Mario Pardo
Risk Management Expert | Quantitative Finance Consultant
Mario Pardo has over 20 years of experience in risk management and quantitative finance. He was Risk Manager for a $3 billion European Family Office for two decades, focusing on portfolio construction and risk analytics. His work has been endorsed by Nobel Laureates Robert Merton and Myron Scholes.
Since 2019, he has collaborated with Columbia University’s Operations Research Department on research involving non-linear modeling, copula theory, and machine learning for multi-asset portfolio risk.
He is the founder and CEO of Ryse, Inc. (est. 2006), providing risk management consulting and solutions to pension funds, family offices, and hedge funds, where the overall sum of all assets advised by the company’s clients is north of $30 billion. Previously, he worked at Booz Allen & Hamilton advising on M&A transactions in agribusiness and consumer goods in Latin America.
Mr. Pardo holds degrees from Vanderbilt University and an MS from MIT’s Management of Technology program.
He has spoken at the Bloomberg Quant Seminar (2025), the Institutional Society of Risk Professionals (2024), and multiple GAIM hedge fund conferences globally.
Ryse, Inc
RYSE Corporate Biography
Founded in 2006, RYSE is a leading provider of investment risk solutions dedicated to serving institutional investment managers, pensions, endowments, foundations, and family offices. With experience advising portfolios ranging from $100 million to over $20 billion, RYSE has established itself as a trusted partner in managing complex investment challenges.
RYSE was created to address the growing demand for comprehensive, forward-looking risk management in an increasingly dynamic marketplace. The firm specializes in all aspects of risk oversight, from portfolio construction and stress testing to performance attribution and scenario analysis. Since 2020, the Operations Research Department at Columbia University has sent students each semester to RYSE for training in modeling non-linear assets within a portfolio management context. Their work focuses on credit risk, risk budgeting, and risk aggregation—reflecting the firm’s commitment to advancing applied knowledge in modern risk management.
At the core of RYSE’s innovation is its proprietary team of Artificial Intelligence (AI) agents. These advanced systems are designed to explain, analyze, and process large volumes of data, actively supporting portfolio risk management on a day-to-day basis. By capturing both linear and nonlinear characteristics of risk drivers, RYSE delivers robust insights into the distributions that influence the risk and performance of multi-asset class portfolios.
Through a combination of deep expertise and cutting-edge technology, RYSE empowers investors to make informed decisions, protect capital, and achieve long-term investment objectives.
Risk Transparency in Risk Governance
Risk transparency is a cornerstone of effective risk governance. While traditional views equated transparency with full position disclosure, institutional investors increasingly recognize that what is truly needed is “risk translucency”—a clear and consistent understanding of the portfolio’s underlying risk profile rather than a list of holdings.
Through the application of both linear and non-linear risk aggregation frameworks—including copula-based methodologies and Monte Carlo variance/covariance simulations—investors can obtain meaningful risk budgeting analytics. This allows stakeholders to assess exposures across credit, market, and aggregate portfolio risks with greater clarity.
Importantly, risk transparency serves three governance objectives:
Risk monitoring – ensuring managers do not exceed stated exposures, leverage, or investment guidelines.
Risk aggregation – enabling institutions to understand the combined risk implications across multiple portfolios.
Strategy alignment – confirming that managers remain consistent with stated investment styles and strategies.
Rather than exhaustive position-level data, summary risk profiles and stress-tested analytics often provide more useful and actionable insights. By presenting risk in a standardized, comparable format, stakeholders gain the necessary transparency to evaluate, monitor, and make informed portfolio-level decisions.
In this way, risk transparency is not only about disclosure—it is about delivering clarity, consistency, and accountability, ensuring that all stakeholders are equipped to exercise effective governance at the institutional level.
Mario Pardo