Category: Uncategorized
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Announcing KidbrookeONE: Revolutionising Investment and Wealth Analytics
Since our start in 2011, Kidbrooke has been at the forefront of financial technology innovation within the wealth analytics space. As the financial industry evolves, so do we. We’re excited to announce the transition from OutRank, the financial simulation engine, to KidbrookeONE, our Unified Investment and Wealth Analytics platform.
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OutRank User Guide: The Walkthrough of the Digital Investments Use Case
In today’s video, we will go through our most popular use case, “Short-to-medium term investments” and the APIs that comprise of its core elements
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Steps to heaven – How to take your customers on a journey to the land of digital trust
The financial guidance and advice services, which constitute the life insurer’s core business, were among the first to go through the transformation. Joakim Pettersson, the digital strategy and innovation lead at Skandia, believes that digitalisation is “the only way to scale financial advisory services”.
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Part II – Artificial Neural Networks as a Substitute to LSMC and Nested Simulations
The first article in this series contained a high-level introduction to ANNs and this second article builds on that to describe how such networks can be used as a substitute to the more established methods when exposed to a problem that requires nested simulations. We specifically address what to consider…
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Part I – Introduction to Artificial Neural Networks
The approach we present is based on Artificial Neural Networks (ANNs) which previously have been successfully applied in many contexts such as Image recognition and Natural Language Processing. As we shall see later in the series, this fairly modern technique has the potential to improve the accuracy of more traditional…
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Part III – Portfolio Construction – The Real World Analysis
The optimization methods proposed in the previous articles are compared to two established automated portfolio optimization methods; Markovitz’s Mean-Variance optimization and the Hierarchical Risk Parity method. In addition, a clustering method is proposed in order to solve the numerical issues associated with optimization of a large sample of assets.
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Beyond Modern Portfolio Theory: Expected Utility Optimisation
This article focuses on the comparison of the strengths and weaknesses of the Modern Portfolio Theory and the utility-based approach, along with their application to portfolio construction.
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Part II – Portfolio Construction – Sampling & Optimisation
The main findings of the first part of the “Portfolio Construction”- series suggest that the parameter uncertainty has a significant impact on the optimal portfolio allocations. Therefore, a Bayesian sampling method is proposed to introduce the parameter uncertainty to the model. This section presents and compares the results from the…
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Part I – Portfolio Construction – Parameter & Model Uncertainty
The first article of the series evaluates and distinguishes the components of asset modelling process responsible for the poor performance of the optimal portfolios. This process examines whether the most significant challenge of the modelling process is arising due to the calibration uncertainty, the incorrect model structure, or the simple…
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Hierarchical Clustering: Prediction of Systematic Underperformance
Hierarchical clustering is an unsupervised learning algorithm that uses the distances between data points and assigns them into clusters with similar traits. This study applies hierarchical clustering to funds in order to group them if they demonstrate similar statistical behaviour in their returns. By testing the hypothesis that there are…
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Part I: An Introduction to Self-Normalizing Neural Networks
This article defines the concepts used for the comparative case study of liquidity risk measurement for the Swedish bond market, presented in the second piece of a given series. The concepts of neural networks, back-propagation and numerical optimisation algorithms are outlined. This is followed by an introduction to SNN, which…
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Part III: Asset and Liability Management Using LSMC – Allocation Optimisation
In the previous articles of the series, an asset and liability management (ALM) framework using least-squares Monte Carlo (LSMC) was outlined. Moreover, the accuracy and performance of the LSMC method were cross-validated against a benchmark provided by a full nested Monte Carlo (MC) simulation. In this third and concluding part…
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Part II: Asset and Liability Management Using LSMC – Accuracy and Performance
In the previous article, ”Part I: Asset and Liability Management using LSMC – Introduction to the Framework”, we presented the asset and liability management (ALM) framework based on the replicating portfolio approach. Additionally, that article briefly introduced the least-squares Monte Carlo (LSMC) method as a time efficient alternative to the…
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Part I: Asset and Liability Management Using LSMC – Introduction to the Framework
New regulations and stronger competition have increased the demand for advanced asset and liability management (ALM) models within insurance industry. An efficient ALM strategy can considerably impact the solvency of an insurance undertaking, which in turn affects capital requirements. Furthermore, ALM is often used as a strategic decision-making tools enabling…
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Machine Learning: A Regulatory Concern?
With the rapid adoption of artificial intelligence in the financial sector, banks are looking towards machine learning to stay regulatory compliant.
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Introduction to Credit Index Modelling
Credit risk is ubiquitous for most financial institutions. The inability of modelling credit risk efficiently has been recognised as one of the major contributing factors to the development of the 2008 financial crisis followed by the European debt crisis in 2010. Realistic models for credit risk is not only in high demand for…
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Overcoming the Insufficiency of Historical Data; The Rolling Window Method
In the process of mitigating risks and preventing the possibility of future crises, the ability to accurately estimate future risk and understand the amount of uncertainty an estimate carries is essential. However, risk measurements are heavily reliable on data quality and quantity, and unfortunately insufficient data is a prominent and…
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The Past, Present and Future of Central Bank Balance Sheets
In a recent post on the Bank Underground −− a blog where Bank of England staff can share their views −− James Barker, David Bholat and Ryland Thomas write about the past, present and future of central bank balance sheets. They comment on the quirks of the financial accounts of central banks and how…
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ESMA has Published a Consultation Paper on the Money Market Funds Regulation, Part II
As presented in a recent post, the European Securities and Markets Authority (ESMA) has published a Consultation Paper on the Money Market Funds Regulation (MMFR) and represents a first stage in the development of technical advice of the MMF framework. In the previous post we covered the key regulatory requirements in regard…
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ESMA has Published a Consultation Paper on the Money Market Funds Regulation, Part I
The European Securities and Markets Authority (ESMA) has published a Consultation Paper on the Money Market Funds Regulation (MMFR), which according to ESMA primarily will be of interest to MMF managers, alternative investment funds and UCITS managers and institutional and retail investors investing in a MMF. The paper represents a…
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The Volatility Components and Their Effect on the Macroeconomy
Cyclicality is a well established behaviour of volatility and has been widely used in its modelling. In particular, it is well documented that market volatility can be characterised by a two-factor process, one with a slowly varying long run component called the core volatility, and another strongly mean-reverting short run…
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KIIDs SRRI and the Swedish Mutual Funds Market
In its aim to standardise the provision of data from fund groups to permit easier comparison by advisers and investors, ESMA has introduced the Key Investor Information Document (KIID). One of the key components of the KIID is the Synthetic Risk and Reward Indicator (SRRI for short) which is used in the…
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FinTech and the Regulatory Road Ahead
In a recent speech by the Managing Director of the Monetary Authority of Singapore (MAS), Mr Ravi Menon discusses, among other topics, his view on FinTech and the regulatory future the industry faces. Below we summarize some of the key points Mr Menon presented. In pace with the rapid growth…
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An Introduction to Stochastic Volatility Jump Models
Stochastic Volatility Jump Diffusion (SVJD) is a type of model commonly used for equity returns that includes both stochastic volatility and jumps. The advantage of the model is that it is possible to replicate stylized facts such as heavy tails and volatility clustering and mean reversion, negative correlation between returns…
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11 Important Properties of Asset Returns
When specifying your next risk model for asset returns there are a number of properties, or stylised facts, to keep in mind. A stylized fact is an empirical finding that is believed to hold for a diverse collection of instruments, markets and time periods. A number of authors have studied stylised facts…
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