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Kshitij Anand Profile
Kshitij Anand

@kshitijandquant

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Quant Risk Analyst | Someone who loves Quantitative Finance

Gurgaon, India
Joined November 2024
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@kshitijandquant
Kshitij Anand
9 hours
Quant Bible by MIT Sloan Business Club. Access it from here:- https://t.co/VOH0GvrRUu
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@kshitijandquant
Kshitij Anand
4 days
VIX derivation explained:- A really good mathematical exercise. Please DM me in case of any doubt. https://t.co/DT6ResAGXb
drive.google.com
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@kshitijandquant
Kshitij Anand
6 days
Maths time!! Derivation of VIX:- A really good blog written by Mr. Gregory Gundersen. https://t.co/KhK2XKFdXB I will soon release a video explaining the derivation, along with the derivation of the India VIX as well. #Quant #QuanitativeFinance
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@kshitijandquant
Kshitij Anand
9 days
Lambda functions from C++11 to C++23 by Quasar: https://t.co/JG4QHDrWnv Do bookmark his blog for really good insights on C++ for Quantitative Finance.
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@kshitijandquant
Kshitij Anand
11 days
Algorithmic Differentiation:- > Multiple Risk Factors > Need to calculate sensitivities across all of them > AD makes it fast Do read the notes I have prepared on AD:-
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linkedin.com
Algorithmic Differentiation: An Extra Mile Toward Efficient Computation In Quantitative Finance, we often deal with multiple risk factors and need to compute sensitivities to those factors, whether...
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@kshitijandquant
Kshitij Anand
13 days
A good collection of blogs on Cpp for Quantitative Finance written by a guy who is working at Citadel as Quant trader. https://t.co/NjdnGyU4L8 If you have more such blogs on cpp, do share in comments.
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@kshitijandquant
Kshitij Anand
15 days
I remember a project that I did long back using KKT; it was risk parity portfolio optimisation. The mathematics behind risk parity portfolio and this particular Optimization made me learn a lot. Absolutely amazing!! Project material btw:-
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topmate.io
Risk Parity Portfolio Implementation
@probnstat
Probability and Statistics
16 days
Karush–Kuhn–Tucker (KKT) conditions characterize optimal solutions of constrained optimization problems by linking gradients, constraints, and multipliers. In probability, they guide maximum-likelihood estimation under constraints. In machine learning, they shape SVMs,
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@kshitijandquant
Kshitij Anand
15 days
A frame which will display live volatility surface while I cook (the trade?)!
@JesterJum
Jum
16 days
This is our new kitchen. What would you put here?
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@kshitijandquant
Kshitij Anand
16 days
Can someone familiar with Hedge Funds confirm whether hedge funds still use index-rebalancing strategies, or has the opportunity become too dry to extract any juice? #Quant #Hedgefunds
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@kshitijandquant
Kshitij Anand
17 days
I support Quant Club at @febsiitbbs because I am one of the founding member and they are doing quite good in Quantitative Finance. πŸ™‚πŸ™‚
@heis_fede
𝐟𝐞𝐝𝐞𝐯𝐚π₯𝐯
18 days
Be honest, what is the reason behind the club you support?
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@kshitijandquant
Kshitij Anand
18 days
Best combo!! πŸ™ŒπŸ™Œ
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@kshitijandquant
Kshitij Anand
18 days
Suppose the ATM implied volatility is 25% when the index is at 200. If the index rises to 220, the sticky-moneyness rule says the 220 strike option (now the new ATM) should carry the same 25% implied volatility. That’s exactly why we call the behaviour sticky moneyness / sticky
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@kshitijandquant
Kshitij Anand
18 days
Sticky Delta, or Sticky Moneyness, implies that the implied volatility will adjust so that the Delta of the option remains constant even when the underlying stock price changes.
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@kshitijandquant
Kshitij Anand
18 days
You hold a put option on stock ABC with a strike of $100, and the stock is currently trading at $100. Now the stock drops to $95. Under a sticky strike assumption, the implied volatility for the $100 strike put stays the same, even though the underlying price moved lower.
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@kshitijandquant
Kshitij Anand
18 days
Sticky Strike Vs Sticky Delta Sticky Strike refers to a situation where the implied volatility (IV) of an option remains constant for different strike prices, even as the price of the underlying asset changes.
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@kshitijandquant
Kshitij Anand
19 days
Graviton vs Quantbox on r/quantindia πŸ˜‚πŸ˜‚ #QuanitativeFinance
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@kshitijandquant
Kshitij Anand
19 days
Both sides of the market (importers/exporters, global funds, banks) hedge moves in both directions, which lifts volatility on both wings. Because of this two-way hedging and generally more symmetric return distributions, IV ends up rising on both sides of the strike axis, giving
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@kshitijandquant
Kshitij Anand
19 days
An interesting but easy question, why do we observe an Implied Volatility Smile rather than skew in FX Options? FX options usually show a smile instead of a strong skew because the currency market is naturally more balanced than equities. In equities, everyone worries about
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@kshitijandquant
Kshitij Anand
21 days
Learn and implement with peers!!
@ifeel_garv
Gaurav
22 days
How to be insanely good at C++ like able to build low latency systems
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@kshitijandquant
Kshitij Anand
21 days
If you are interested in sell-side quant roles, learning Partial Differential Equations and how to solve them using Finite Difference Approach is a must. You can also learn new age methods like ADI ( Alternate Direction Implicit ) and have projects based on it. #Quant
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