(A little thread) #PnlExplain
Whatever options position one enters it's important to know the risks one is taking and hence where the PnL is coming from. As an example if one is taking delta-neutral strangles (say 2% away OTMs on Nifty weeklies) at market open on a Friday and
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within a couple of hours sees a 5pt reduction in the price of the strangle (with delta still neutral) then the PnL is coming from the following:
Vega - given this is a -ve vega/short vol position a decrease in implied vols of both the OTM options in those couple of hours
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results in a positive Pnl
Theta decay - this is technically the decrease in an option price in a given period of time "keeping all other factors constant".
In all likelihood given the current vol levels, Vega would've driven the 5pt Pnl profit more than theta given +
contribution from theta is low on a friday in a few hour period. Although one should note that theta depends on the level of vols and a high/low vol regime has a high/low theta.
I've seen some traders incorrectly assign their profits solely to theta when it's actually vega
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that's driving their profits more. If you know where your profits/losses are coming from you can plan your next move accordingly in terms of adjustments etc. So as a simple example if your strangle, which you only entered to capture theta and intend to keep it from mkt open
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to close, is losing money due its high vega at entry and with vols increasing (and assuming you're constantly adjusting to keep delta neutral) then you might want to reduce overall vega by shifting both strikes further away.
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If you are selling strangles in a bullish, low vol environment keep the following in mind:
Assuming you entered a delta-neutral strangle on an index(Nifty/Bnf), any further upmove on the index will change delta of the strangle, i.e. making
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it -ve, rapidly and more likely to hit SL on the call side. Given vols are already low at entry further decrease in vols contributing to call delta getting supressed is immaterial, i.e. vanna impact is low (check post below on vanna in general), and so
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call delta will mostly only depend on index moves. In other words, your PnL doesn't have a buffer from vanna effect and is exposed solely to index moves and how good is your SL strategy. The Pnl's movement isn't smooth and adjustments become difficult.
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Let’s focus on expiry trading as this is the easier bit compared to trading them on other days or entering positional (which I'll cover in future posts).
First let me quickly mention what I did last Thursday.
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Let’s try to understand what factors impact ratio spreads and how to trade & risk manage them.
As it’s impossible to fit everything into one thread I’m dividing this into two. I’ll cover factors that affect ratio spreads
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in the first part and then discuss how to trade & risk manage them in the second (which I’ll post tomorrow morning). Finally, I’ll discuss the best case scenario to trade them that has a very good risk reward.
A quick (boring) intro:
Ratio spreads (RS): Short OTM/ATM options
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and go long options that are more in the money than the options shorted. Quantity of options shorted are in multiples of quantity of options bought.
Factors that affect ratio spreads:
(super important)
Delta wrt TTE – As we near expiry, delta of OTM option goes down(see pic)
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Thought of posting a primer on stochastic processes that’ll be useful for any future posts on whether deriving Black’s formula for pricing calls/puts (my next post and should be a quick one) or discussing interpolation of vol surfaces (SVI) etc.
This should also help understanding my VIX derivation post better.
Any let's get started.
Any underlying variable, be it a Nifty/BankNifty, USDINR, crude etc, can be represented as a stochastic process with a drift and a diffusion(random) component.
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Think of a stochastic process as a random variable evolving with time OR a collection of random variables that have been gathered at different times (Usually all stochastic processes, expectations are always defined under some probability measure but I’m not touching on
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#Distribution Below is how an index return distribution can "potentially" evolve with time AS OBSERVED at starting time t=0.
As an example, one can view this as a potential #Nifty return distribution with PDFs given by Nov month end options (t=1), Dec month end options (t=2)..
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and so on (T=1 can be weekly also but I reckon weekly distributions won't look that smooth based on what I observed of option price/IV behavior).
Things to note:
At t=0 nothing is random, everything deterministic and pdf is a dirac-delta function.
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And with time, probability of index moving away from its mean (colored with orange ticks) goes up and so pdf spreads wider and it's peak value keeps coming down in order to assign more weight to returns away from its mean.
Once upon a time (many many many years ago!) one of my friends was asked to implement a forecasting project as the last stage of getting an offer from a prop trading firm in Europe. Below is the problem statement:
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Input data consists of (several hours of) trade and order book data for a listed product.
Order book data consists of time, bid/offer price and size resp. whereas the trade data
consists of time, price and volume.
Objective: Build a quantitative model to trade this instrument.
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My friend was a uni. chicago booth grad but had a lazy arse so I helped him implement it with the help of a common HFT friend. We both knew shit and the project was all implemented through the HFT guy’s guidance. I’m just presenting the report below. Check out.
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