We enable risk management
Learn More01Women in Quantitative Finance: Interview with Mika Lindahl
We are excited to share the story of Mika Lindahl, an associate consultant and a team manager at Kidbrooke Advisory. She has been working at the company for more than 1.5 years now, successfully balancing deep technical expertise with excellent leadership skills. In this short interview piece, Mika tells us about her career choice, her role in quantitative finance and her message to women considering a similar path.
Read More02February 2019 News Update
We are excited to present our news selection for February 2019! Although anticipated to be a conventional means of providing investment advice in the longer term, automated financial advice is still an emerging subsector in the global wealth management industry. Some sources anticipate that the expansion and specialisation of such services would bring the developing digital advice providers to their maturity, while the early adopters evaluate the lessons learnt from the implementation of robo-advisers. Meanwhile, the large banks do not rush to engage in the FRTB implementation projects before the local regulators come up with the final version of the new rules. At the data intelligence side, the data scientists deploy artificial intelligence to assess the ESG practices of companies.
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Digital Advice
Financial advice is being digitalised and is increasingly provided on an automated basis. Download our summary of the latest developments within this exciting field.
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Machine learning applications have become more prominent in the financial industry in recent years. Our new article series is exploring the benefits and challenges of using the self-normalizing neural networks (SNNs) for the calculations of the liquidity risk. The first piece of the series introduces the main concepts used in the investigative case study on the Swedish bond market.
In the third and concluding article in the ALM using LMSC series, we focus on analyzing the optimal asset allocations in the context of changing asset classes as well as finding the optimal allocation by maximizing the risk-adjusted net asset value. The estimates based on the LSMC method are then compared to the estimates obtained from the full nested Monte Carlo method.
The second part of the series exploring the use of Least Squares Monte Carlo in Asset and Liability Management is focused on evaluation of accuracy and performance of this method in comparison to full nested Monte Carlo simulation benchmarks.
In the first part of the ”Asset and Liability Management using LSMC” article series, we outline an ALM framework based on a replicating portfolio approach along with a suitable financial objective. This ALM framework, albeit simplified, is constructed to provide a straightforward replication of the complex interactions between assets and liabilities. Moreover, a brief introduction to the LSMC method used to generate all underlying risk factors is presented.
What we do
We improve decision making under uncertainty
Our work empowers millions of people to make, or benefit from, informed financial decisions under uncertainty. Asset liability management, capital requirements and automated financial advice - everything we do helps support our vision that everyone should have access to world class risk management tools.