Arnab Nayak

Assistant Vice President, Nomura services

Location: Mumbai, India Hand phone: +91-974******* Date of Birth: 24/04/1988

Email: ac4hzu@r.postjobfree.com

Work Experience

Assistant Vice President ( July 2017- Current )

Nomura Services, Mumbai, India

Model Validation Quant( Risk Models )

Project work:

o VaR calculation methodology: Investigate unexplained PnL materiality for greek based pnl calculation vs full revaluation across different asset classes. Investigate source of large discrepancy due to unstable gamma of binary payoff in CMS dual rate option trades. Similar analysis on agency MBS TBA bonds, equity based VIX options, forwards.

Associate ( June 2013-June 2017 )

Goldman Sachs, Bangalore, India

FICC Macro Strategist (APAC Macro Trading Desk ) o Specializing in pricing of various FX and Rate based long dated hybrids structured products ex. PRDC, Callable inverse floaters, Multi-Range Accruals, Bermudans, Swaptions and other structured OTC fixed income derivatives using HJM framework for Rates, Local Vol model for FX dynamics. o Support day-to-day activity, Pnl explanation, portfolio analysis under various market constraints, investigate irregularity in greeks etc.

Project work:

o American Monte Carlo: For trades with multiple call optionality feature use Kernal regression instead of simple OLS regression reducing sub-optimality in call decision due to extreme observations. Thus avoiding pricing arbitrage for out of the money call options by making sure call value to be positive. Reducing Monte Carlo noise as well.

o Multi-FX Copula Model: For structured trades which are sensitive to multiple FX, rate terminal distribution and correlation develop model-implied unmarked correlation parameters between quanto rates and FX using simple linear algebra. Thus stable random number generation using robust correlation matrix and good fit in bootstrap calibration. Enabling desk to efficiently risk manage and trade more exotic products.

o FX Local Vol model uplift for Quanto trades: For univariate structured trades where coupons are Libor or CMS linked and payoff is in a foreign currency, uplift underlying FX vol model to be able to calibrate to smile instead of ATM calibration only.

o Selective call window for risk management and Totem price submission: For exotic structured products with American call feature implement pricing and risk management strategy that enables the trade to use selective time periods where price of the call option is taken account into pricing and thus bucketing, concentrating risk more efficient way. Reducing transaction cost and enabling efficient risk management. Also price-model independent call value control enabling to match prices against Totem. o Parameterization of calibration instruments: For emerging market currencies in Asia implement dynamic method for choosing calibration instruments based on trade specification for rate based Linear Gauss Markov volatility model( HJM framework ). Avoiding static choice of calibration instruments where market may be illiquid and hence potential incorrectly marked volatility surface leading to bad calibration.

o Notes Automation: Coordinating with operations and technology division build life cycle events platform using #slang# ( GS proprietary coding language )for exotic structured complex notes issued by GS ( similar to US treasury notes ) for easier and automated way of cash-flow generation and cross verification each time trade evolves and thus reducing operational risk and manual labor. Education

MSc. in Applied Mathematics (2011-2013)

Chennai Mathematical Institute (Chennai, India)

Coursework: Optimization, Algorithms, Data Mining & Machine Learning, Econometrics, Simulation techniques. Master and Bachelor of Statistics. (2005-2010)

Indian Statistical Institute (Kolkata, India)

Coursework: Probability, Linear Algebra, Statistical Inference, Numerical Analysis, Differential Equation, Applied Multivariate Analysis, Bayesian Inference, Time Series Analysis, Regression Techniques. Skills and Interest

Professional Assets: Fast learner, enthusiastic, team oriented, hard working. Languages: English, Hindi.

Interest: Probability, Numerical Optimizations, Machine learning. Statistical Software: Matlab, R.

Programing Languages: C++, Python, #Slang#( GS proprietary language for quant researchers ) Operating systems: Windows, Linux.

Software: Microsoft office suite (Excel, PowerPoint, Word)

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