Knowledge Base Articles
Part II: Self-Normalizing Neural Networks - Bond Liquidity Classification
In the second part of the article series, we outline a framework utilising both the Self-Normalizing Neural Networks (SNNs) and the logistic regression for bond liquidity classification. This framework is subsequently applied to the Swedish bond market in an investigative case study.
Part I: An Introduction to Self-Normalizing Neural Networks
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 self-normalising neural networks (SNNs) for calculating liquidity risk. The first piece of the series introduces the main concepts used in the investigative case study for the Swedish bond market.
Part III: Asset and Liability Management Using LSMC - Allocation Optimisation
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.
Part II: Asset and Liability Management Using LSMC - Accuracy and Performance
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.
Part I: Asset and Liability Management Using LSMC - Introduction to the Framework
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.
Part III: Cyber Risk Management, Security Controls and Insurance
In continuation of our discussion of cyber risk, this paper investigates the issues of cyber risk management within financial industry. In particular, we look into the process of determining the optimal size of the investments in cyber security as well as the quantification of the appropriate cyber insurance premiums.
Part II: Cyber Risk; A Prime Component of Operational Risk
In continuation of our discussion of cyber risk, this article reviews different methods and models, which can be used to analyse and quantify the risks of information security breaches faced by the contemporary financial industry.
Introduction to Credit Index Modelling
This article will discuss why it is important to model credit indices and detail a number of different approaches to this problem.
Summary: Swedish FSA releases consumer protection report
The Swedish Financial Supervisory Authority (FI) releases a yearly consumer protection report featuring customer security highlights in the Swedish financial industry. As in the previous years, the main risks related to customer security relate to mortgages and loans. The interest payments can potentially threaten the economies of the individuals in case of the economic downturn or the increase in interest rates. Another threat that is amplifying in the context of the digitalising society relates to customer data protection. The FI calls for more advanced security systems that would protect the consumer at all stages of a payment transaction. The improvement of these solutions is especially relevant in the context of increased instances of financial fraud. Finally, FI announces that the protection of the wealth management customers and the enforcement of the MiFID II requirements regarding third-party inducements becomes a vital area of the regulators' future work.
Women 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.
February 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.
The Stockholm FinTech Week 2019: Summary
Last week Kidbrooke Advisory participated in the Stockholm FinTech Week 2019! More specifically, we had a chance to attend the events within the following areas: FinTech & Market leadership collaboration, RegTech, InsurTech, Sustainability and Impact Tech and finally Regulation & Nordic Collaboration. The discussions centred on the best practices in building cooperative relationships between the emerging industry participants and the traditional financial institutions, the role of the regulations and the regulators in a changing industry, the rising awareness of climate change as well as the potential of the cutting-edge technology adopted by the market participants.