Quantitative finance
WHAT IS THIS? Quantitative finance is a field of applied mathematics concerned with financial markets. In banking, it spread from the pricing of derivatives to the modelling of credit, market and operational risks. Today’s quantitative analysts are scattered across a range of functions, from risk management and model validation, to data science, algorithmic trading and regulatory compliance.
Quant Guide 2020: University of York
York, UK
Quant Guide 2020: Imperial College London
London, UK
Quant Guide 2020: University of Paris
Paris, France
Quant Guide 2020: Fordham University
New York City, US
Quant Guide 2020: New York University (Tandon School of Engineering)
New York City, US
Quant Guide 2020: Baruch College, City University of New York
New York City, US
Quant Guide 2020: Boston University (Questrom School of Business)
Boston, Massachusetts, US
Quant Guide 2020: University of Minnesota
Minneapolis, US
Quant Guide 2020: KU Leuven
Leuven, Belgium
Quant Guide 2020: Columbia University
New York City, US
Quant Finance Master’s Guide 2020
Risk.net’s guide to the world’s leading quant master’s programmes, with the top 25 schools ranked
Princeton tops Risk.net Quant Guide for second year running
UK’s Imperial ranks sixth; first university from mainland China features
Rising star in quant finance: Blanka Horvath, Aitor Muguruza and Mehdi Tomas
Risk Awards 2020: New machine learning techniques bring ‘rough volatility’ models to life
Looking forward to backward‑looking rates
Interbank offered rates are critical in the world of contracts and derivatives, acting as reference rates in millions of financial contracts and with a total market exposure in the hundreds of trillions of dollars. Bloomberg explores why offering…
The rise of the robot quant
The latest big idea in machine learning is to automate the drudge work in model-building for quants
Deep hedging and the end of the Black-Scholes era
Quants are embracing the idea of ‘model free’ pricing and hedging
Fishing for collateral with neural nets
SocGen quant uses deep learning technique to optimise collateral substitution
Optimal posting of collateral with recurrent neural networks
Pierre Henry-Labordère applies neural networks to a control problem approach for managing collateral
CVA wrong-way risk: calibration using a quanto CDS basis
Tsz-Kin Chung and Jon Gregory calibrate wrong-way risk with the help of quanto CDS values
Crowding can be good for quants (sometimes) – Goldman
Study finds timing dictates different results for convergent and divergent strategies in herd moves
Keep it real: tail probabilities of compound distributions
Igor Halperin proposes new approach to compute probabilities of heavy-tailed distributions
UK quant academics fear Brexit brain drain
Brexit hitting graduate jobs, funding and “driving European academics away from the UK”, say universities
Dabbling in data science won’t cut it
Banks are seeking data-led boost for research arms – only a few will succeed