Quant Hub

Investment banks like Morgan Stanley, Goldman and JP Morgan have specialized Quant desks which focus on specific asset classes or trading strategies Here's a glimpse into some of the key quant desks - Electronic Trading Desk: This is the high-speed, algorithm-driven hub. Quants here develop and implement complex trading models that execute trades in milliseconds, capitalizing on fleeting market opportunities. They work closely with electronic traders to ensure the algorithms are optimized and react swiftly to market changes. Delta-One Desk: Delta One products are essentially derivatives that have a delta close to or equal to one. This desk focuses on replicating the performance of underlying assets through a combination of various instruments. Quants here design strategies using futures, options, swaps, and other derivatives to achieve a "delta-one" exposure, meaning the price movement of the created product mirrors the underlying asset. Exotics Desk: This desk deals with complex, non-standard financial instruments like barrier options or knock-in puts. Quants here develop pricing models and risk management frameworks for these exotic derivatives, catering to clients with sophisticated investment needs. FX (Foreign Exchange) Desk: Here, quants build models for analyzing currency exchange rates and developing trading strategies. They incorporate factors like economic data, interest rate differentials, and political events to predict currency movements and advise traders on how to navigate the FX market. Rates Desk: This desk focuses on interest rate products like bonds, swaps, and futures. Quants here develop models to price these instruments, assess interest rate risk, and create strategies for clients looking to manage their exposure to interest rate fluctuations. Banks might have additional specialized desks focusing on commodities, credit derivatives, or even specific industries. The specific structure depends on the bank's overall strategy and client base.

We evaluated the content of 50+ online resources for learning Quant Finance and picked out the top 11 recommendations 1) IIT Kanpur Quant Finance course by Professor Raghu Nandan - https://lnkd.in/dHewAfYc 2) IIT Bombay, Stochastic Process course by Prof. Manjesh Hanawal https://lnkd.in/gRq6f-NJ 3) MIT Financial Mathematics course - https://lnkd.in/gwmmyWrz 4) Yale Univerity Quantitative Finance course - https://lnkd.in/gqGXFGeZ 5) Mathematical Methods for Quantitative Finance - https://lnkd.in/gPTRQMdc 6) IIT Guwahati - Mathematical Finance https://lnkd.in/gHwrzhEa 7) Introduction to computer science and programming in Python - https://lnkd.in/gFX4XZPp 8) Computational Finance by Leipzig University- https://lnkd.in/gHMkr8Ue 9) IIT Kanpur Probability and Stochastics for Finance - by Professor Joydeep Dutta - https://lnkd.in/ggDJkVgE 10) Harvard Applied Mathematics - https://lnkd.in/gzrYZ7Ne 11) Harvard CS50 – Full Computer Science University Course - https://lnkd.in/gK9qVMQX (If you don't have a coding background) We are conducting a masterclass at Quant Insider "Cracking a Career as a Quant, Quant Developer, or Strat." with Andrey Chirikhin, " He will be talking about the following - Cracking the first quant role across the Buy side, sell side, Fintech, and Financial Consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Daily task of a Quant and how to excel them to faster career trajectory Date - 13th October 2024 (Sunday) Time - 9:30 PM IST / 5:00 PM London time Registration Link - https://lnkd.in/gikCxT72 ------------------------------------------------------------------------------------- ➡ Kickstart your Quant Interview Prep ↗ ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) ↗ Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) ↗ Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte and Project Handbook Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Quant Insider Resume Writing / Review Session - You will learn to make a tailored resume for the Job Description make effective use of keywords, and bullet points to create an impactful resume. https://lnkd.in/gi6yznXa Machine Learning for Finance course- Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the ML for Finance course

Baruch College is a rising star when it comes to its MS in Financial Engineering (MFE) program, Here are lecture notes on "Arbitrage-free SVI volatility surfaces" by Jim Gatheral (Ex-Managing Director Merrill Lynch) We are conducting a masterclass at Quant Insider "Cracking a Career as a Quant, Quant Developer, or Strat." with Andrey Chirikhin, " He will be talking about the following - Cracking the first quant role across the Buy side, sell side, Fintech, and Financial Consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Daily task of a Quant and how to excel them to faster career trajectory Date - 13th October 2024 (Sunday) Time - 9:30 PM IST / 5:00 PM London time Registration Link - https://lnkd.in/gikCxT72 ------------------------------------------------------------------------------------- ➡ Kickstart your Quant Interview Prep ↗ ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) ↗ Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) ↗ Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte and Project Handbook Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Quant Insider Resume Writing / Review Session - You will learn to make a tailored resume for the Job Description make effective use of keywords, and bullet points to create an impactful resume. https://lnkd.in/gi6yznXa Machine Learning for Finance course- Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the ML for Finance course

Here’s a breakdown of how SSVI ensures that the volatility surface is free of static arbitrage (i.e., free of both butterfly arbitrage and calendar spread arbitrage) and can fit market option data accurately.🧵  https://lnkd.in/gAu4z6uq 

Here are the key takeaways from the paper titled Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs by Nigar Karimova: Models Compared: The traditional Delphi model (based on logistic regression). A random forest model based on machine learning techniques. Key Variables: Both models analyzed several key financial variables including revenue growth, cash flow variance, debt-to-equity ratio, net profit margin, commodity price sensitivity (e.g., oil, cotton, grain prices), and market conditions (GDP growth). Performance Metrics: Accuracy: Increased from 0.69 in the Delphi model to 0.83 with the random forest model. Precision: Improved from 0.65 to 0.81. Recall: Increased from 0.56 to 0.77, reflecting fewer false negatives. F1 Score: Improved from 0.58 to 0.79, a balanced metric of precision and recall. Advantages of Random Forest: The random forest model captures non-linear relationships between variables, something that the Delphi model, based on logistic regression, struggled with. It is better suited to handling dynamic variables and non-linear data, which is particularly useful in the volatile environment of SMEs in emerging markets like Azerbaijan. Implications: The use of AI, particularly the random forest model, could lead to more accurate credit risk assessment, reducing unfair credit rejection for SMEs, and enabling financial institutions to minimize default risk more effectively. However, potential biases and ethical challenges need to be carefully managed. Checkout our course Machine Learning for Finance Designed and Taught by Industry Veterans with years of working at the biggest Investment Bank and trading firms at Wallstreet - https://lnkd.in/eyXnPRwz Use Coupon code - "EARLYBIRD20" for 20% off on the course Machine Learning Concepts Customized to Finance Separate modules for each AI and Machine Learning Type with exhaustive concepts. Course Description Supervised Learning Regression and Classification models 1. Linear and Logistic Regression 2. Random Forest and GBM 3. Deep Neural Network (including RNN and LSTM) Includes 6+ case studies Unsupervised Learning Clustering and Dimensionality Reduction 1. Principal Component Analysis 2. k-Means and hierarchical clustering Includes 5+ case studies Reinforcement Learning and NLP Value/Policy based RL models and sentiment analysis 1. Deep Q- Learning RL model 2. Policy-based RL models 3. Sentiment based trading Includes 4+ case studies Your document has finished loading

"We trade the volatility smile" – gets crushed by volatility term structure shift We are conducting a masterclass at Quant Insider "Cracking a Career as a Quant, Quant Developer, or Strat." with Andrey Chirikhin, " He will be talking about the following - Cracking the first quant role across the Buy side, sell side, Fintech, and Financial consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Daily task of a Quant and how to excel them to faster career trajectory Date - 13th October 2024 (Sunday) Time - 9:30 PM IST / 5:00 PM London time Registration Link - https://lnkd.in/gikCxT72 ------------------------------------------------------------------------------------- ➡ Kickstart your Quant Interview Prep ↗ ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) ↗ Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) ↗ Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte and Project Handbook Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Quant Insider Resume Writing / Review Session - You will learn to make a tailored resume for the Job Description make effective use of keywords, and bullet points to create an impactful resume. https://lnkd.in/gi6yznXa Machine Learning for Finance course- Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the ML for Finance course

This Mathematical Finance CheatSheet will help you Ace your Quant Interviews It covers important Stochastic Calculus topics and Quantitative Models We are conducting a masterclass at Quant Insider "Cracking a Career as a Quant, Quant Developer, or Strat." with Andrey Chirikhin, " He will be talking about the following - Cracking the first quant role across the Buy side, sell side, Fintech, and Financial consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Daily task of a Quant and how to excel them to faster career trajectory Date - 13th October 2024 (Sunday) Time - 9:30 PM IST / 5:00 PM London time Registration Link - https://lnkd.in/gikCxT72 ------------------------------------------------------------------------------------- ➡ Kickstart your Quant Interview Prep ↗ ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) ↗ Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) ↗ Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte and Project Handbook Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Quant Insider Resume Writing / Review Session - You will learn to make a tailored resume for the Job Description make effective use of keywords, and bullet points to create an impactful resume. https://lnkd.in/gi6yznXa Machine Learning for Finance course- Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the ML for Finance course

This you will not find on YouTube or any other platform, Understanding the concept of New Business, Unrealized PnL, Realized PnL, and Day-on-Day Unrealized PnL is crucially important for anyone working as a quant, especially in trading, risk management, and portfolio management roles. The image provides a detailed breakdown of the different components of Profit and Loss (PnL) tracking for a trading session, distinguishing between "New Business," "Unrealized PnL," "Realized PnL," and "Day on Day Unrealized PnL." Here's an analysis of each component and how they are calculated: Key Components in the Chart: New Business (Blue Section) This refers to the intraday PnL that arises from opening or increasing a position, whether long or short. In the table, this is highlighted when a new trade is initiated. For example, on Day 1, a buy order of 1 share of Stock A at $100 results in a new business PnL of $10, which is the difference between the closing price ($110) and the purchase price. Unrealized PnL (Green Section) Unrealized PnL refers to the potential PnL if the position were fully liquidated at the current market price. It tracks the market value of the current position without any actual selling. For example, on Day 1, after buying Stock A for $100, its price increases to $110, resulting in a $10 unrealized PnL. It continues to update until the position is closed, such as on Day 3 when the stock is sold at $115, realizing $5. Realized PnL (Red Section) Realized PnL refers to the profit or loss made by actually selling or reducing a position. For instance, on Day 3, the sell order at $115 results in a realized PnL of $5. The realized PnL accumulates over time, shown in the "Session Realized" and "Cumulative Realized" columns. Day on Day Unrealized PnL (Purple Section) This shows the change in the unrealized PnL from the previous day. It helps track day-to-day fluctuations in market value, without any position changes. For example, from Day 1 to Day 2, the unrealized PnL stays at $10, as the market value of Stock A remains at $120. However, on Day 3, the unrealized PnL drops to $0 when the stock is sold, and thus there is no further day-to-day unrealized movement. We are conducting a masterclass at Quant Insider "Cracking a Career as a Quant, Quant Developer, or Strat." with Andrey Chirikhin, " He will be talking about the following - Cracking the first quant role across the Buy side, sell side, Fintech, and Financial consulting. - How to build a long-term career and grow as a Quant Professional - The inside of the Quant Industry across different roles. - Required skill sets (hard and soft skills) - Daily task of a Quant and how to excel them to faster career trajectory Date - 13th October 2024 (Sunday) Time - 9:30 PM IST / 5:00 PM London time Registration Link - https://lnkd.in/gikCxT72 (PPT credit Andrey Chirikhin and QA profession Course -https://lnkd.in/gikuMSRg)

The paper titled "Option Pricing using Quantum Computers" explores how quantum computing can provide a computational advantage in pricing financial derivatives like options. Here are the key takeaways: Quantum Amplitude Estimation (QAE) for Option Pricing: The paper focuses on leveraging amplitude estimation, a quantum algorithm, which offers a quadratic speed-up over classical Monte Carlo methods for calculating option prices. It is demonstrated that quantum algorithms can efficiently price vanilla options, path-dependent options, and multi-asset options by encoding the relevant probability distributions and payoffs into quantum circuits. Challenges of Classical Monte Carlo Methods: Classical Monte Carlo simulations are highly flexible and widely used but suffer from slow convergence rates (O(M^(-1/2))), making them computationally expensive, especially for complex options like barrier and Asian options. The quadratic speed-up of QAE reduces this to O(M^(-1)), where M is the number of quantum samples, significantly improving efficiency. Quantum Circuits for Option Pricing: The paper outlines how to construct quantum circuits that encode the probability distributions of asset prices and implement the option payoffs, including path-independent and path-dependent options (such as barrier and Asian options). These circuits use advanced quantum techniques like controlled Y-rotations, comparators, and phase estimation to model complex payoff functions. Real-world Implementation: The authors demonstrate quantum circuit implementations on real quantum hardware (IBM Q Tokyo) and use an error mitigation scheme to address the noise in quantum systems. This is a crucial step toward making quantum option pricing practical. Checkout our course Machine Learning for Finance Designed and Taught by Industry Veterans with years of working at the biggest Investment Bank and trading firms at Wallstreet - https://lnkd.in/eyXnPRwz Use Coupon code - "EARLYBIRD20" for 20% off on the course Machine Learning Concepts Customized to Finance Separate modules for each AI and Machine Learning Type with exhaustive concepts. Course Description Supervised Learning Regression and Classification models 1. Linear and Logistic Regression 2. Random Forest and GBM 3. Deep Neural Network (including RNN and LSTM) Includes 6+ case studies Unsupervised Learning Clustering and Dimensionality Reduction 1. Principal Component Analysis 2. k-Means and hierarchical clustering Includes 5+ case studies Reinforcement Learning and NLP Value/Policy based RL models and sentiment analysis 1. Deep Q- Learning RL model 2. Policy-based RL models 3. Sentiment based trading Includes 4+ case studies

In the SABR model, Nu and Rho are key parameters that influence the shape of the volatility smile Nu (ν) in the SABR Model: Nu is referred to as the "volatility of volatility" parameter. It measures the extent to which the volatility itself is expected to fluctuate over time for the forward rate process. Impact on the Volatility Smile: Nu affects the height of the volatility smile: High Nu: Results in a steeper and more pronounced smile, meaning more variability in implied volatility across different strike prices. Low Nu: Leads to a flatter smile, with less variation in implied volatility. This can be observed by analyzing the prices of straddles and strangles, which involve options at strikes equidistant from the at-the-money (ATM) level. Plot Explanation (Left Plot): Red Line (High Nu): represents a higher value of Nu, leading to a taller parabolic curve. The implied volatility is higher for both in-the-money and out-of-the-money options, and the smile curve reaches a higher peak. Green Dashed Line (Low Nu): represents a lower value of Nu, leading to a flatter, shorter smile. Lower Nu results in less variability in implied volatility across strike prices. Rho (ρ) in the SABR Model: Rho represents the correlation between the forward rate and its volatility. It determines the skewness or slope of the volatility smile and reflects how the forward price movement relates to changes in volatility. Impact on the Volatility Smile: Rho affects the slope of the volatility smile: High Rho: Results in a steeper slope or tilt in the volatility smile, meaning the implied volatility increases more rapidly for out-of-the-money calls and decreases faster for out-of-the-money puts. Low Rho: Leads to a flatter or less tilted volatility smile, with less pronounced changes in volatility. Plot Explanation (Right Plot): Blue Line (High Rho): Represents a higher value of Rho, leading to a steeper slope in the smile. The volatility increases more rapidly for out-of-the-money call options and decreases faster for out-of-the-money put options. Orange Dashed Line (Low Rho): Represents a lower value of Rho, leading to a flatter slope in the smile. Changes in implied volatility with respect to strike prices are less pronounced. We are conducting a masterclass at Quant Insider "Cracking a Career as a Quant, Quant Developer, or Strat." with Andrey Chirikhin, " Date - 13th October 2024 (Sunday) Time - 9:30 PM IST / 5:00 PM London time Registration Link - https://lnkd.in/gikCxT72 ------------------------------------------------------- ➡ Kickstart your Quant Interview Prep ↗ ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) ↗ Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) ↗ Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte and Project Handbook