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Antiquated VaR takes backseat to analytics  that deliver superior insights into true portfolio risk

By Mohit Arora

For most traders and advisory firms, value at risk (VaR), is the default tool for calculating risk within a portfolio. Although VaR originated in banking and today is used widely in finance, energy and elsewhere, it is far too simplistic to be an adequate predictor of true market risk. 

Yet, it’s no secret VaR has a number of faults, with many traders using their own workarounds. It’s also no secret these shortcomings contribute to large scale financial failures. 

Risk managers, however, continue to rely on VaR simply because it is the status quo, inertia, its ease of use and the fact that a lot of people learn it in business school.

Less understood are the ways in which VaR is vulnerable to significant mispricing of risk, a common cause of failure in commodity-based deals. In fact, along with poor financial hedging (the term hedging used loosely in many cases), mispricing the risk of assets and acquisitions is a primary reason for commodity intensive firms to fall into financial distress, often spectacularly.

For VaR to deliver accurate results consistently in favor of client objectives, traders must have a sophisticated understanding of market-based deal risk in order to price commodities correctly. 

Limited to a daily basis, VaR gives a reasonable measure of risk. The problem, however, is VaR forecasts centennial events as occurring every 100 years. In today’s reality, events on scale with what used to be seen as once in a generation or once a century are happening at much greater frequency, sometimes occurring as often as two and three times a year. This singular miscalculation of VaR is a major contributor to the dramatic and totally unpredicted losses most companies experience. 

VaR also doesn’t account for unique company or business objectives and the bottom line impact of portfolio risk.

A better approach for risk methodology would be to capture a picture of risk over time instead of limiting the view to maximum potential daily losses. The methodology must also show optimal option structures with a clear understanding of tail risk. This applies regardless of industry or type of portfolio. 

The shortcomings of VaR inspired us to create our own proprietary risk measurement methodology, M(β)risk™(or M-Risk). Our methodology is not bound to normalities assumed in VaR when generating thousands of possible price scenarios. 

M-Risk, instead, provides a holistic view of risk for efficient and effective risk management decisions based on a company’s true portfolio and associated risk. Our visualization, through our RiskNetTM platform presents a thorough understanding of company objectives that include free cash flow, EBITDA, etc. Mobius’ bespoke approach is guided from a deeper discussion of company goals to guide risk management strategies that maximize revenue and minimize market risk. M-Risk analyzes portfolio risks and generates a picture of component risks, as well as the concentration of risk by asset class, location (or basis) and tenor.  

M-Risk methodology and related software deliver unprecedented insight into true portfolio risk, positioning clients for informed decision-making about risk management and more effective hedging, as well as consistent profitability. 

We start by computing M-Risk and determining primary sources of risk. Market fundamentals are incorporated, along with liquidity, market pricing of the curve (including volatility and skew). Client objectives and capital/counterparty constraints also play roles in hedge structure and execution. 

Outcomes associated with an M-Risk approach each client’s unique objectives in a cost-effective manner. 

Pain and disruptions within oil and gas industries caused by the pandemic in 2020 present a case study on the efficacy of M-Risk as a superior option to VaR. Our methodology enabled clients to hold tight during the turbulence, positioning many to be positively positioned for capitalizing on growth opportunities. 

For instance, a deal where a financial sponsor wanted a client to hedge 80 percent of its volume (and risk) in an acquisition for five years ended up being a teachable moment for both the sponsor and the client. 

We used M-Risk to give them the visibility to understand their predominant risk was oil, while their largest hedging costs would be from deferred gas basis and NGLs, enabling them to make efficient and effective hedge decisions — something they wouldn’t have been able to see based solely on VaR. We proposed constructing a hedge that achieved at least 80 percent risk reduction (as desired), at least 80 percent revenue hedged while minimizing spending to achieve it.

This structure reduced risk to the level required by the sponsor and ultimately resulted in a savings on execution costs in the neighborhood of $10 to $20 million. 

If this client had been forced to hedge in the prescribed way, costs likely would have been at least two times higher. This hedge strategy also meant allocating a larger percentage of trades into crude (since this was the intersection of the highest risk and highest liquidity), which became a very fortunate position by March of 2020.  

Had this client followed a VaR-driven strategy, they would have lost at least 20 percent of the value of their asset in year one of the acquisition, forcing them to play defense. As it happened, they were comfortably hedged and went on the offense, picking up value-priced assets.

Learn more

Today, the M-Risk methodology is visualized in Mobius’ proprietary commodity trading risk management platform, RiskNetTM. Our app, M-Risk Visualization, is available as a standalone or within our CTRM platform, RiskNet. We applied our deep experience to build them in-house and to match the needs of companies managing multi-asset portfolios.

Mobius delivers a risk management strategy guiding clients in knowledge-based risk management decisions that achieve client objectives for their capital plans. 

Author Profile
Mohit Arora
Mohit Arora
Vice President, Risk Management and Decision Sciences

Mohit has more than 20 years of quantitative analysis, trading, structuring, and risk management experience across the energy spectrum (crude, products, natural gas and NGL’s). Prior to joining Mobius, he was designing option strategies and trading crude and product options at Koch Supply & Trading. He also constructed portfolio risk mitigation option structures for the crude, products/fuel and NGL desks. Before Koch, he traded crude and products at AAA Capital Management, with a focus on options. He moved to AAA from Entergy Koch/Merrill Lynch Commodities where he developed and traded natural gas option strategies, proposed trades as well as enhanced return/index strategies to hedge funds and was the primary hedge structurer for corporate clients (producers, consumers and refiners). Mohit received his BTech from the Indian Institute of Technology Kanpur and a dual PhD (Aerospace Engineering and Scientific Computing) from the University of Michigan, Ann Arbor.