- Deep understanding of derivatives and empirical asset pricing theory (Black-Scholes framework, stochastic calculus, jump diffusion etc.).
- Understanding of VaR, modelling of VaR using monte-carlo simulation, historical and parametric approach.
- Understanding of interest rate curves, modelling interest rates, calibration of stochastic interest rate models (BK, HW etc.)
- Understanding of Volatility Modelling (HJM, SABR, LMM frameworks)
- Independently able to price derivative instruments of vanilla and exotic payoff structures (swaps, options, CDS etc.) using analytical, simulation, trees etc.
- Understanding of counterparty risk - CVA, DVA, FVA (using analytical and simulation approach). Independently able to validate pricing, market risk models and the underlying concepts.
- Understanding of quantitative methods like bootstrapping, numerical simulation, trees and their application in quantitative finance.
- Understanding different components of market risk and development/validation of risk models and pricing models.
Must have :
- Background in computational/quantitative/financial engineering.
- Master's/PhD in Computational Math/Financial Engineering/other quantitative disciplines.
- Sound knowledge of computer programming at least any two among Python, R, C++, Java.
- Excellent communication and time management skills.
- Co-ordinate with different stakeholders, across geographies.
Good to have :
- Certifications like CQF and FRM.
- Ability to program in multiple languages/platforms.
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