Agustin Lebron Profile picture
Feb 8, 2021 63 tweets 11 min read Read on X
1/ Welcome to Options 201! You might remember me from such threads as Options 101:



Or What’s The Deal With Having Edge?

2/ I wasn’t sure if I should keep going. We’re getting closer to “how to make money in options,” and I think public sources of such claims are always and invariably scams.

But I’ve decided this is still more “useful information to know” than trading edge, so it should be ok.
3/ Let’s start by talking about time. Time is surprisingly hard to think about. But why do we care?

Well, as we said in Options 101, we want to convert our options prices into vols because of #reasons. And to do so, you need to know t_exp.
4/ Ok, no big deal. my_option.exp_date – datetime.now(), done. Not so fast.

In the real world, time flows at a constant rate.
In the financial world it doesn’t.
5/ When you’re talking about vol and time-to-exp, different periods are more or less vol-y.

Time flows more slowly on the weekend.
Faster on Mondays and Fridays.
Time flows very fast on earnings day.
6/ Remember, BS assumes vol is constant in time. Every timeslice is the same as every other.

So if we want to calc sensible vols, we need to calc a sensible clock for the stock. A way to “divide time” so that the clock ticks off constant chunks of volatility. In theory at least.
7/ This is impossible to do perfectly. If you knew what vol was going to be in the future, just trade it!

What we’re really after is a decent approximation. So here’s a thing we can do:
8/

A. Take 5min returns of the stock intraday (or whenever options trade) and overnights/weekends.
B. Calculate variances for every block for a few years.
C. Aggregate by days in the monthly exp cycle.

This variance-time is now our measuring stick.
9/ Lots of variance on a Monday morning from 9:35am to 9:40am? Time flows fast in that 5min block.

Of course we should probably renormalize variances so that the units are still roughly days or years or something intelligible. Hopefully you get the idea.
10/ Yes there are problems with this approach.

Q: What if the stock had earnings on some day?
A: Probably we need to throw that out for our var-time calculation.
11/ What else?

Q: Does it make sense to have a different var-time for different stocks?
A: Technically probably yes, but they should be fairly similar to each other in the same country, or size, or sector or whatever. Probably some aggregation is in order.
12/ More questions:

Q: How far back should we sample data for?
A: Bias vs variance tradeoff. Again.

Q: What about if you had a market crash in your dataset?
A: I dunno, maybe throw it out? Depends.
13/ Yet more:

Q: Why aggregate monthly? Why not weekly?
A: Weekly could be sensible...
14/ Closing thoughts.

There’s no right answer here. All there are are tradeoffs. But if you want to get vols even vaguely right, you do need to think about this stuff.

Comments?
15/ Ok, today we’re continuing with Options 201 by talking about THE GREEKS!

But before we do, let’s take stock (ha!) of where we are. Because some of you will say “Bah, Greeks, that’s 101 stuff”. I don’t agree.
16/ Options are weird and wonderful, and it takes some time until your intuitions line up with reality.

Thinking in terms of partial derivatives helps build intuitions, but only after you really “get” the more basic stuff.
17/ Thinking in terms of Greeks, IMO, is not the right place to *start building options intuitions.

Ok, enough soapbox, off we go.
18/ As I said, all we’re talking about are partial derivatives of some options things with respect to other options things.

But what’s important is that for most of these, there are many ways of thinking about the same thing.

And those connections matter a lot.
19/ DELTA.

“The change in option price as a f’n of a change in und price.” BORING!

Delta is how happy/sad you are if stock goes with/against you. But only for small quick changes.
20/ Like if you’re long an ATM call (say 100 strike) and stock goes up 10c in the next minute. You probably made 5c. On paper. Yay.

But if stock goes up $1000, you definitely made more than $500!
21/ Same on the down side. If stock went down 10c, you probably lost 5c on paper.

But if stock drops to zero, you didn’t lose $50. You lost much less (the premium of the option before the move).
22/ That’s the deal with the being long options.

When und moves in your favor, you “make more than you should” and when it goes against you, you “lose less than you should”.
23/ The “should” here is relative to owning (or getting short) the underlying. Of course, you’re paying for that wonderful behavior. The amount you’re paying we call “premium”. Right?

But delta isn’t only this. It’s much much more!
24/ Delta is also (somewhat obviously, if you’re paying attention) how much you should hedge your position in order to be indifferent to those small moves.
25/ And being indifferent is a good thing.

If you’re trading options, you should almost never do it because of an opinion in the und. If you do, just trade the und.

Remember Options 101?
26/ So “delta-hedging” in this way lets us get rid of a risk we’re not being paid to take, and lets us isolate the ones we do want to take.

But wait, there’s more!
27/ Delta is also (roughly) the chances you’re going to want to exercise your option at expiration. In other words, the chances the stock will end up above/below the strike price of your call/put.

Not exactly, but usually close enough.

That’s kinda cool, I think.
28/ GAMMA.

“The change in delta as a f’n of change in the underlying price.” Again, BORING!

Remember small moves? When the und moves small, the delta of an option stays basically the same. But now let’s make the move “a bit” larger.
29/ Now the delta itself changes. If und moved in your favor and you were long, the delta went up. Against, delta went down.

Flip these if you’re short options.
30/ Gamma is the way that the “more than you should” and “less than you should” bits above express themselves. So it’s a measure of option-y-ness.
31/ A waaay OTM option doesn’t have any gamma because the optionality is all gone.

The stock is way far away from the strike price so change in stock don’t affect that option’s value. It’s near zero.
32/ But a waay ITM option ALSO doesn’t have any gamma because it basically behaves just like stock.

It’s getting exercised, almost definitely, so you’re getting (or selling) stock almost definitely, so it’s not really an option anymore.
33/ Gamma, or optionality, concentrates near-the-money. There’s where the action is, which is why that’s where the liquidity and volume are in options markets.
34/ THETA.

“The change in price of an option due to the passage of time.” Hmm, sounds complicated.

Why “theta”? Because theta starts with t, and so does the word time. Genius right?
35/ So what does this mean? Let’s go back to our way OTM option. It doesn’t have much theta because its value is already basically zero. That’s easy.
36/ But our deep ITM option ALSO has zero theta.

Because remember, it’s going to turn into stock for sure, so its value basically only tracks stock. If stock doesn’t move, our deep ITM option price doesn’t move.
37/ All the theta concentrates in the near-the-money parts. Sound familiar? It should.

Gamma, our old (4 tweets ago) friend!

In fact, ignoring some scaling bits, gamma and theta are the same thing! Which they should be.
38/ If gamma expresses how much optionality a option has, then surely that optionality disappears over time.

And the amount of optionality you’re losing over time is exactly what theta expresses. The system works!
39/ VEGA.

“The change in price of an option due to change in volatility.” Ok, I’m going to level with you.

Vega isn’t real like the other greeks are. But what do I mean by “real”?
40/ Well up to now, the sensitivity of options prices were to observable things. Und chg, und chg chg, time. These are real things.

Even if you had no idea about options pricing models, you could come up with delta, gamma and theta estimates just by looking at historical prices.
41/ But “change in vol” isn’t a real thing because vol isn’t a real thing. It’s only real in the context of some model: some way of transforming prices into vols and back again.

And there are LOTS of ways of making that transformation. Just ask Espen Haug! It’s model-dependent.
42/ And yet vega is among the most important sensitivities options traders track. Usually #2 next to delta, in fact. (Don’t yell at me if you’re a short-term gamma scalper!)
43/ Because remember, when you’re trading options you’re trading vol. That second-order moment.

So vega is really the equivalent of plain old delta in stock-land. If you’re long stock and it goes up, you win. And vice versa.
44/ Well vega plays the same role in options land. If you buy some options, keep them nicely hedged, and aren’t close to expiration, your Pnl is defined by your vega.

If you’re long options and vol goes up, you win $. It’s pretty much that simple.
45/ But because vega is model-dependent, it gets more complicated to even be sure what your exposure is sometimes.

Just ask those VIX traders last March, right?
46/ Ok, that’s good for today. We covered the big ones. If there’s interest, we can get to the crazy ones next.

(No, rho isn’t a big one. Interest rates will be zero until the end of time, remember?)
47/ Aaaand we’re back, talking about second-order greeks.

Honestly, I don’t really get why so many people care. I was an options market-maker for a long time, and I almost never thought about second-order greeks.

Like a few times a month, tops.
48/ If you’re an exotics trader then fine. But I doubt there are more than a dozen of you reading this.

And if you’re exo trader or options MM, you should have better sources of info than some rando Twitter handle. :)
49/ So why does #fintwit care? Some of it is curiosity, learning for the sake of learning. That’s great.

But I bet some of it is trying to feel and sound smart. However, feeling and sounding smart doesn’t pay the bills. So be careful.

Ok, no more soapbox. Let’s go.
50/ SPEED: d gamma / d spot.

Ok, I’m cheating already since this is a *third* order greek. Booo! But it’s the most important “weird one” for a market maker IMO.
51/ Gamma is basically “How much am I going to need to hedge to stay delta neutral?”

The delta of low-gamma positions stays pretty constant, high-gamma positions have deltas that move around a lot.

Why should I care?
52/ Expiration, usually. Say I have a pos with high gamma. I’m probably trading a lot (more than I’d like) trying to stay flat.

If I have a high speed on top of it, then I’m exposed to the risk that if stock moves, I’ll have so much gamma I won’t be able to hedge well anymore.
53/ Near expiration, I could easily get stuck with a large delta I can’t hedge.

No market maker likes to sweat a big delta over the weekend. That’s not what you’re paid to do.
54/ CHARM: d delta / d time.

Another one that matters mostly near expiration, for much the same reasons as above. Near expiration, the mere passage of time can change your delta a lot.

ITM options get ITMier, OTM options get OTMier.
55/ Of course, ATM options kind of stay around 50 delta as long as spot doesn’t move.

So the charmiest options are the ones on the shoulders of the Gaussian. The 25/75 delta options, basically.
56/ VOMMA: d vega / d vol.

I didn’t worry much about this one, because I didn’t really trade long-dated options and this is a thing that matters more as t_exp gets bigger.

Usually, the way you think about this is “If vol explodes, does my book get bigger or smaller?”
57/ Since we’re good boys and girls and we’re delta hedging, options trading is about trading vol. And vega is the size of your vol bet.
58/ And since IV only really moves fast in one direction (up), vomma tells you the answer to the question “If the world goes crazy, do I now have a massive book on my hands?”

Which is something worth knowing, for sure.
59/ D VEGA / D SPOT:

I don’t know what this one’s called. But it’s another specific case of d vega / d whatever.

And as before, what we care about is “How big/small does my book get if X happens?”
60/ In fact, as you can tell, this is how I think about all of these 2nd order greeks.

I’ve never put on a trade thinking “Ooh, I see a vomma mispricing”.

I’m sure that’s someone’s job but it never was mine. Thinking about this stuff was always about risk management for me.
61/ But again, usually if you take care of the first-order stuff, you don’t really need to think about the second-order stuff except in weird situations.

I think that’s it for second order greeks. Yes I skipped a big one. That’s cuz I have nothing interesting to say about it.
62/ Please don’t think of this as definitive on the subject. It’s just one person’s perspective, given the experiences I’ve had.

I’m sure @bennpeifert @Ksidiii and others have different (and possibly more informed) views. That’s ok too.

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More from @AgustinLebron3

Mar 9, 2023
Well this is it! I’m excited (and a bit terrified) to announce what I’ve been working on for the last year and a half.

It’s been a wild ride so far, and I’m sure it’s about to get wilder.

coindesk.com/business/2023/…

1/n 🧵👉👉👉
2/ There was a moment back in May of last year when everything crystallized for me and my co-founders Craig and Rich.

Luna, 3AC, and all of that craziness was going on. And one of us (can’t remember who) asked a simple question:
3/ “Why does *anyone* ever trust someone they’ve never met with their money?”

And there are lots of answers.
Read 28 tweets
Jan 19, 2023
Listening is among the most cognitively hard things to do well. 3 levels.

Not Listening
Waiting for them to stop talking so that you can talk.

Bad Listening
Thinking what YOU would mean if you said their words.

Good Listening
Thinking what THEY mean when they say their words.
In social situations we're usually not listening. It's more like a two person game we play, ChatGPT'ing our way through the situation.

In fact, in those spots it's actually a little unsettling when someone listens to what you're saying. They seem a little too intense.
At work we're usually in bad-listening mode. Getting the information we need, often needing clarification on things because of a thousand little miscommunications.

As the speaker, you're always wondering "I know they're listening but did they really understand what I just said?"
Read 4 tweets
Dec 12, 2022
1/ I was asked a question on DM and figured I'd write a short thread about:

Classifying trading days.

👉👉👉
2/ It matters a lot what we mean by "classifying". In particular, most people who talk about this implicitly mean:

"Look at a bunch of days in the past, and cluster them into some classes somehow."
3/ Which is fine, but IMO the important question comes right after: "In order to do what?"

Because if it's to make money trading then you're already in trouble.
Read 9 tweets
Oct 26, 2022
1/ No-code "solutions" are the most frustrating thing ever.

You're solving the wrong problem!!!

Arcane programming language syntax wasn't created in some darkened room to keep out non-nerds (APL is the exception of course).
2/ Syntax exists to solve a very real problem:

Q: How do you translate what you want into a form that a system with ZERO semantic understanding does the thing you want?

A: Programming language.
3/ The hard part about programming isn't translating what you want into code.

It's understanding your problem SO WELL that you can ELI5 it to a thing that's... actually much stupider than a 5 year old.
Read 9 tweets
Oct 21, 2022
The question of what trading should be allowed, and what trading shouldn't, is obviously a bit difficult to answer.

Yes, it's a bit of a game we all play to outwit the other. So who's to say what's fair-game outwitting and what's not?

Lemme suggest a useful heuristic:
The key concept to develop is the idea of a fair value.

"What *should* this thing be worth?"

I get that some trades and strategies don't care about that question. But for the purposes of understanding which trades are "good" and which ones are "bad/manipulative" it's useful.
The heuristic:

"Does the effect of my trading move the price towards my perceived fair value, or away?"
Read 7 tweets
Aug 25, 2022
Latest job ponzi I've discovered:

Companies penetration-testing their software suppliers.

Up and down the stack, companies are hiring and resourcing teams to penetrate their suppliers, while also having teams to protect against their customers pen testing them.
It's like watching the world computer evolve an immune system in real time. Slow by human standards, blazingly fast by wet evolution standards.

And we're the mindless B-cells, T-cells and neutrophils.
@vgr I feel like you may enjoy thinking about this. :)
Read 4 tweets

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