamental shift (e.g., a cold-weather forecast update) rather than noise, and if that trend has continued into the morning of 10/29 (unobserved to us), the market could already be very close to or above 2.950 before 5pm EDT — my estimate could be too low simply because I lack the current intraday price.

• to Analyst Bmethodological 0.2B computes the return distribution from only n=61 daily observations (sd=2.77%, q95=4.45%) versus my n=123 (sd=2.70%, q95=4.29%). Both land near the same ~4-5% base rate, but B's smaller window is more sensitive to recent sampling noise and could understate or overstate tail frequency depending on which regime it captures; I don't think the conclusion changes much, but the methodological choice of window length isn't justified by either of us.
• to Analyst Cinterpretation 0.3C starts from essentially the same ~5% base rate I do but inflates to 13% citing back-to-back large up-days as a momentum/short-covering signal, despite C's own admission that lag-1 autocorrelation is statistically zero and the COT regression is insignificant. I read the same facts (two big up-days, zero autocorrelation) as justifying a much smaller nudge (my 8% vs C's 13%) because there's no statistical evidence supporting the momentum story C leans on.
• to Analyst Dfactual 0.6D reports a return distribution with negative skew (-0.87) and high kurtosis (19.5) from a 502-day sample, implying large up-moves are structurally rarer than down-moves and giving q95=+7.35%. This contradicts my sample's positive skew (0.19) and much lower q95 (4.29%). We can't both be describing the same underlying return series accurately - one of us has a materially different distributional shape, which drives D's base rate (15.5%) far above mine (4.5%). This is a first-order factual disagreement that needs reconciling since it is the dominant driver of D's 3x-higher probability.
• to Analyst Dmethodological 0.4D derives the base rate by linearly interpolating between the 75th (2.29%) and 95th (7.35%) percentiles to place the 4.72% required move at the ~84th percentile. Linear interpolation between percentiles is invalid for a fat-tailed distribution (kurtosis 19.5 per D's own numbers) - the percentile curve is highly convex in the tail, so this method likely overstates the probability of the specific move needed.
• to Analyst Einterpretation 0.3E treats the 13-day mean (2.951) and recent decline-then-bounce pattern as evidence that a breakout above 2.950 is 'the exception rather than the trend,' leaning bearish on top of the base rate. I weight the physical-market signal (deepening storage withdrawals into late October, d6) more heavily as supporting continued upside pressure, which E does not incorporate at all (E's evidence set has no storage/fundamental data).
• to Analyst Fmethodological 0.2F cites the price level's historical percentile (2.950 sits at the 70-75th percentile of an 83-day price distribution) as evidence, but this is a level-based static-range statistic and is not informative about the probability of a single day's return threshold being crossed - it conflates a price range question with a return/move question. I don't incorporate this type of evidence because it doesn't bear on a one-day move probability.
• Same evidence as another seat: Daily log-return distribution with mean≈0, sd≈2.70-2.79%, and q95≈4.29-4.45% (used in various forms by Analyst B's s3, Analyst C's s2, Analyst E's s2, and Analyst F's s3, matching my own s2); Near-zero lag-1 autocorrelation of daily returns (~0.001-0.02), cited by Analyst B, Analyst C, Analyst E, and Analyst F as well as my own evidence; Last close of $2.817 on 10/28 as the anchor price requiring a ~4.6-4.7% move to clear $2.950, used identically by every analyst (d1); The two most recent sessions' large up-moves (+3.6-3.65%, +4.3-4.45%) cited by every analyst including me as the key momentum/regime-shift risk factor in the 'own objection' section

• to Analyst Afactual 0.3A's return series uses n=123 observations with sd=2.70% and q95=+4.29%, while my sample (n=61) gives sd=2.77% and q95=+4.45%. These are supposedly draws from the same underlying NG daily-return history but produce materially different tail estimates — the underlying dataset/window is not consistent across our tool pulls, which matters because the final probability is highly sensitive to the exact tail percentile used.
• to Analyst Afactual 0.3A cites d6 as containing usable storage-withdrawal data (deepening draws to -242,693 by 10/20) supporting a tightening/weather narrative, whereas in my data pull d5/d6 (weather/HDD-anomaly series) came back empty after the seasonal anomaly transform. Either the tool returned different content to different analysts or one of us mis-specified the data request; this should be reconciled before leaning on any storage/weather catalyst.
• to Analyst Ainterpretation 0.3A nudges the ~4.5% empirical base rate up to 8% citing the storage-withdrawal backdrop as a slow-moving tightening signal. I find this unpersuasive as a same-day catalyst — a weekly storage trend has no mechanical link to a single day's 1-minute settlement candle — so I'd attribute less of the upward adjustment to that evidence than A does.
• to Analyst Cmethodological 0.2C computes the base rate via a normal-distribution z-score approximation (z≈1.65) on sd=2.794% rather than the empirical bootstrap percentile I used. For a fat-tailed, weather-driven commodity like NG, a Gaussian approximation is likely to understate true tail probability relative to an empirical/bootstrapped distribution, so I'd expect C's 'clean' 5% base rate to be a slight underestimate even before any momentum adjustment.
• to Analyst Cinterpretation 0.3C updates from 5% to 13% largely on the back-to-back large-up-day pattern despite explicitly noting autocorrelation is statistically zero and the COT-positioning regression (s6) was insignificant (p=0.819). I think leaning that heavily on a vol-clustering narrative when the only quantitative test of it (COT regression) failed is an over-update relative to the evidence actually in hand.
• to Analyst Dfactual 0.6D's return distribution (n=502) reports q95=+7.35%, skew=-0.87, kurtosis=19.5 — sharply different from the q95≈4.3-4.45% and near-zero/slightly-positive skew (0.1-0.2) reported by A, C, E, and myself on smaller/different samples. A q95 of 7.35% vs ~4.4% is not a minor discrepancy; it changes the base rate from ~4.5% to ~15.5%, roughly 3x. One of these distributions mischaracterizes recent NG volatility, and D's longer window may be pulling in a historically more volatile regime (e.g., a past winter cold-snap period) that isn't representative of current conditions.
• to Analyst Dinterpretation 0.3D's thesis notes negative skew (large down-moves more common than large up-moves) yet still lands on a base rate of 15.5% for an up-move purely from unconditional percentile interpolation, seemingly without adjusting for the stated negative skew working against an upside breach. That's an internal tension in D's own reasoning that I don't resolve the same way — if skew is genuinely -0.87, the probability of a +4.7% up-move should be discounted relative to a symmetric estimate, not left at the raw interpolated percentile.
• to Analyst Einterpretation 0.1E and I rely on essentially the same inputs (sd≈2.77%, near-zero autocorrelation, n=61) and both frame the move as a ~1.6-1.7 sigma event with base rate near 5%, so there is minimal substantive disagreement here — mainly a difference in how much weight to give the 'declining 13-day regime until last two sessions' narrative, which E treats as evidence against a breakout while I treat as largely uninformative given the zero-autocorrelation finding.
• to Analyst Finterpretation 0.1F's s1 evidence (2.950 sits at the 70th-75th percentile of the 83-day *price* distribution) is presented as a secondary data point, but F itself flags it as not directly relevant to a single day's return question; I agree with that self-correction and don't weight it, so this is a non-disagreement worth noting rather than a real split.
• to Analyst Fassumption 0.3F treats the lack of a demonstrated HDD/weather causal mechanism (d4, s7, both insignificant/small-n) as grounds for near-zero weight on a weather catalyst this specific day. I'd push back methodologically: absence of a statistically significant correlation in a small, likely lagged weekly HDD series is weak evidence of absence for an intraday weather-news catalyst, since same-day forecast-revision shocks wouldn't show up in that kind of regression at all.
• Same evidence as another seat: s2/s3 bootstrap of ~61 daily NG returns (sd≈2.77%, mean≈0, q95≈+4.45%) — same underlying series and sample window used by Analyst E and largely overlapping with Analyst F's n=123-165 return stats; d1 last close of 2.817 on 2026-10-28 as the anchor price for computing the required +4.7% move — used identically by Analysts A, C, D, E, and F; Finding that lag-1 return autocorrelation is statistically indistinguishable from zero — reported independently (but as the same underlying fact) by Analysts A, C, E, and F

• to Analyst Amethodological 0.3A anchors on n=123 sample (sd=2.70%, q95=4.29%) and lands at 8% without any explicit quantification of the momentum/vol-clustering adjustment beyond a qualitative nudge — same directional logic as mine but less transparent about how much of the 8% is base rate vs adjustment. I treat this as a different sample window producing a slightly lower base rate (4.5% vs my 5%) and a smaller, less-explained upward adjustment (8% vs my 13%); given my own cap on the adjustment was explicit (momentum stat insignificant, no GARCH), this is a difference in how much weight to place on the same uncertain signal, i.e. interpretation of the same evidence.
• to Analyst Afactual 0.2A cites deepening storage withdrawal (d6, -242,693 by 10/20) as corroborating tightening/upside skew. I did not have or use this evidence; if real, it is a legitimate independent signal I am missing, which could argue my 13% is too low if the market is genuinely supply-tightening rather than just mean-reverting from an oversold level.
• to Analyst Binterpretation 0.3B explicitly raises the same vol-regime-shift objection I raise (recent 2 days as near-tail events suggesting elevated realized vol), explicitly estimates that under a 4-5% conditional sd the probability could reasonably be 15-20%, yet reports a final point estimate of only 0.045 — i.e., B's stated objection is not reflected at all in B's own number, whereas I incorporated a partial (not full) upward adjustment to 13%. This is the same evidence/reasoning, different degree of update.
• to Analyst Dfactual 0.6D reports a negatively skewed daily-return distribution (skew=-0.87, 502-day sample) and a 95th percentile up-move of +7.35%, while my sample (172 obs) shows mild positive skew (+0.105) and a much smaller required-move-to-percentile mapping. These are materially different distributional shapes from what should be roughly the same underlying instrument, suggesting either a longer/different historical window with different volatility regimes (e.g., including past extreme down-move shocks) or a methodological difference in how returns were computed/cleaned. This drives D's base rate (15.5%) far above mine (5%), the largest base-rate disagreement on the committee.
• to Analyst Dmethodological 0.5D's base rate of 15.5% comes from linearly interpolating the required move's percentile rank between the empirical 75th (+2.29%) and 95th (+7.35%) percentiles. Linear interpolation between percentiles is a poor approximation deep in a fat right tail (kurtosis=19.5 per D's own data) — tail percentiles compress non-linearly, so this interpolation likely overstates the true percentile/probability of the +4.72% move relative to a proper tail-fit (e.g., EVT/GPD) or direct empirical count.
• to Analyst Emethodological 0.3E frames the base rate as the probability of a move of +4.7% 'in either direction,' which conflates a two-sided tail probability with the one-sided (upside-only) event the question actually requires. If E's q95=+4.35% reported is the upper one-sided quantile this is fine, but the stated 'either direction' framing is internally inconsistent with using a single upper quantile, and risks either double-counting or mislabeling the tail used, versus my explicit one-sided framing (probability return > +4.61%).
• to Analyst Einterpretation 0.3Like B, E explicitly entertains that the true probability 'could be materially higher, perhaps 12-18%' given the two recent large up-moves, but reports a final estimate of 0.05 with no adjustment at all — the stated reasoning and the reported number are disconnected, whereas I applied a partial adjustment (5%→13%) that is at least directionally consistent with my own objection.
• to Analyst Fassumption 0.1F's evidence s1 (2.950 sits at ~70th-75th percentile of the 83-day price level distribution) is explicitly flagged by F as not directly relevant to a single-day-return question, and I agree it is a weak/irrelevant frame (price-level percentile over 83 days says little about the probability of a specific one-day percentage jump); I would not include it as supporting evidence at all, whereas F lists it as evidence s1 in the thesis.
• to Analyst Cassumption 0.0(Self-check, no disagreement filed against self.)
• Same evidence as another seat: Daily log-return distribution with mean≈0, sd≈2.70-2.79%, near-zero lag-1 autocorrelation, used independently by Analysts A, B, E, and F (and by me as s2) to establish the unconditional ~4.5-5% base rate for a >4.6-4.7% one-day move — this is effectively the same statistical object computed on overlapping historical windows, not independent confirming evidence.; The last-close anchor of $2.817 on 2026-10-28 and the resulting required move of roughly +4.6% to +4.72% to clear $2.950, cited identically (d1) by every analyst including me.; The observation that the two most recent sessions (Oct 27, Oct 28) realized large consecutive up-moves (~+3.6% and ~+4.35-4.45%), used by every analyst (A, B, D, E, F, and me) as the central bullish/momentum fact and as the basis for each analyst's 'own objection' about a possible volatility-regime shift — this is the same single data observation repeated six times, not six independent signals.

• to Analyst Afactual 0.6A's return sample (n=123) reports sd=2.70% and q95=+4.29%, implying the +4.59% threshold sits just above the empirical 95th percentile (base rate ~4-5%). My 502-day sample shows q95=+7.35%, q75=+2.29%, interpolating the required move to only the ~84th percentile (base rate ~15-16%). These are materially different tail estimates for what should be the same underlying asset's return distribution — the discrepancy is almost certainly driven by sample window length (123 days vs 502 days), with A's shorter window likely missing the fat-tail events (e.g. my observed max +28.9%) that drive my higher 95th/75th percentile values.
• to Analyst Ainterpretation 0.3A nudges the base rate upward (0.045 -> 0.08) citing deepening storage withdrawals (d6) as a slow-moving tightening signal supporting upside skew. I did not incorporate storage data at all and view it as a low-information input for a 1-day settlement question; I'd weight it near zero rather than as a positive nudge.
• to Analyst Bfactual 0.6B's n=61 sample gives sd=2.77%, q95=+4.45%, placing the +4.72% threshold just beyond the empirical 95th percentile (base rate ~4.5%). My longer 502-day sample puts the same magnitude move at only the ~84th percentile (base rate ~15.5%) because my distribution has much fatter right-tail (q95=+7.35%) and heavier overall kurtosis (19.5 vs implied near-normal in B's framing). This is a real factual disagreement about the correct volatility/tail regime to condition on, not just interpretation of the same numbers.
• to Analyst Cfactual 0.7C reports skew=+0.105 and excess kurtosis=0.259 (near-Gaussian) for the return distribution, directly contradicting my measured skew=-0.87 (meaningfully negative) and kurtosis=19.5 (strongly fat-tailed). If both are drawn from the same underlying NG return series, one of these moment estimates is wrong or they are computed on non-overlapping/different-length windows — this materially changes the normal-approximation z-score approach C uses (z≈1.65, ~5%) versus my empirical-percentile interpolation (~15-16%).
• to Analyst D-vs-Emethodological 0.6E treats daily log-returns as approximately normal (sd=2.77%, near-zero autocorrelation) to get a 1.6-1.7 sigma tail probability (~5-6%). Given my data show skew=-0.87 and kurtosis=19.5, a normal approximation is a poor model for the right tail specifically — asymmetric, fat-tailed distributions can have a lower-than-normal probability in the extreme right tail even while having a much higher 95th percentile than a normal distribution would predict at the same sd, so the z-score method and the percentile-interpolation method are not interchangeable here, and likely understate the true base rate by using a parametric shortcut on manifestly non-normal data.
• to Analyst Fassumption 0.5F models the market as 'close to a random walk' with sd≈2.7% and treats the required move as above the 95th percentile (base rate ~4.5%), implicitly assuming the shorter n=123-165 sample's volatility is the representative regime for today. I reject that assumption given the discontinuity between my 502-day distribution's heavier tails (q95=+7.35%, kurtosis=19.5) and the shorter samples' tamer tails (q95≈4.3%) — the choice of estimation window materially drives the base rate, and I think the longer window is the more defensible lower bound on tail risk for a weather-sensitive commodity entering heating season.
• Same evidence as another seat: Last close of $2.817 on 2026-10-28 used as the anchor price for computing the required move to $2.950 (used identically by Analysts A, B, C, E, F, and myself).; The two most recent daily returns (~+3.6% to +3.65% on 10/27 and ~+4.3% to +4.45% on 10/28) cited as the recent momentum/rally fact by Analysts A, B, C, E, F, and myself.; Near-zero daily return autocorrelation (reported variously as -0.0008, 0.001, 0.02, -0.076, -0.117) used by nearly all analysts, including myself, to argue momentum has little statistical persistence for predicting a third consecutive large up-day.

• to Analyst Afactual 0.2A reports sd=2.70% and skew=0.19 on an n=123 sample, slightly different from my n=61 sample (sd=2.77%, near-zero implied skew). Likely a different lookback window than mine; not corroborated by my data.
• to Analyst Ainterpretation 0.4A nudges the base rate from ~4.5% to 8% citing deepening storage withdrawals as justification. I treat this as unsupported for a 1-day horizon — weekly storage data is slow-moving with no demonstrated same-day predictive power, so I would not apply this adjustment.
• to Analyst Binterpretation 0.0B's final probability and reasoning are essentially identical to mine (same sd=2.77%, same near-zero return autocorrelation, same conclusion). No substantive disagreement.
• to Analyst Cinterpretation 0.6C adjusts the base rate from 5% up to 13% citing back-to-back large up-days as suggestive of momentum/short-covering, despite C's own admission that lag-1 autocorrelation is statistically zero and the COT regression was insignificant (p=0.819). I view this as an unjustified discretionary inflation not supported by any statistically significant evidence C presents.
• to Analyst Dfactual 0.6D uses a 502-day sample reporting 95th pct=+7.35%, skew=-0.87, kurtosis=19.5 — materially different moments (especially negative skew and a much fatter right tail at the 95th percentile) than my 61-day sample (sd=2.77%, roughly symmetric) and than A/C's reported slight positive skew (~0.1-0.2). These cannot both accurately describe the same underlying return series; I consider my shorter, more recent sample more relevant to the current volatility regime, but flag this as an unresolved factual conflict in the committee's evidence base.
• to Analyst Dmethodological 0.5D interpolates between the 75th (+2.29%) and 95th (+7.35%) percentiles to get a ~15.5% base rate for a +4.72% move, but percentile interpolation on a fat-tailed, non-linear tail is unreliable — the tail density between the 75th and 95th percentile is not uniform, and this method likely overstates the true probability mass in the 4.6-4.8% region given kurtosis=19.5 (tail mass concentrated further out).
• to Analyst Finterpretation 0.0F's analysis and final reasoning (sd≈2.77%, ~4.5% base rate, no causal catalyst evidence) is effectively the same as mine with no material disagreement.
• to Analyst Dinterpretation 0.3D frames the last two days as 'a two-day bounce, not an established uptrend, after a steeper multi-day decline' — a reasonable qualitative read, but it sits in tension with D landing on the highest quantitative base rate (15.5%) of the entire group.
• Same evidence as another seat: Daily log-return distribution with sd≈0.0277 and near-zero autocorrelation, used by Analyst B and Analyst F in near-identical form to my own s2 evidence — same underlying ~60-day return sample.; Empirical tail probability of a 1-day move exceeding ~4.5-4.7% computed from the same short-window (n≈61-65) daily return distribution, used by Analyst B and Analyst F yielding the same ~4.5% base rate I computed.

• to Analyst Dmethodological 0.6D uses a 502-day sample yielding a base rate of ~15.5% for a +4.72% move, far above the ~4.5-5% base rates everyone else gets from 61-172 day windows. This isn't just a sample-size quirk — it implies the volatility regime is non-stationary across windows, which is exactly the kind of regime-dependence that should be tested (e.g., splitting the 502-day sample into sub-periods) rather than taken at face value as 'the' base rate. Mixing calm and extreme-volatility NG regimes into one unconditional distribution overstates the relevant base rate for the current (apparently calmer, sd~2.7%) regime that the shorter-window analysts are using.
• to Analyst Dinterpretation 0.4D notes negative skew (-0.87), meaning large up-moves are structurally rarer than down-moves, yet still lands on the highest base rate (15.5%) and keeps an elevated final estimate. This internal tension (skew argues down, percentile interpolation argues up) isn't reconciled — it should push the estimate toward the lower end, not be set aside.
• to Analyst Cmethodological 0.6C explicitly finds autocorr_1≈0.001 (no momentum) and an insignificant COT regression, yet still adjusts the base rate upward from 5% to 13% based on 'vol clustering' that was never tested with a GARCH model or even a simple rolling-volatility comparison. This is an unfalsified, narrative-driven adjustment — the same two anomalous days are used both as the thing-to-be-explained and as justification for raising the probability, which is circular.
• to Analyst Bmethodological 0.5B's own-objection reasons that if realized vol has shifted to 4-5% sd, probability should be 15-20%, but no test (e.g., comparing realized vol over the last 5-10 days vs. the full sample) was run to check whether this regime shift is actually present in the data. The adjustment is asserted, not evidenced.
• to Analyst Aassumption 0.3A treats deepening storage withdrawals (d6, weekly data through 10/20) as supportive evidence nudging the estimate up from 4.5% to 8%, but storage draw data is slow-moving, already stale by over a week relative to the as_of timestamp, and seasonally expected in late October — it doesn't establish that TODAY specifically is more likely to see a >4.7% jump. This conflates a plausible slow fundamental tailwind with a same-day causal trigger.
• to Analyst Efactual 0.3E states the price had been in a 'declining regime' with a 13-day mean of 2.951 and a recent high of 3.283, framing the last two up-days as an exception to a downtrend — this is a different characterization of recent price history than the others, who mostly only discuss daily returns in isolation. This is additional unverified context not cross-checked by other analysts.
• to Analyst A, Analyst C, Analyst D, Analyst Emethodological 0.7Several analysts (A at 8%, C at 13%, D at ~15%, E citing 12-18% in objection) inflate the base rate using the same two observed data points (+3.6%, +4.35% days) as justification, despite each independently confirming near-zero return autocorrelation. This is the single largest source of disagreement across the committee and amounts to double-counting one piece of anecdotal momentum evidence as if it were several independent confirmations — no placebo test (e.g., checking how often two consecutive >3% up-days are historically followed by a third >4.5% day) was run to validate this adjustment.
• Same evidence as another seat: Daily log-return distribution statistics (mean≈0, sd≈2.7-2.8%) used by Analysts A, B, C, E and myself are drawn from essentially the same underlying daily-return series (d2/d3/s2/s3), just with different sample windows (61-172 days) — treating these as independent confirmations overstates the evidential weight of the ~4.5-5% base rate.; The last close value of 2.817 on 2026-10-28 (d1) is used identically by all six analysts as the reference starting point.; The two most recent daily returns (+3.59-3.65%, +4.35-4.45%) are cited by every analyst including myself as the key momentum fact, making this a single piece of evidence repeated six times rather than six separate findings.

To: none (light debate)

light debate: rebuttal round skipped

Holds. Would change my mind: If the Oct 29, 2026 5:00 PM EDT 1-minute candle close for the Kalshi NATGASD contract is above $2.950, this forecast (probability 0.08 for YES) is falsified as too low; conversely if natural gas trades materially above $3.10 intraday on heavy volume driven by confirmed cold-weather demand news before settlement, that would also indicate the thesis underweighted a genuine regime shift.

To: none (light debate)

light debate: rebuttal round skipped

Holds. Would change my mind: If the Oct 29 5PM EDT 1-minute close is reported at or below 2.950, this forecast (and the thesis that the move is an extreme tail event) is confirmed; if reported above 2.950, the thesis that the required move exceeds realistic 1-day volatility is falsified and should be revisited — check Kalshi settlement data for KXNATGASD-26OCT2917-T2.950 after market close on 2026-10-29.

To: none (light debate)

light debate: rebuttal round skipped

Holds. Would change my mind: If Henry Hub front-month NG settles (per Kalshi's 1-min candle at 5:00 PM EDT Oct 29, 2026) at or below $2.950, this forecast is falsified on the low side (I assigned 13% to YES); if it settles above $2.950, the forecast is validated directionally but the specific 13% probability should be checked against whether the move was driven by continued momentum (supporting the 'regime-shift vol' objection) or by a discrete news shock not captured in the historical return distribution at all.

To: none (light debate)

light debate: rebuttal round skipped

Holds. Would change my mind: If natural gas settles between $2.818 and $2.950 (a positive but sub-threshold move) or declines, that confirms the base-rate-driven low probability was correct; if it settles above $2.950, check whether a specific weather/storage catalyst occurred intraday on Oct 29 to distinguish genuine news-driven moves from random fat-tail draws for future calibration.

To: none (light debate)

light debate: rebuttal round skipped

Holds. Would change my mind: If the natural gas front-month price is already trading above 2.90-2.95 USD/MMBtu intraday on 10/29 (i.e., has already rallied materially from the 2.817 10/28 close) before the 5pm EDT settlement window, this would falsify the base-rate-anchored low probability and should push the estimate toward 25%+ depending on magnitude and time remaining.

To: none (light debate)

light debate: rebuttal round skipped

Holds. Would change my mind: If the actual 1-minute candle close at 5:00 PM EDT on 2026-10-29 is retrieved and is ≤2.950, this estimate is validated as directionally correct; if it is >2.950, especially driven by a continuation of the 10/27-10/28 rally without any observable new information in d4 (HDD) crossing a threshold, that would falsify the 'no reliable causal driver' claim and support a momentum/weather-driven explanation instead."]

• +0.03 502-day sample (vs my 123-day) places the required +4.72% move at ~83rd-85th percentile of observed returns, implying an empirical base rate near 15-16% — roughly 3x my sample's tail estimate, suggesting my shorter window may understate right-tail frequency for this instrument. (Analyst D)
• +0.01 502-day return distribution carries kurtosis ~19.5 and negative skew (-0.87), meaning large up-moves, while rarer than large down-moves in principle, still occur far more often than a Gaussian sd=2.7% would predict — fat tails cut both ways and the linear percentile interpolation (75th->95th) is a crude but directionally informative signal that my normal/empirical-quantile approach likely underweights. ()
• -0.01 No analyst (including B/F who explicitly checked) found working HDD/weather-anomaly data to substantiate a cold-snap catalyst; the physical-fundamentals case for continuation remains unconfirmed, capping how far I update from the larger-sample base rate. (Analyst B)
• -0.01 Multiple independent analysts (B, E, F) converged on 4.5-6% from similar unconditional-normal/empirical approaches, reinforcing that the shorter-sample tail estimate I started from is not an outlier methodology, limiting how much weight the single larger-sample estimate should get. (Analyst B/E/F consensus)

Base rate: empirical probability a single-day NG log-return exceeds the ~+4.6-4.7% threshold, from my own 123-day sample, is ~4.5% (q95=4.29%, required move just above that). I started my estimate at 8%, nudged above the raw base rate for the physical tightening backdrop (deepening storage withdrawals, d6) and the two recent large up-days, while discounting heavily because daily-return autocorrelation is ~0 (near-random-walk) and no confirmed weather catalyst was observable as of 11:42 UTC. What's genuinely new across the panel: Analyst D ran the same calculation on a materially larger sample (502 days vs my 123), and in that longer window the required move sits at the 83rd-85th percentile of returns (not 95th+), implying a base rate near 15-16% rather than 4.5-5%. That's a meaningful methodological disagreement — a longer sample should, in principle, capture more of the fat-tail/weather-driven NG return distribution (kurtosis 19.5 is explicitly cited), and my shorter window may simply have missed a few historical outsized up-days that a 502-day lookback includes. This is new information I didn't have (none of my own tool evidence included a comparable long-sample percentile estimate), so I give it real weight — but not full weight, because (a) the interpolation between the 75th and 95th percentile is linear/crude and could overstate the true density in that exact tail region, (b) D's own distribution shows negative skew (-0.87), meaning the up-tail is thinner than the down-tail, which cuts against the headline 15-16% number being purely about upside risk, and (c) three other analysts (B, E, F) independently ran similar unconditional/empirical tail calculations on comparable-length samples (61-172 days) and converged tightly on 4.5-6%, essentially replicating my own base rate rather than adding new information — duplicated evidence, so it counts once and mostly just confirms my prior rather than moving me. Net effect: I move from 8% to 9%, primarily crediting Analyst D's longer-sample tail estimate as a genuine data point I lacked, while holding the line against over-crediting it given the skew caveat and the crude interpolation, and against the duplicated confirmatory estimates from B/E/F which add no new information. No analyst produced confirmed weather/storage catalyst data (B and F explicitly report that HDD/anomaly transforms failed/were empty), so the physical-fundamentals thesis (storage withdrawals deepening, d6) remains unconfirmed by any new intraday catalyst — I still cannot justify the double-digit estimates (13-15.5%) that rest partly on an unconfirmed vol-regime-shift narrative. Final: 9%, reflecting a modest, specifically-attributed update rather than a leap to the panel's middle.

• +0.03 502-day sample (vs. my 61-day window) places the required +4.7% move at ~84th percentile, implying an unconditional base rate near 15-16%, roughly 3x my 61-day-derived 4.5% base rate — longer sample suggests my window underestimated tail frequency for this fat-tailed series (Analyst D)
• -0.01 Same 502-day sample shows return distribution is negatively skewed (-0.87) with high kurtosis (19.5), meaning large UP-moves specifically are structurally rarer than large down-moves despite fat tails overall — partially offsets the higher percentile-based base rate (Analyst D)
• +0.00 Multiple independent analysts (A, C, E, F) using different sample windows (61-172 days) all converge on the same ~4.5-5% unconditional base rate and near-zero return autocorrelation, reinforcing rather than adding to my own prior estimate (duplicated signal, no new information) (Analyst A/C/E/F (consensus))

BASE RATE (mine, 61-day sample): empirical P(1-day NG return ≥ +4.72%) ≈ 4.5%, close to the 95th percentile of realized daily returns. My prior estimate (0.08) already nudged above this base rate for the two-day vol-clustering/momentum regime observed in d2 (+3.6%, +4.3%), per my own stated objection, while discounting because return autocorrelation is statistically zero. The committee's new evidence that changes my mind is Analyst D's use of a much longer (502-day) sample, which places the required move at the 83rd-85th percentile of historical daily log-returns — implying a base rate near 15-16%, roughly 3x higher than the 61-123 day windows used by me and four other analysts (A, C, E, F), all of whom converged independently on ~4.5-5%. A 502-day sample captures more regime variation (including prior cold-snap episodes) and is less likely to be an artifact of a recent calm stretch, so I treat this as genuinely new information about tail frequency rather than noise. This pushes my estimate up. Counterbalancing this, the same longer-sample evidence (Analyst D) also reveals the return distribution is negatively skewed (-0.87) with high kurtosis — meaning that among the fat tails, large DOWN-moves are structurally more common than large UP-moves of equivalent size. This tempers how much I should raise my probability: the 15.5% figure is a naive percentile interpolation that doesn't account for the asymmetry working against a YES outcome specifically. No analyst produced actual intraday Oct 29 price data, weather/HDD catalyst confirmation, or a GARCH conditional-vol estimate — these remain the key uncertainties everyone flagged (including me) but nobody resolved. The consensus base-rate convergence (A, C, E, F) doesn't add new information since I already incorporated a similar 61-day-based ~4.5% base rate myself. Net: I move from 0.08 to 0.10, reflecting a modest upward correction for the longer-sample base-rate evidence, partially offset by the negative-skew finding, while maintaining moderate (not high) confidence since the core uncertainty — whether a same-day weather/storage catalyst is driving the last two sessions' rally — remains untested by any analyst, exactly as it was in my original thesis. FALSIFIED IF: unchanged from my original — if the Oct 29 5PM EDT 1-minute close is at or below $2.950, this estimate is validated as directionally correct (NO); if above $2.950, check whether the move coincided with a confirmed weather/storage catalyst (supporting Analyst D/others' regime-shift concern) versus a pure fat-tail draw with no identifiable cause.

• +0.04 502-day sample shows required +4.72% move near 84th percentile of returns, implying base rate ~15.5%, higher than my 172-obs normal approximation of ~5%. (Analyst D)
• +0.00 2.950 is not an extreme price level historically (70-75th percentile of 83-day price distribution); confirms question is about move magnitude not rare price level. (Analyst F)
• +0.01 Storage withdrawals deepening into late October, consistent with seasonal tightening; weak and slow-moving but new to my evidence set. (Analyst A)
• +0.00 Several analysts independently arrive at objection-adjusted ranges of 12-20 percent via different methods, corroborating but not adding new quantitative information. (Analyst B)

Base rate restated: unconditional probability of a >=4.6 percent one-day NG log-return from my 172-obs normal approximation was about 5 percent; I had updated to 13 percent for momentum and vol-clustering concerns that could not be confirmed statistically. New evidence this round: Analyst D used a 502-day sample (roughly 3x mine) and found the required move sits near the 84th percentile of returns, implying a base rate near 15.5 percent rather than 5 percent. Longer samples generally capture more tail events, so this is the most credible single update, and I weight it most heavily (+0.04). Analyst A's storage withdrawal data is a new fundamental signal supporting mild upside bias, though slow-moving, warranting a small nudge (+0.01). The broader convergence of other analysts toward 12-20 percent objection-adjusted ranges corroborates but does not add new quantitative information beyond what I already incorporated, so no further delta. No analyst produced a working conditional-volatility (GARCH) model or found significant momentum, weather, or positioning effects, so my central uncertainty about a possible vol-regime shift remains unresolved. Net result: I move from 0.13 to 0.15, with moderate confidence (0.55) given the unresolved core uncertainty about whether the last two large up-days reflect a genuine volatility regime shift or ordinary fat-tail noise.

• -0.04 Four independent analysts (A, B, E, F) using smaller samples (n=61-165) consistently report sd≈2.70-2.79% and 95th-percentile daily moves of only +4.29% to +4.45%, implying unconditional base rates of 4.5-5% — roughly one-third of my 502-day interpolated estimate (15.5%). The convergence across independent shorter windows suggests my larger sample's percentile interpolation (75th=2.29%→95th=7.35%) may be stretched by a small number of historical extreme events (consistent with the max +28.9% outlier I flagged) that inflate the implied tail probability for 'moves of this magnitude' relative to the recent/relevant volatility regime. (Analyst A/B/E/F (converging independently))
• +0.01 Analyst A's storage data (d6) shows withdrawal magnitudes deepening to -242,693 by 10/20, the most negative in the 8-week sample — a genuine fundamental tightening signal I did not have, supporting some upward skew in near-term price risk even though it's a slow-moving weekly signal, not a same-day predictor. (Analyst A)
• -0.01 Analyst C independently reached a similar momentum-adjusted estimate (13%) via a different unconditional base rate (5%) plus vol-clustering/regime-shift logic, partially corroborating the qualitative case for upward adjustment from base rate but anchoring to a lower unconditional starting point than mine. (Analyst C)

Base rate before update: my own estimate used a 502-day sample interpolating between 75th (2.29%) and 95th (7.35%) percentiles to get ~15.5% probability for a +4.72% move. Four other analysts, using shorter but still substantial samples (61-165 days), independently converged on a much lower unconditional tail probability (4.5-5%) with similar sd (~2.7-2.79%) but thinner tails (lower 95th percentile, lower kurtosis). This convergence across independent analysts and independent sample windows is new information: it suggests my 502-day sample's fat right tail (kurtosis 19.5, skew -0.87) is likely driven by a small number of extreme historical outliers (e.g., the +28.9% max) rather than being representative of the current regime's conditional volatility. This is a legitimate reason to pull my estimate down toward the consensus, though I don't fully converge to their 4.5-8% range because: (1) the last two sessions' back-to-back large up-moves (+3.65%, +4.45%) remain a real, shared observation across all six analysts that nobody can fully dismiss as pure noise given near-zero autocorrelation is a weak test for vol-clustering specifically; (2) Analyst A's storage-withdrawal evidence (d6) is new fundamental information supporting modest upside risk that offsets some of the downward pull from the base-rate convergence. Net effect: I move from 0.155 to 0.12, reflecting a meaningful but not complete concession toward the lower consensus base rate, while retaining a premium above the strict 4.5-5% unconditional estimate for the shared momentum/fat-tail-catalyst risk (cold snap, storage surprise) that every analyst, including myself, flagged as the chief source of model error. Falsification condition unchanged: settlement ≤$2.950 confirms; settlement >$2.950 should be checked against whether a specific weather/storage catalyst occurred (distinguishing genuine news-driven moves from tail draws) to recalibrate which analyst's volatility-regime assumption was closer to correct.

• +0.02 Analyst D's 502-day sample implies a base rate of ~15.5% for the required move, higher than the 4.5-5% from shorter samples. (Analyst D)
• +0.01 Analysts B and C both independently flagged vol-clustering risk from the two recent large up-days, reinforcing a concern I had but could not quantify. (Analyst C)
• +0.00 No analyst retrieved live Kalshi order-book implied probability, open interest, or funding data for this contract. (self)

Base rate for the required +4.6-4.7% move clusters at 4.5-5% across short samples, with one longer 502-day sample giving 15.5%. My prior was 0.07. The positioning mandate requires market-implied probability and crowding data (OI, funding, prior catalyst reactions), none of which was provided by any analyst, so that component remains untested and caps confidence at 0.35. Structurally, single-day threshold markets on headline-driven commodities after consecutive large up-days are prone to favorite-longshot bias, so the actual Kalshi price may run above fair value, favoring NO even if fundamental probability is modestly underestimated. I raise my point estimate to 0.095 for the fat-tail and vol-clustering evidence, but confidence stays low given the missing price/crowding data. Falsified if Kalshi implied YES exceeds ~15% while spot stays below 2.90, or if spot is already above 2.90 intraday before settlement (which would invalidate the whole framework and push probability to 25-40%+).

• +0.04 502-day return sample interpolation placing the +4.72% required move at ~83rd-85th percentile (base rate ~0.155), far looser than the 61-123 day samples everyone else (including me) used (Analyst D)
• -0.01 Same 502-day sample shows negative skew (-0.87): large up-moves are structurally rarer than large down-moves despite high kurtosis, partially offsetting the higher base-rate figure (Analyst D)
• +0.00 Weather data (d5/d6) failed/empty after anomaly transform, confirming no analyst has any actual catalyst evidence; corroborating absence, not new information (Analyst B)
• +0.00 No analyst produced a GARCH/conditional-vol model despite flagging vol-clustering in the last two sessions; remains untested, so no probability movement attributed to the unverified 'regime shift' story (Analyst C)

Causal-skeptic read: every analyst independently landed near a ~4-5% unconditional tail base rate using short samples (61-172 obs), which is exactly the kind of small-n, window-selection fragility I flagged originally about the n=16 and weather correlates — it generalizes to the base-rate estimate itself. The genuinely new, non-duplicated fact is Analyst D's much longer 502-day sample, which gives a materially different (roughly 3x higher) base rate for a move of this size. That discrepancy is itself evidence of regime/sample dependence: short recent windows (possibly a calmer stretch) may understate the true unconditional tail frequency. I can't rule out that my (and everyone else's) 61-123 day sample simply happened to be drawn from a lower-volatility regime (a selection effect on the estimation window, distinct from any causal story). That pushes my estimate up from the ~5% anchor. However, D's same long sample shows strong negative skew (-0.87), meaning big up-days are historically rarer than big down-days, which tempers the upward revision — a 15.5% unconditional base rate overstates upside-specific risk. Importantly, no one supplied actual intraday Oct 29 price data, a confirmed weather/storage catalyst, or a conditional-volatility (GARCH) estimate, so the 'regime shift from back-to-back rally days' narrative remains an untested conjecture across all six analysts — consistent with near-zero return autocorrelation everywhere it was checked (d2, s2, s5 CI crossing zero). Per my own standard, I decline to move probability based on an unestablished causal mechanism. Net effect: a modest upward revision driven purely by a sample-size/selection discrepancy exposed by Analyst D, not by any new causal driver, landing at 0.08. Confidence stays moderate (0.55) because the committee is still split 0.045–0.155 on the base rate alone, with no resolution of which estimation window best represents the current volatility regime, and the key uncertainty I flagged originally (no visibility into intraday Oct 29 price action) remains completely unresolved.

Base rate: across five of six analysts using overlapping 61-172 day samples of NG daily log-returns, the empirical/normal-approximation probability of a one-day move ≥~4.6-4.72% clusters tightly at 4.5-5%. This is effectively one evidence line (same series, different windows), not five independent confirmations. One analyst (D, 502-day window, weakest calibration on the panel) estimates ~15-16% via linear interpolation between the 75th and 95th return percentiles of a distribution it itself reports as negatively skewed (-0.87) and extremely fat-tailed (kurtosis 19.5) -- a method that likely overstates upside-tail probability given both the interpolation's invalidity in a convex tail region and the stated skew working against an upside breach specifically. Update from base rate: I move from the ~4.5% unconditional base rate to 8%, crediting (a) a small premium for genuine sample-window/regime-selection uncertainty that D's longer sample legitimately exposes (short windows may be drawn from a calmer stretch), and (b) the shared, un-dismissable fact that the prior two sessions realized unusually large up-moves, even though no analyst found statistical persistence (autocorrelation ~0) or a confirmed causal catalyst (weather/storage data was inconsistent across analysts' own tool pulls, and COT regression was insignificant). I do not move further toward the panel's higher estimates (12-15%) because those rest on a momentum/vol-clustering narrative that every quantitative test run by the panel (autocorrelation, COT regression) failed to support, and because D's headline figure has an internal methodological flaw that none of the panel fully corrected for. This sits slightly below the deterministic calibration-weighted pool (0.10), reflecting my judgment that the pooling mechanism cannot fully correct for the fact that four-to-five of six "independent" probabilities are built from the same underlying duplicated return series, while D's distinct-but-flawed long-sample estimate pulls the pool upward more than its evidentiary quality warrants. Tradeability: Not confidently tradeable as stated. The single largest gap on the entire panel is the absence of any actual market-implied price (e.g., Kalshi YES price) or intraday Oct 29 price action -- without observing where the market/price currently sits relative to this 0.08 estimate, there is no demonstrated edge to express. If offered a market pricing YES meaningfully above ~15% with no confirmed weather/storage catalyst visible, I would express a NO/sell-YES position sized modestly, given moderate (not high) confidence and a wide [0.04, 0.16] interval. If priced near or below 5-8%, there is no edge to act on. Given only hours remain to the 5PM EDT settlement, the dominant actionable signal from here forward is direct observation of intraday price action and any same-day weather/storage headlines, not further re-analysis of historical return distributions.

Consensus 10% · interval 4% to 16%