Monitoring Risk with V-LAB

With V-Lab, Rob Engle and his colleagues have created a powerful econometric tool for measuring asset price volatility and forecasting market risk. And by posting it online with compelling visualizations of real-time data, the Ta-Chung Liu Distinguished Visitor has achieved something even more remarkable:  he's made examining volatility accessible, approachable, and even fun–no advanced understanding of econometrics required.

V-Lab is a working model of market volatility that, infused with fresh data daily, provides real-time measurement, modeling, and forecasting of the amplitude of change that asset prices experience over time.  Engle explained the project and a few of the conversations that its data has provoked in a Becker Brown Bag talk to MBA students on April 18, 2014.

By posting his working model online for use and critique and subjecting it to market data constantly, he has challenged himself and colleagues to build models that hold up to out-of-sample data over time. V-Lab has also proven to be an invaluable tool for risk managers and regulators looking to improve volatility forecasts in the marketplace.

Because it’s available to the public, Engle says that V-Lab functions as a great jumping off point for public discussion of how today's news affects tomorrow's market activity and, in turn, the broader economy. “It’s a fun way to talk about current events, and see what’s going on in the economy and in financial markets.”

Take what's happening in the Crimean peninsula of Ukraine. Examining V-Lab’s volatility measures over the past six months, the market impact of Russian action in Ukraine coincides with an enormous spike in volatility in the Russian stock market at the beginning of March 2014. But volatility quickly decreases even in the face of rising tensions, economic sanctions, and other political fallout.

Why? Engle thinks that the market may have anticipated a series of similar episodes to play out along the Russian border following the Ukrainian incursion; the Crimean occupation and subsequent annexation seemed muted in comparison to their expectations. "Investors moved the stock price and volatility in a way that reflects that this might not be the worst thing that happens," said Engle. Seeing that the Crimean incursion wasn’t the first of many similar military actions on the part of the Russian government, the market quickly stabilized. That insight has implications for helping us understand how Russians feel about Ukraine and Crimea, and how they might react as events unfold.  

Predicting the future is a tall order, but one that Engle had to face frequently after a trip to Stockholm in 2003. “When I won the Nobel prize, people started asking me hard questions. ‘If your model is so good, could it predict the financial crises?’” That degree of economic clairvoyance is tricky; it requires models of volatility and systemic risk that accommodate data that didn’t exist and wasn’t anticipated when the model was developed.

V-Lab became a tool for using real-time data to continually refine those models. Working with then-student Bryan Kelly (now a Chicago Booth assistant professor) and others, Engle has leveraged V-Lab since 2009 to continually refine his models, getting better and better at predicting financial calamity. So how close can he get today? “One day ahead, with good reliability," said Engle knowingly. Enough warning to mitigate disaster, but certainly not enough to avoid it completely. "A reporter rightly pointed out that we need to be able to predict things further out than that.”  

Being able to predict short-term risk serves as a valuable tool in monitoring our economic stability, but Engle cautioned against focusing too heavily on it just because we can measure these risks more easily. He also warned of what he called 'risk myopia,' confusing long-term risks with short-term risks.

During the financial crisis, investors often took on risky assets as a quick route to hefty returns; the ability to predict near-term risk made it seem safe since risky assets could be sold in anticipation of increased risk. But mortgage debt turned out to be more of a long-term risk, difficult to liquidate in a pinch.  "We’re trying to provide tools that the risk management industry can use to better forecast long run risk,” Engle noted.

Perhaps more important than predicting disaster, Engle discussed how those long run risk forecasts can be used to craft better regulatory frameworks for averting future crises altogether. SRISK, Engle’s measure of systemic risk using firm-level market data, establishes how much capital a financial institution would need to raise in order to function normally in the event of another financial crisis that would limit their access to credit.

Engle and his team calculate SRISK for 1,200 institutions around the world on a weekly basis; it allows us–and regulators–to ask a lot of what-if questions about what segments of the world economy would be most at risk in the face of another financial meltdown. “What’s the total capital that the world would need to provide to keep all financial institutions running normally in the face of a financial crisis?" asked Engle, "Just under $3 trillion." China holds the lion’s share of that risk at $550 billion, though Switzerland appears most at risk in proportion to their size, requiring 13 percent of their total GDP to remain solvent in a crisis.

Engle says V-Lab can examine SRISK down to the firm and sector level, leveraging the depth of publicly accessible trading data The Federal Reserve achieves the same thing, but the proprietary information and meticulously crafted reports cannot so easily be remixed into highly-readable visualizations. By that measure, the project illustrates the potential of expansive, open data sets made visual and digestible. "That’s the advantage of a market-based measure; you get to see what happens in real time."

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