Knowledge Base Articles
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.
P&L Attribution: Similarities and Differences between FRTB and Solvency II
In this article, we discuss the challenges of implementing the internal model approach under FRTB and Solvency II. In particular, we focus on the P&L Attribution test, which financial institutions have to continuously perform and pass to maintain their eligibility for internal model use. The article outlines the similarities and differences between the two regulatory regimes that require the P&L Attribution test; FRTB for banks and Solvency II for insurance companies.
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.
We present some technical concepts regarding different valuation structures both from a financial point of view and a mathematical perspective.
Libor Market Model
In this note, we explain Libor Market Model for interest rate. Furthermore, we go through the calibration of LMM conforming to Solvency II.