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.
Structured products – The ART of risk transfer
Exploring the risk thrown up by autocallables has created a new family of structured products, offering diversification to investors while allowing their manufacturers room to extend their portfolios, writes Manvir Nijhar, co-head of equities and equity…
Goldman improves execution ‘by 50%’ with new algos
Bank uses neural networks and other AI tools to cut slippage in stock trading
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
AllianceBernstein uses AI to sidestep ‘growth trap’
Random forest model aims to sort success stories like Amazon and Netflix from fast-growth losers
How AI could tear up risk modelling canon
BlackRock, MSCI, LFIS among firms looking to replace traditional, linear risk models
A tech-driven transformation
A panel of experts explores how greater collaboration between risk and finance teams can garner significant benefits and add value, how technological innovation is making the regulatory landscape more complicated to navigate and produce transformative…
Model risk management transformation
Financial institutions have been maturing their approaches to MRM and – as models become more complex and pervasive, and regulatory expectations continue to increase – leading financial institutions seek faster and further movement. Ashutosh Nawani, head…
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
One size does not fit all – Adapting to meet investment goals
Guillaume Arnaud, global head of quantitative investment strategies (QIS), and Sandrine Ungari, head of cross-asset quantitative research at Societe Generale, explore the benefits of QIS for investors, why flexibility is crucial for investors to meet…
A helping hand – Addressing industry concerns
The Basel Committee on Banking Supervision’s final revisions to the FRTB guidelines aim to address industry concerns around complexity and capital implications. A forum of industry leaders discusses whether the changes have been effective and how banks…
Cleaning noisy data ‘almost 70%’ of machine learning labour
Quants flag signal-to-noise ratio as key to reducing overfitting risk
Machine learning governance
The ability of machine learning models to read great quantities of unstructured data, spot patterns and translate it into actionable information is driving a significant uptake in the technology. David Asermely, SAS MRM global lead, highlights the need…
Systematic manager puts up guardrails for AI
Boston-based Acadian aims to limit risks from complex, machine learning algorithms
China Minsheng and SocGen team up for quant index product
CMBC Macro 1 signal index attracts $580 million as investors adapt to products without performance guarantees
Crowding can be good for quants (sometimes) – Goldman
Study finds timing dictates different results for convergent and divergent strategies in herd moves
Portfolio traders turn to tech – A new generation of strategies
Chris Bruner, head of US credit product at Tradeweb, explores the products that can help managers express portfolio views and how they can maximise the benefits they can reap by evaluating and understanding the price, risk and relative value of each…
