Knowledge Base Articles
Part II - Portfolio Construction - Sampling & Optimisation
The second part of the “Portfolio Construction”-series explores whether introducing parameter uncertainty to the model would improve the out-of-sample performance of the optimal portfolio. Additionally, the article proposes and tests two adjustments to regular utility optimisation.
Part I - Portfolio Construction - Parameter & Model Uncertainty
There is a number of challenges associated with portfolio construction based on historical data. This three-part article series explores some of the most common issues attributed to the model-based portfolio optimization: the sensitivity to changes in data, large variations in portfolio weights and the bad out-of-sample performance.
Hierarchical Clustering: Prediction of Systematic Underperformance
As machine learning methods grow in use and popularity, we explore yet another dimension of wealth management that our experts consider fit for applying such frameworks. In this article, we deploy hierarchical clustering to find more consistent ways of predicting the relative future performance of funds.
Beyond Modern Portfolio Theory: Expected Utility Optimisation
The modern wealth management industry still relies on the 50-year-old approaches to portfolio management, widely popularized by Markowitz's Modern Portfolio Theory (1952). Despite heavy criticism within the academic circles, the alternative methods remain undeservingly overlooked in practice. In the context of the modern leap for hyper-customization, we look into one of the alternatives to Modern Portfolio Theory in greater detail - the Utility-based approach.
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.
Leaning Into the Curve of the Pandemic
As well as COVID, the world must now contend with renewed tensions between the world’s two superpowers and the aftermath of the horrific murder of George Floyd. Conditions are febrile, but it is often in moments of maximum uncertainty and duress that lasting, positive change is made. Policymakers are forced to act in the national interest and interventions such as the furlough and the various COVID loan schemes in the U.K. deserve recognition. Nonetheless, few would have predicted a few months ago that 2020 would prove to be such a seismic event, notwithstanding that it’s US election year. Decisions made now will reverberate for years, if not decades to come. We will stay in our lane here and look at how FinTech can support better outcomes as we come out the other side.
Retirement Planning Post COVID-19
Several months into the COVID-19 outbreak, it would be foolish not to expect significant and lasting change to both our economies and our societies in general. What does this all mean for our retirement plans? For those of us who’s jobs and livelihoods are directly imperilled by the crisis, a brass-tacks evaluation of the likely path for our specific roles and industries will, of course, be imperative. For example, those working in the service sector and in the cultural economy, existential concerns are likely to be paramount and pension planning is likely to be a second-order priority. For those of us fortunate enough to come out of the other side (relatively) unscathed we should directly assess the impact of COVID on our retirement plans – such will be its direct impact. As individuals, we frame our retirement by our age and where we consider ourselves to be in our life journey at that moment in time. We need to generalise here so let’s consider the three broadly accepted stages in turn; accumulation, at-retirement and decumulation.
The Role of Economic Scenario Generators in the Age of Covid-19
Economic Scenario Generators (ESGs) are fundamental to the analysis of ALM problems. Oversimplifying, they are software tools that facilitate simulated analysis of economic variables and risk factors. 6 months ago, no one in the West could have predicted what we are now experiencing. Nonetheless, we are truly now in un-navigated economic territory globally. Stress-testing and scenario analysis comes in a variety of formats and styles. Many are formulated by benchmarking variability on previous events and crises. None of these would have offered any forewarning of the impending magnitude of Covid-19. Specific predictions vary and are challenging to make, but we can be confident in seeing a record single quarter decline in global GDP. ESGs are not crystal balls and would not, ceteris paribus, have provided any direct mitigation to these challenges. However, as we prepare to make our first tentative steps into the ‘new normal’ we must surely re-evaluate the role that enhanced analytics can provide for asset allocators.
Those of us working in financial services are tasked with trying to quantify the impact the pandemic is having on the global economy. If for the purpose of this analysis alone, we selectively classify the outbreak as a financial crisis, we see a familiar pattern of behaviour: A flight to safety away from risky assets has certainly been evident in the past 6 or so weeks. Bonds, the dollar and (to some degree) gold have all benefited from the market volatility. The global financial crisis of 2008-9 is a relatively recent reminder of the last time we witnessed similar moves in asset prices. Therefore, it is absolutely reasonable to look for a correlation between that crisis and where we might head in the coming months and years.