Working Papers
Several risk-related topics that merit deeper exploration are addressed in dedicated working papers presented in the following section.
From Gamma to Convexity: Unified Insights into NonLinear Risk Management Across Asset Classes
Why Leveraged ETFs Lag in Performance?
This paper delves into the complexities of leveraged Exchange-Traded Funds and their performance over extended investment periods. While these financial instruments can boost returns for adept investors in the short term, they often falter in volatile or stagnant markets due to issues like volatility drag, the need for constant portfolio rebalancing, path dependency, and asymmetric returns.
Evaluate Causation between Variables with Limited Historical Data
This document proposes avenues to identify causation patterns for variables with limited historical data, for instance for assets in Private Equity or Private Debt. This paper specifically focuses on causation, and not correlation, as correlation doesn't necessarily imply causation, and that the correlation coefficient should be computed on large datasets to be relevant.
Corporate Valuation Methods: Advantages and Disadvantages
The document proposes to review to key advantages and disadvantages of the most standard valuation methods used in Corporate Finance. This paper will cover the asset based (Cost to Build, Replacement Cost), the market based (Comparable Companies, Precedent Transactions), and the income based valuation techniques (Capitalisation of Earnings, Discounted Cash-Flows).
Estimating Profitability of Future Investments: Highlights and Challenges
Capital budgeting is a decision-making process where an organisation assesses if a project is expected to be profitable and is worth to be funded. Many techniques are frequently used in Capital Budgeting, including IRR, MIRR, PPM, NPV, AARR, PI and EAC; this paper is dedicated to briefly get through those techniques while highlighting the pros and cons of each method.
Maximising Wealth in Unpredictable and Irrational Financial Markets
Behavioural Finance - analysing the unpredictable and irrational behaviour of investors - has become an increasingly popular discipline in the modern financial literature. This science weakened the foundations of rationale investing and shed the light on maximising wealth under irrational markets. The purpose of this paper is going through a few common-sense rules helping investors to maximise portfolio returns in irrational markets.
Risk Management in Mutual Funds
This model highlights they key Risk Management techniques to implement per Investment Strategy. This includes Debt Funds, Money Market Funds, Private Equity, Hybrid Funds, and Equity Funds.
Leverage and Exposures under the AIFM Directive
The AIFM Directive requires AIFM to compute the Leverage of the AIFs they manage under the Gross and the Commitment method. The methodologies to compute the Leverage under both approaches have been discussed widely; within this paper we do want to focus on the formulas to compute the exposures at the product level without considering the netting and hedging arrangements.
Modelling a Custom Index: A Selection of Technical and Strategic Considerations
Despite a very large and diversified universe, it may happen that you don't find the index that meets all your expectations and requirements, constraining you to model a custom one. Within this paper, one highlights a few key technical and strategic aspects to consider when modelling such an index, including managing unbalanced panel data and aligning units and scales.
Picking the Right Risk-Adjusted Performance Metric
Investors often rely on Risk-adjusted performance measures such as the Sharpe ratio to choose appropriate investments and understand past performances. The purpose of this paper is getting through a selection of indicators (i.e. Calmar ratio, Sortino ratio, Omega ratio, etc.) while stressing out the main weaknesses and strengths of those measures.
Understanding and Defining Liquidity Risk
It is fundamental for financial institutions to monitor Liquidity Risk in an effective way, particularly in a period of volatile markets. Despite that many quantitative methods flourish in the modern finance literature, one can note that (i) it is challenging to unambiguously define what Liquidity Risk is, and (ii) it can be cumbersome to apply modern monitoring techniques, notably because of the data sample available and the assumptions made [e.g. correlations, distribution of returns, etc.].
Extracting Liquidity Risk from Cross-Sectional Returns
Assessing Asset Liquidity Risk is a major source of concern for Financial Institutions; despite that many methods flourish in the modern literature, this paper focus on evaluating the robustness of a single method built on cross-sectional returns. Could comparing security returns to benchmark returns allow segregating a change in the liquidity condition of a security? This is the core problem question that will be investigated within this working paper.
Arithmetic Returns vs. Logarithmic Returns
At the beginning of a modelling process where parameters are derived from historical time-series, one of the first arising questions can be: what type of returns should I consider? Do I have to compute the arithmetic ones or the log ones? In Quantitative Finance, log returns are widely used; main reasons are listed in this working paper.