Knowledge Base Articles
Part III - Portfolio Construction - The Real World Analysis
In the third and the final part of our “Portfolio Construction” article series, the findings of the previous sections are applied to a broader and more realistic set of assets to evaluate the performance of the proposed methods against more conventional techniques.
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 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 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: CVA Pricing Frameworks
In this article we will expand the concept of CVA by presenting different cases where the investor is seen as either risk-free or risky. We then present four different CVA pricing frameworks and discuss their level of sophistication.
Redesign and Reuse: Gauging the Non-Modellable Risks under FRTB
The set of Basel III rules finalized the development of the regulatory capital framework’s post-crisis reforms, accompanied by an industry's lobby battle. However, there is an element of the newly developed FRTB regime, which carries on keeping the industry leaders awake during the nights: the new approach to treating the non-modellable risk factors (NMRFs). This topic is gaining prominence both due to the knotty nature of these risks and because 29% of total market risk capital charges under FRTB could be attributable to NMRFs. However, we argue that despite the differences in the treatment of hard-to-model risks, the existing framework could still be put to use while addressing the new FRTB requirements.
Balancing Innovation: Use Both Hands in Establishing Robotic Advice!
In the wake of rapid technological advancements and looming regulatory challenges, large players of the British financial industry turn to innovation as a tool to preserve the margins high and keep the customers satisfied. However, the extent to which the multinational giants commit to letting their new offerings cannibalize their traditional businesses varies dramatically.
Mitigating Risk: A Joint Model for High-Yield and Investment-Grade Credit Indices
Today, there are many flawed corporate bond pricing models. However, there is also a novel credit-spread approach that can simulate index prices and accurately capture probability of default, enabling better risk management and regulatory compliance.
A parametric approach to haircut modelling
Determining collateralised derivatives haircuts is becoming an increasingly more important problem. At the same time, the method used today has been found to have significant shortcomings. To sidestep these issues industry is now looking towards a parametric modelling approach to determining haircuts.