Jawwad Farid Profile picture
Apr 29, 2021 7 tweets 3 min read Read on X
In the modeling world we use a term marking a model to market.

If you have a pricing model and use that to estimate price of a security, how far off are your estimates compared to the market?

The mismatch between the model price and the market price is your model error.
With some models we let the error ride. With others we need to calibrate it down to zero.

A family of models where the pricing model is calibrated to match current market prices is called an arbitrage free model.

Remember the BTC model we built a few days ago. Not arb-free.
You can see the fit is not strong. There is a significant mismatch between actual market prices and model prices.

Here are two variations of the same model with market price calibration related adjustments. Still not arbitrage free, but much better fit.
How did we get here?

We changed the criteria for regression fit. Rather than using absolute prices for fit we used relative change. The same dataset, same technique much better fit.

But you can see it is not arbitrage free.
Same concept, different market. Bond prices.

We model the underlying rate curve, fit prices of bond on the curve and then match the same prices to market prices by tweaking the model till we get perfect fit.

This is what the end result looks like. Arbitrage free.
Now option markets.

With calls and puts we get a range of instruments across maturity horizon and strike prices.

Think yield curves in bond markets. Think a curve of volatility in option markets. Except not a curve but a surface.

Each point on the surface maps to an option
Your model uses a volatility estimate to price an option.

The price from your model may not match the market price. You change the volatility to get to a point where prices match.

Implied volatility is the volatility estimate where market and model prices match.

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More from @rebootdude

Jan 31
Had two insightful chats with two fellow academics and educationists yesterday.

The topic? What is it that we need to change to improve state of higher education in universities we teach in.

Here is a quick summary / the cliff notes edition of the conversation.
The purpose of undergraduate education?

Help our students discover, explore, develop and acquire interests.

A more ambitious goal? To get comfortable with personal performance windows and envelopes.

What are you really capable of? What is it that you want to do? Who are you?
The "who are you" question has been a key question in my life.

Self awareness is a powerful personal attribute that simplifies decision making and goal setting.

Because once you have answered this, you can also answer where do you want to go and what do you want to do?
Read 22 tweets
Sep 19, 2023
SWIFT MT 103. Foreign Currency Wire Transfers.

A single customer credit transfer made on the SWIFT network.

The bane of remittance/ free lancer / tech founder challenge across banking sector in Pakistan. More so for small businesses / founders that receive sub $ 1,000 wires.
Once upon a time in a different life we wrote, ran and supported treasury systems that generated MT 103 messages for local banks.

Given how treasuries front / back offices are structured and how all $ payments are consolidated and cleared via NYC, workflow was simple.
Collect all outgoing payments, bundle them together into one balance for one market / relationship / target account.

Send a shorter message to primary US$ Nostro account targeted for out going transfers for one amount.

If there is a shortfall, move money around.
Read 21 tweets
Feb 28, 2023
If you work with risk, insurance, trading or pricing, loss distributions are a key concept.

Traditional or introductory statistics courses don't have time to do justice to them.

We took a deep dive with automobile claims data (80,000 rows) in the ADA course.
With insurance models we are interested in two key parameters.

a) Frequency - the likelihood or probability of a claim.
b) Severity - the size of a claim, when a claim occurs.

Loss distributions focus on b).

If a claim occurs how big or bad is it likely to be?
Actuarial teams use data analysis to identify common attributes across high severity claims.

A large majority of motor insurance claims are within pricing limits.

A small percentage are 25 - 50x larger.

If we could identify the profile, we could price/underwrite better risks.
Read 10 tweets
Nov 12, 2022
How do you encourage or foster creativity?

Cross pollination and collaboration of ideas from different worlds and backgrounds?

We learn more when we

a) Work with individuals and teams with different perspectives.

b) Remove crutches that we use without thinking about them.
How would you design a high fidelity prototype for a game without using technology or standard art?

You level the playing field in one stroke. No developer, artist or designer would have an edge.

Only creativity would rule.

Welcome to @ibakarachi @Mindstormstudio @RaptrGames
Open to all undergraduate and graduate programs in Karachi.

If you are:

a) Avid game player
b) Aspiring game designer
c) Care about creative expression
d) Curios about what make games playable, addictive, instant hits, or
e) Love real time community feedback
Read 5 tweets
Nov 11, 2022
Fun talk today by Dr. Alnoor Bhimani @ibakarachi on data as a product, monetizing data, qualitative insights, and decision making processes.

And what all of the above implies for business school education.
My favorite takeaway?

If the data is the new oil for the knowledge economy, and we produce a lot of it, how do we productize and monetize it as an economy.

If we don't, someone else will. No one had put it down or explained it this clearly.

Data is our new edge.
Dr. Bhimani also made me a convert to the NFT cause.

I don't think he realized what he did but he said something so profound and powerful that it finally connected the dots for me.

"We may not be willing to pay for art produced by an AI today, but one day we will."
Read 4 tweets
Nov 9, 2022
The most fun engagements in my senile years are ones where I pick a new skill, crack a difficult problem or learn stuff I didn't know before.

Easier to do this type of work when you run a small specialized shop without distractions of a large team.

Small shop. Large footprint.
2018-19.
IFRS 17.
Teaching Portfolio Optimization Models + Algorithms + Financial Modeling.

2019-20.
First and second half marathon.
Remote work + Zoom Cohort based live training.
Founder Puzzles. Product launch.

2020-21.
Surviving Covid + recovering.
Close call.
3rd half.
2021-22.
Context switching. Fixing my knee.
Growth and Economic Complexity.
Raising 6 at 18 post.
Scale multiples and idea selection framework.
The Pakistan dataset.
Ed-Tech and crypto deep dive.
Teaching Venture Capital + Fintech and Financial Innovation.
Shipping FP, 2nd Ed.
Read 4 tweets

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