Using returns-based investment risk analytics to shine a brighter light into the dark areas of individual funds and investment portfolios.
It’s been a wild rollercoaster ride these days for Bitcoin investors. The cryptocurrency hit an all-time high of $64k in April only to plummet nearly 50% a month later. Last year, as the entire world shut down access to mountain peaks and surfing spots, people started to look for stay-at-home ways to supply their adrenaline fix – and speculative trading fit the bill.
This document provides an introduction to MPI portfolio stress testing methodology as well as a step-by-step overview of how to conduct fund- and portfolio-level stress tests within the MPI Stylus Pro application.
In this post, our research team demonstrates how scenario analysis can highlight different risk sensitivities among same-vintage TDFs that could go undetected by traditional risk measures.
In this post, our research team shows how returns-based scenario analysis can be used to enhance traditional portfolio risk analysis by helping to assess potential fund performance through extreme market events.
In this post, our research team demonstrates a clever way to backtest forward-looking scenarios commonly used in portfolio risk analysis.
In this post, our research team uses regime-based investment risk analytics to present an approach to assessing the size and significance of investor blind spots during a typical manager screening process.
It is generally known that endowments invest in risky assets, but quantifying such risks has remained challenging due to a lack of information about returns. We set out to address this challenge and developed a new basis for estimating endowment risks.
Investors have a tendency to downplay interest rate sensitivity as a factor influencing equity products, with the assumption being that its effect must be negligible at most. One of a handful of exceptions to that assumption, however, is concern over the rate sensitivity of low volatility “smart beta” funds.