01April 2019 News Update
We are delighted to present our analysis of the top April trends within the financial industry! This month we identified the growing need for risk expertise among the asset managers striving to provide truly sustainable financial advice. Moreover, we see that a different set of factors determines the competition among the digital offerings in asset management compared to the traditional financial advisory services. Additionally, we firmly believe that it is crucial for the financial institutions to measure and prepare for the impact of the looming -IBOR transition early on, and come up with an appropriate action plan to minimise the adverse PnL effects.Read More
02End of LIBOR Event Summary
We are delighted to present a free summary of a recent event hosted by Kidbrooke Advisory in a partnership with FinCAD and Erik Vynckier dedicated to analysing the consequences of moving away from -IBOR. The discussion involved exploring differences between the -IBOR and the alternative reference rates, technical aspects of the transition, fallback contracts' intricacies and their potential impact on the trading positions, as well as the situation at the Swedish market.Read More
Financial advice is being digitalised and is increasingly provided on an automated basis. Download our summary of the latest developments within this exciting field.Download
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.
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.
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.
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.