One person's trash is another person's treasure. To somebody who has no imagination, of course everything is dead.
I had the same question, but after reading more, I don't think they will sell more shares. The GME saga started with Michael Burry telling Game Stop management to buyback shares in 2019. Game Stop bought a lot back, raising the price (which his fund owned), after which he began to cash out. That said, I can see the share went back up afterwards, so I am not sure if they re-liquidated after: Outstanding shares: https://www.sharesoutstandinghistory.com/?symbol=GME Burry's holdings: https://public.tableau.com/shared/S...rigin=viz_share_link&:embed=y&:showVizHome=no
I thought HTZ commented about issuing more shares, but they got blocked by the SEC or decided that they could not because they had already filed or announced intent to file bankruptcy. Bankrupt company can't issue shares.
I think companies have to announce the issuance of new shares for employee compensation. However, if memory serves me correctly, Tesla got approval to release new shares within a few days of their short squeeze in 2019?...if I recall correctly.
Wait, Tesla is buying GameStop?? Is that right...Or is GameStop buying Tesla? I'm so confused...I'll check on my Blackberry.
Everyone in the company should sell their shares, exercise options then declare 20 for 1 reverse stock split.
At this level it ceases to have any meaningful meaning. Volatility is useful for describing small variations around a point but in this case, stock going from $500 to $150, we're talking about jumps. So the market seems to have thought that a reversal to $500 is possible (a jump back), problem is the Black-Scholes model is not supposed to support this use case. It sort of shoehorns it by calculating: "what's the totally meaningless number we need to put for this parameter such that going from $150 to $500 in a day is still within the probable realm? Oh, 1000% volatility, OK then, let's use that."
What do you think about the Parkinson model of calculating historical volatility that can produce a one day historical volatility? The one day volatility Thursday was 1500%. We may say the IV and HV are irrelevant at these levels but at least they are comparable.
Never heard of it. I found this: https://derivvaluation.medium.com/p...on-volatility-analysis-in-python-8ffee87f1a84 If so it doesn't look that different from the classical way. Unless I see the exact way to derive "one day HV", I can only assume. The accuracy of computing volatility from samples is a matter of number of samples used. "Historical 252 days volatility" is using 252 samples, so one year. Therefore I assume 1 day volatility is using either: intraday samples (but how many?). Or daily samples including open, close, lo, hi, but how and most importantly how many samples? Still 252? The fewer you use the more "wild" the result can be. Problem with small number of samples are outliers. Stock jumping from 500 to 150 means the return is Ln(150/252) = -1.2. Other samples are in the 0.01 range, so a difference of 100x in magnitude. If the calculation doesn't use many samples, one or a few outliers will dominate the result and practically pollute it. In practice volatility calculation discards the outliers, so a few jumps will not influence the volatility at all. So I think it's just another calculation artefact (an incorrect one). Correctly, the jump would have been ignored at all in calculation so there would be no change in historical volatility, surely not jump to 1000%.
Standard deviation moves are garbage in the context of gap risk. Not to mention a 1sd move only captures ~2/3 of moves in a normal dist.