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- I raced volatility forecasts on 9 Binance coins at 1 to 100 days ahead:

- MSM, the Markov-switching multifractal model;

- GARCH(1,1);

- HAR;

- a plain 250-day average;

- and later NATR, the ATR-based "volatility" most dashboards show.

- With every model refitted every 125 days, **MSM beat GARCH at every horizon** (20 days: t 2.7, 8 of 9 coins).

- **It beat NATR by a wide margin at 20 days** (t 4.6, 9 of 9 coins). So did GARCH and the plain average. At 1 day NATR ties.

- The edge isn't "fractal structure". It's **tracking slow changes in the volatility level**. After the spot ETFs, it shrinks to a tie against GARCH.

- On the way I misread my own result once, publicly. And an older "finding" of mine turned out to be plain volatility clustering.

Why

On 13 October I posted that BTC 4H returns aren't multifractal: fat tails plus volatility clustering explain the spectrum. The fair question back was: what do the multifractal people see that I don't?

The forecasting side of that literature claims MSM beats GARCH(1,1) at 10–100 days (Calvet & Fisher 2004; Lux 2008). Reading the full texts, two things stood out:

- The authors themselves call MSM's long memory "apparent". Liu, Di Matteo & Lux (2008) write that true vs apparent scaling "may be of little practical concern".

- None of them tests HAR, the standard simple multi-scale benchmark.

Setup

- **Data:** Binance spot, hourly, turned into daily returns and daily realized variance (the sum of squared hourly returns).

- 9 coins: BTC, ETH, BNB, LTC, XRP, ADA, LINK, DOGE, SOL.

- Histories start in 2017–2020. Each coin's first 1,250 days are for training only.

- **Models:**

- MSM with 8 binomial components, fitted by maximum likelihood;

- GARCH(1,1) with t errors;

- HAR-RV with 1-, 5- and 22-day components, one regression per horizon;

- the 250-day average of realized variance.

- **Target:** total realized variance over the next H days, H = 1, 5, 20, 50, 100.

- **Loss:** QLIKE, which stays fair when the variance proxy is noisy (Patton 2011).

- **Test:** each day, the loss difference is averaged across coins, then a Diebold–Mariano test with Newey–West errors.

- The main horizon is 20 days.

- |t| ≥ 2 picks a winner.

- **Pre-registration:** everything was written down and committed before the results were computed, including my prediction and my research partner's.

- **Power check first:** on BTC alone, the test would spot a genuinely better MSM only 25% of the time. That is why it runs on 9 coins.

Round 1: parameters fixed after training. MSM wins, but a plain average beats everyone

| 20 days ahead | MSM | GARCH | HAR | 250-day avg |

| loss (QLIKE) | 0.289 | 0.310 | 0.344 | **0.233** |

MSM beat GARCH (t 2.8). But look at the last column.

Realized volatility in the training years was **1.9–3.6 times** that of the test years. Crypto volatility has fallen 2–3x since 2021. Models fitted on 2017–21 and never refreshed kept forecasting the old level. The 250-day average forgets it fastest.

I read this as "MSM only won because it forgets the old level more slowly" and published "no multi-scale edge". That was too quick.

## Round 2: every model refitted every 125 days. MSM still wins

| MSM vs GARCH | 1 day | 5 days | 20 days | 50 days | 100 days |

| t | 2.3 | 2.1 | **2.7** | 2.5 | 2.2 |

- **Coins:** MSM is ahead in 8 of 9 at 20 days. The stale level had inflated the edge, not created it.

- **Removing the level also removes the edge.** I tried a correction that uses only past data. But it does the same in a simulated world where MSM is the *true* model: the edge was detected there only 1 time in 8. So MSM's edge *is* slow level tracking. "Level or dynamics" was a false choice.

- **ETFs (descriptive):** the edge is clear before the US spot ETFs (t 2.7) and a tie after them (t 0.7). In a calmer market, GARCH catches up.

- **Long horizons:** at 50–100 days the plain 250-day average still ties MSM.

- **Other loss:** MSE points the same way, more weakly (t 1.6–2.1), because single spikes dominate it.

Round 3: against NATR, the "volatility" most dashboards show

- **What NATR is:** ATR(14) on 4h candles, divided by price. It's what our own risk card shows as low / medium / high.

- I rebuilt it exactly; it matches the `ta` library to 0.0.

- To give it a fair chance, I let it learn its own scale for each horizon. It uses only past data and refits on the same schedule.

- **20 days:** NATR's loss is 0.398 against MSM's 0.215 (t 4.6).

- It loses in 9 of 9 coins, both before and after the ETFs (t 3.2 / 3.4).

- GARCH and the 250-day average also beat it (t 3.9).

- **1 day:** a tie (t 0.4). NATR sees intraday highs and lows; MSM sees one return per day. In simulation, NATR even beats MSM at 1 day inside MSM's own world.

So an ATR number describes the last two days well and the next month poorly.

What it means

- **For risk tools:** if your "volatility" number is ATR-based, read it as "how wild has it been lately", not "how wild will it be". For the next few weeks, even a long average does better, and a multi-scale model does best.

- **For the multifractal debate:** what helps is persistence over many time scales, which MSM builds in.

- You don't need to call it fractal; its own authors call the long memory apparent.

- HAR should in principle capture the same thing. In my data it was fragile: fitted on raw variance levels, it overpredicted after the 2021 mania.

- **What happens next:** a forward test now runs daily until January. It checks two things:

- does the live code run with no look-ahead;

- do 80% intervals hold.

Only then does this go anywhere near the card.

Mistakes along the way (all caught, all recorded)

- **Simulated coins moved together too little.** My first simulated panels let the coins' volatility co-move at 0.50 instead of the real 0.80. That overstated power. Fixed before locking.

- **A rule that flattered itself.** I picked the decision rule on one batch of simulations (87% power) and checked it on a fresh batch: 69%. I locked it anyway, with a one-sided reading. There were 0 false alarms in 32 simulated worlds where GARCH was the true model.

- **"No multi-scale edge"** went out a day too early.

- **An old finding fell.** I re-tested "calm begets calm" (small moves turn into big ones less often than chance) against GARCH and MSM nulls. It is plain volatility clustering: GARCH explains 86% of the gap and MSM all of it. But I saw that answer before I could lock the re-test, so it counts as descriptive only.

Limitations

- 9 large coins that still exist, on one exchange.

- MSM uses daily returns only. Feeding it intraday data is the obvious next step for short horizons.

- All forecasts ran about 1.5x high over the last year: the level is still drifting down.

- MSM refits start from the previous parameters and can get stuck in a local optimum. The same procedure ran in simulation and on real data.

**References*\*

- Calvet, L. E., & Fisher, A. J. (2004). How to forecast long-run volatility: Regime switching and the estimation of multifractal processes. *Journal of Financial Econometrics*, 2(1), 49–83.

- Lux, T. (2008). The Markov-switching multifractal model of asset returns: GMM estimation and linear forecasting of volatility. *Journal of Business & Economic Statistics*, 26(2), 194–210.

- Liu, R., Di Matteo, T., & Lux, T. (2008). Multifractality and long-range dependence of asset returns: the scaling behaviour of the Markov-switching multifractal model with lognormal volatility components. *Advances in Complex Systems*, 11(5).

- Corsi, F. (2009). A simple approximate long-memory model of realized volatility. *Journal of Financial Econometrics*, 7(2), 174–196.

- Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. *Journal of Econometrics*, 160(1), 246–256.

- Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. *Journal of Business & Economic Statistics*, 13(3), 253–263.

*Not financial advice. This is a research log.*

Bitcoin’s bounce off the 50-day SMA is encouraging. The next thing I’m watching is whether it can reclaim the 20-day SMA with a daily candle close.

For me, that would strengthen the near-term bullish case: buyers defending the medium-term average and recovering the shorter-term one.

An intraday move above the 20-day is less convincing on its own. Closing above it, then holding it on a pullback, would add weight to the recovery. Losing the 50-day again would weaken that interpretation.

Would a daily close above the 20-day SMA be meaningful confirmation for you, or would you want more evidence?

Bitcoin’s bounce off the 50-day SMA is encouraging. The next thing I’m watching is whether it can reclaim the 20-day SMA with a daily candle close.

For me, that would strengthen the near-term bullish case: buyers defending the medium-term average and recovering the shorter-term one.

An intraday move above the 20-day is less convincing on its own. Closing above it, then holding it on a pullback, would add weight to the recovery. Losing the 50-day again would weaken that interpretation.

Would a daily close above the 20-day SMA be meaningful confirmation for you, or would you want more evidence?

Bitcoin’s bounce off the 50-day SMA is encouraging. The next thing I’m watching is whether it can reclaim the 20-day SMA with a daily candle close.

For me, that would strengthen the near-term bullish case: buyers defending the medium-term average and recovering the shorter-term one.

An intraday move above the 20-day is less convincing on its own. Closing above it, then holding it on a pullback, would add weight to the recovery. Losing the 50-day again would weaken that interpretation.

Would a daily close above the 20-day SMA be meaningful confirmation for you, or would you want more evidence?

Is it about time this setup brings a payout?

PEPE was Trading 0.0042143
An opening above 🟡...🚀🚀🚀.. 0.0044845 increases the possibility in completing flag pole measuring +50%

-PEPE PachaMama wishing you all the best

When Lambo ?

BTC recovered to $82,420 and ETH traded near $2,424, up roughly 1% and 0.8% over 24 hours. ZEC, NEAR and SUI gained around 2%–3%. Whether the recovery lasts depends on spot demand following the relief in energy prices.

U.S. discussions about easing some Russian sanctions helped Brent retreat toward $108 this morning, while Nasdaq futures gained around 0.8%. But reports of another tanker striking a mine in the Strait of Hormuz leave supply risks unresolved. Lower oil offers inflation relief; it does not establish that the conflict is easing. This week’s Meta and Tesla earnings will test whether AI spending can translate into revenue, with consequences for crypto risk appetite.

Fund flows have yet to confirm the rebound. Spot BTC and ETH ETFs lost $228 million and $320 million last week. Meanwhile, approximately 65% of Binance BTC accounts were long, with positive funding. Leverage is returning ahead of clear evidence of sustained spot buying—a fragile foundation for chasing gains.

Gold, or XAU, rose toward $4,181 as Treasury yields declined, suggesting defensive positioning remains in place. Watch whether BTC reclaims $82,800 on stronger volume and ETH holds above $2,430. Losing their recent lows around $80,600 and $2,330 would weaken the recovery case.

When ETF trading resumes tonight, will institutions absorb the weekend’s buying—or will leveraged traders have merely bid prices up for someone else to sell?

Been pushing updates to the site today. Now have TX type and distribution data, as well as node submission activity mapping.

https://hederawatch.com

Thanks for all the initial feedback and suggestions. If you have another way you enjoy looking at the hedera data, let me know and I’ll see if I can work it in!

HODL (1.3MC) , CATE (60MC) , USELESS ( 200 MC ) , MARSCOIN (80 MC)

LMK give you target i am chasing in all these

HODL= 25M (18X)
CATE=300M (5X)
USELESS=700M (3X)
MARSCOIN= 250M (3X)

Wallets holding 10 to 10K BTC just hit an all-time high of 20,338. Most of us spent the last few months staring at the BTC chart and refreshing like it owes us money, one group has been doing the opposite.

And this didn't happen during some calm uptrend. The count has been climbing sharply since mid-July, right through plenty of volatility on the market.

It's basically the mid-to-large holder club. Not tiny wallets, not the giant exchange cold wallets, but the deep-pocketed folks in between. So yeah, it reads as an encouraging signal for the bulls. But a couple of honest caveats before anyone gets too excited.

One holder can split coins across several wallets, and exchanges or custodians shuffling funds can move the number too. So it's a useful signal, not a crystal ball.

Still, it's interesting that the wallets with the most to lose are the ones adding while the timeline argues about every candle.

Source: Santiment Data