Options greeks ranked from worst to best, by someone who doesn't really understand options.
10. Rho -- worst sensitivity, very boring, mostly ignored except by people trading very long maturities (who you should not trust anyway)
9. Delta -- very terrible and boring first-order sensitivity. Hedged by everyone except retail. May be driving the stock market now????? (unclear)
8. Theta -- seems sexy and interesting at first, really just the opposite to gamma upon closer inspection. Has potential to blow up sellers (only reason it is ranked above delta)
7. Color -- honestly only included because I wanted a ten item list. Can tell you when to expect your carefully gamma hedged portfolio to become unhedged, but only if you assume the stock price never moves (very bad assumption) so essentially useless.
6. Vega -- only interesting first-order sensitivity. Responsible for most of your mark-to-market PnL, quite neat (unless you are a retail degenerate who never holds anything beyond weeklies)
5. Speed -- very mysterious and exotic third-order sensitivity, understood by few. Tells you how quickly your careful gamma hedge is going to disappear when the stock moves. Also helpful for explaining PnL when the stock jumps, 8/10 for usefulness
4. Gamma -- very cool and important second-order sensitivity. Essential for PnL explain, also tells you how quickly your delta hedge will break down, watched by many as an indicator of whether dealers will suppress or exacerbate realized vol. Would calculate again.
3. Charm -- sexy, mysterious older brother of color. Mostly quiet and brooding until shortly before expiry when he runs out of the bedroom wearing sequins and performs a raunchy dance number, very exciting to watch
2. Volga -- Often maligned but very cool second-order sensitivity, major reason for vol surface smile. Responsible for many nice profits from buying wings. Also named after a Russian river???
1. Vanna -- unquestionably best sensitivity. Responsible for vol surface skew and profits when buying downside strikes. Dual use as measuring sensitivity of both delta (to vol changes) and vega (to spot changes). Can contribute to market crashes in extreme moves, very thrilling!
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A couple of people asked how to price this bet. As a reminder the bet Peter offered was 5-1 against that BTC/USD would hit $100,000 before the end of the year (i.e. he receives $20,000 if Bitcoin hits 100k, and he pays $100,000 if Bitcoin does not hit 100k)
Intuitively that seems mispriced, but how can we sharpen that up a bit? Let's convert it to a derivative contract. The bet (from Peter's pov) is equivalent to paying $100,000 to buy a contract that pays out $120,000 if BTC/USD hits 100k.
This is a "one touch digital option" -- digital because the contract either pays a fixed amount or it pays nothing, and one touch because the price doesn't need to finish the year above the strike, it only needs to touch the strike once.
I feel like some people talk about quantitative/systematic/automated trading as if they are all the same thing, which is not true, and blurring these lines causes confusion for people who want to enter the industry.
“Automated” trading (contrasted with manual trading) is the simplest to understand. If the strategy doesn’t require any human input as part of its execution, then it’s automated. If there is a human in the loop then it’s not automated.
(Though of course there is a bit of a spectrum and semi-automated or “grey box” trading is very popular at firms like Jane Street or Optiver)
Something useful to keep in mind if you're using ridge regression. The normal equations are below, where the parameter lambda controls the amount of regularization.
If you've normalized the columns of X so that the diagonal of X'*X are all 1s, then there's a nice interpretation of this - ridge regression both shrinks X'*X toward the identity matrix (ignoring correlations between features) and shrinks coefficients by a factor of 1+lambda
But there's nothing to say that the amount of shrinkage toward the identity matrix and the overall coefficient shrinkage need to be the same! The constant out the front can be chosen to have any value you like.
Here's a cute story about risk management and how we traded a name that ran up in price from 250 to over 500 in a couple of hours, and then back down to 200 in about twenty minutes. This is the price chart of $TRB over the last 10 days -
We were long this name for a while (a little unusual - the holding period for this strategy is around a day) and here I've normalized our dollar position by dividing by our max pos. We won't buy any more when this ratio is above 1, and we start selling when it's above 1.15ish
We actually traded back and forth quite a bit around this core position though (as signals changed, or we had to trim a large position) - here cumulative $ buys are in blue, sells are in red
A lot of quant workflows basically look like "do these somewhat related but basically different tasks and then combine the result into a normalized output"
For example -
- Pulling together data from different sources, cleaning, mapping symbology etc
- Creating a large number of features as part of a fitting pipeline
- Fitting different models to get a forecast
- Searching over different parameters/strategies in a backtest
When faced with needing to write something like this the temptation is to get it done as quickly as possible, and you end up munging together (a) the logic that determines what tasks you need to do (b) the logic that runs the tasks
This actually seems kinda positive, especially compared to what people were speculating on at the time. bloomberg.com/news/articles/…
The Chinese securities held were commercial paper of ICBC, China Construction Bank and Agricultural Bank of China. These are some of the biggest banks in the world, they have trillions of dollars of assets and decent credit ratings.
The speculation was that they held the debt of Chinese property developers who were going bankrupt - specifically people thought they might hold Evergrande debt. It turns out they held short term debt of investment grade megabanks.