Bloomberg's Equity and Convertibles Quant Team is responsible for the design and implementation of analytics that support the calibration of equity implied data (forward curves, implied volatilities) and the pricing & risk of equity derivatives ranging from vanilla products and light exotics to the most sophisticated exotics including structured products and Convertible bonds.
These analytics serve the entire suite of Bloomberg products and services, including its terminal with 300,000+ clients, trading system solutions, enterprise risk management, and derivatives valuation services.
What’s the Role?
The Equity and Convertible Quant Team is looking for a derivatives quant experienced in the design and implementation of equity derivatives pricers, implementation of routines for the calibration of implied data, e.g. implied volatility, implied dividends, borrow costs, etc.
We’ll trust you to:
Review existing pricing engines, identify misbehavior (e.g. accuracy of calibration, stability of Greeks, etc…) and be able to investigate them and propose improvements
Assess model fitness for purpose by reviewing quality of P&L explain and be able to identify areas of improvement
Design and implement equity derivative pricers, e.g. PDE pricer for single asset derivatives, volatility derivatives pricers, e.g. variance swaps, volatility swaps, VIX options, …
Review existing implied data calibration algorithms and propose & implement improvements
Design & implement more robust & stable alternatives, e.g. explore & propose new parametrisation of the implied volatility, design more appropriate filtering rules for market data quotes input of the calibration, …
Be able to investigate misbehavior in the implied data calibration when they arise
Collaborate with engineering to agree best ways to integrate quant models, e.g. definition of appropriate quant APIs
Collaborate with quant developers to keep up to date with new developments and coordinate their integration within the equity library.
Interact with the business & external clients to present tools/findings
You'll need to have:
7+ years of experience*
Valuation modeling: experience in design & implementation of equity derivatives pricers local volatility, hybrid local volatility, local stochastic volatility, or Bergomi models.
Proven working experience with one of the following asset classes: Equities, Commodities, or FX
Implied data: experience in building implied market data e.g. implied dividends, implied volatility and a demonstrated track record of being successful at providing stable and accurate tools.
Proven knowledge of C++
Demonstrated effective communication with both internal and external stakeholders
Experience in Statistics, Kalman filter, Bayesian inference, maximum likelihood
Highly motivated individual who takes initiative and engages with clients, business & management to identify needs and propose/discuss solution
Collaborative mindset who seeks advice from colleagues and internal clients and is able to navigate the organization to identify synergies and leverage the work of others.
*Please note we use years of experience as a guide, but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.
If this sounds like you:
Apply if you think we’re a good match. We’ll get in touch with you to let you know the next steps, but in the meantime feel free to browse this: http://www.bloomberg.com/professional
Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or maternity/parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law.