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.
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.
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.
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.
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.
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.
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.
This article will discuss why it is important to model credit indices and detail a number of different approaches to this problem.
In this article, we evaluate the rolling window procedure to alleviate the problem of inadequate data by increasing the number of observations extracted from a limited set of data.
In this part we evaluate the framework by performing simulations and discuss the implications of utilizing a dependence model like this.
In this article we seek to develop a model allowing for dependence between equity and credit risk.
Part I of III describing a framework for analysing dependency between equity and credit risk.
In this article we conduct a case study of the operational risk capital requirement, with the ambition of comparing it with the Solvency II Standard Formula.
In this article we investigate the performance of the LSMC approach on a stylised financial product.
We will in this article give an introduction to operational risk, and explain the subject as it is defined in Basel II.
In this article we will introduce an efficient way of estimating and calibrating regression functions in a LSMC environment.