Category: Research

Tackling the challenges of governments as financial institutions

From the MIT Sloan Newsroom:

Governments not only regulate the private financial marketplace, they also own and operate some of the world’s largest financial institutions. Government agencies make trillions of dollars of loans, insure large and complex risks, and design new financial products. Yet their leaders often lack the analytical support and rigorous financial training of their peers in the private sector, and transparency is often lacking.

 

The MIT Center for Finance and Policy officially launched this month to address those gaps, along with other challenges facing financial policy makers.

“This is a big unmet need,” said Professor Deborah Lucas, director of the center. “To have an academic center devoted to the broad swath of government financial policies that have such an enormous effect on the allocation of capital and risk in the world economy.”

“What we want to do is to promote research that policymakers, practitioners, and informed citizens can turn to as an objective source of information when they’re thinking about these policy issues. That information often isn’t available now,” she said.

Research endeavors so far include, among others: the production of a world atlas of government financial institutions, an effort helmed by Lucas to catalog, compare, and evaluate governments’ financial involvement worldwide; a project led by Professor Andrew Lo to develop a dashboard that measures systemic financial risk; a study of the effects of algorithmic and high frequency trading, led by Professor Andrei Kirilenko; and a study of policies on retirement finance led by MIT Sloan professor and Nobel laureate Robert Merton.

Lo, Kirilenko, and Merton are all co-directors of the center.

Along with the research work, Lucas said there is also an educational mission for the center. In many cases, the center’s leaders say, financial problems could have been avoided, mitigated, or at least predicted had public sector workers had an education on par with that received by many private sector finance professionals.

“The idea is to provide the people who are working on finance within a government context with the same skillset as their peers in private industry,” Lucas said. “One reason you see a lack of finance education is because it’s tended to be a rather expensive education. And people going into the public sector may not even realize that finance is what they will need to know.”

At MIT Sloan, work at the center has already led to the creation of Kirilenko’s new course—Core Values, Regulation, and Compliance—as well as a student club on financial markets and policy.

The center began sponsoring events in October 2013, but officially launched Sept. 12-13 with the inaugural MIT Center for Finance and Policy Conference in Cambridge, Mass. More than 120 people attended the invite-only event, which featured discussions on the cost of government credit support, the costs of single-family mortgage insurance, and contagion in financial markets. Peter Fisher, senior director at BlackRock Investment Institute and a former undersecretary at the U.S. Department of the Treasury, gave a keynote talk.

The conference also included a panel discussion on improving government financial institutions, which addressed the need for government agencies to improve how they manage credit portfolios and monitor program risk levels over time. Panel members also discussed ways to raise red flags when there are problems in government credit programs.

The outlook was not entirely dire. “The move toward embracing risk management concepts across the federal government has been impressive in recent years,” said Doug Criscitello, a managing director at Chicago-based audit, tax, and advisory firm Grant Thornton and the former CFO of the U.S. Department of Housing and Urban Development. “We’ve seen the rise of independent risk management offices … that are housed outside the credit extension department.”

Lucas said she believes MIT’s depth in finance, economics, policy, and systems thinking make it the ideal place to study governments as the world’s largest and most complex financial institutions.

“I think an important reason that more academics haven’t taken on these issues—despite their importance—is that they are extremely complex,” she said. “Making progress takes a big investment in understanding institutions and laws and motivations. The problems are inherently interdisciplinary. And MIT is this great institution in terms of having the horsepower and energy to go after it and say ‘We can hit this question from a lot of different dimensions.’”

What is the optimal trading frequency in financial markets?

Trading speeds in financial markets have increased dramatically over the last decade. In markets for equities, futures and foreign exchange, transactions take place in milliseconds to microseconds (or even nanoseconds). Markets for fixed-income securities like corporate bonds and over-the-counter derivatives like interest rate swaps and CDS are also catching up quickly by adopting electronic trading.

The dramatic speed-up of financial transactions can perhaps only be matched by the intensity of the events and debates surrounding it, especially in the context of high-frequency trading. To many, the Flash Crash of May 2010 was a wakeup call for reevaluating market structure. A series of technology glitches proved to be highly costly for some brokers, proprietary firms and marketplaces in terms of profits and reputation. The SEC launched investigations into HFT firms and their strategies. The French regulators introduced financial transaction tax. Michael Lewis wrote “Flash Boys.” The list goes on.

With these events and controversy come important economic questions: What are the costs and benefits to investors for speeding up trading? Is there an “optimal” trading frequency at which the financial market should operate? And does a faster market affect one group of investors more than another?

In a recent research paper, Welfare and Optimal Trading Frequency in Dynamic Double Auctions, my coauthor Prof. Songzi Du (Simon Fraser University) and I attempt to answer these questions.

Our starting point is very simple. Trading frequency can be measured by how often investors transact through market. A higher-frequency market allows investors to access the market more often per unit of clock time. When investors meet each other in the market, they trade. Trading can be motivated by new information about future asset value and idiosyncratic trading incentives such as tax or inventory considerations.

A fundamental function of financial market is to reallocate assets from investors who value them less to investors who value the assets more, at the right price. The better this function is fulfilled, the more efficient the market is in reallocating the asset. We say that the market “improve welfare”—that is, make all investors better off—if it makes the reallocation of assets more efficient.

The bright side and dark side of a higher-frequency market

A higher trading frequency is double-edged sword. The optimal trading frequency depends on how the benefit and cost balance each other.

On the bright side, a higher-frequency market is more responsive to new information. Investors benefit from being able to react immediately to news. For example, following earnings announcements or merger-acquisition news, an investor may find his previous allocation on a stock no longer desirable. The sooner investors react to this information by trading, the better off they are. This effect favors a high-frequency market.

On the dark side, a higher-frequency market reduces the aggressiveness of investors’ trades. Investors are said to be more aggressive if they are willing to tolerate a greater market impact to achieve their target asset position. For example, aggressive execution means trading larger quantities more quickly. By contrast, unaggressive execution means splitting a large order into many small pieces and trading them gradually over time. The more frequently the market allows investors to transact, the stronger is their incentive to split orders over time to avoid price impact; hence, it takes longer to reach desired asset positions, and this is inefficient. If, however, a market opens infrequently, it encourages investors to trade aggressively now—failing to trade now means waiting for longer for the next opportunity to trade; this in turn leads to a faster convergence to efficient allocations. In this sense, somewhat counter intuitively, a lower-frequency market enhances allocation efficiency.

Scheduled versus stochastic news

We show that the optimal trading frequency depends on the nature of information arrivals, which determines the tradeoff between the benefit and cost of a higher trading frequency.

For scheduled arrivals of information, such as earnings announcements and macroeconomic data releases, we find that the optimal trading frequency should be equal to or lower than the frequency of information arrivals. For example, if news only arrives once per day, it is never optimal for investors to trade more than once a day. Moreover, if the market is competitive enough, the optimal trading frequency is equal to the information frequency.

For stochastic arrivals of information, such as surprise news of mergers and acquisitions, we show that the optimal trading frequency can be much higher. Moreover, if the market is competitive enough, continuous trading (the highest-frequency market) turns out to be optimal.

What does this tell us about optimal trading frequency in reality? Assets such as large-cap stocks or Treasuries that have frequent, unpredictable news shocks should be traded close to continuously. Small, illiquid stocks or bonds that have scarce news may best trade in a low-frequency market that only opens a few times a day; concentrating trading interests at specific time creates a deeper market. Therefore, there is no “one size fits all” optimal trading frequency for all securities.

For more details of this research, see “Welfare and Optimal Trading Frequency in Dynamic Double Auctions”, by Songzi Du and Haoxiang Zhu, http://ssrn.com/abstract=2040609.

Haoxiang Zhu is an Assistant Professor of Finance at MIT Sloan School of Management.

Professor Zhu’s research on this topic has also been featured on MarketWatch, click to view the article.

Designing More Efficient CDS Auctions

Professor Haoxiang Zhu

Credit default swaps (CDS) are an important derivative class. According to the Bank for International Settlements, as of June 2013, CDS contracts have $24 trillion notional amount outstanding and $725 billion market value globally.

A CDS contract is a default insurance contract written on a firm, loan or sovereign country. Buyers of protection (CDS buyers) pay periodic premiums on a notional amount of debt to sellers of protection (CDS sellers), until the contract expires or default occurs, whichever is earlier. For example, if a bond investor wishes to insure against the default of $100 million corporate bonds, and a five-year CDS contract on that bond is quoted at 500 basis points (5%) per year, then the CDS buyer pays $5 million per year to the CDS seller for five years. If the bond defaults within five years, the CDS seller pays the CDS buyer the loss given default. The question is: What are the recovery value and default compensation?

The market uses CDS auctions to determine the recovery value of a defaulted bond. For example, the auction-determined recovery rate of Greece debt is 21.5 cents per euro. So, CDS sellers pay CDS buyers 78.5 (=100-21.5) cents per euro of debt insured. In addition to settling CDS contracts, CDS auctions also give investors an opportunity to trade defaulted bonds at zero bid-ask spread. Given the sheer size of CDS markets and high-profile defaults in the recent recession, it is important that CDS auctions deliver unbiased prices and efficient allocations. But do they?

In a recent research paper with Professor Songzi Du of Simon Fraser University, we find that the current design of CDS auctions leads to systematically biased prices and inefficient allocations.

To understand why the auction design is biased and inefficient, we need to understand the auction procedure itself. A CDS auction consists of two stages. In the first stage, investors who have CDS positions submit market orders (called “physical requests”) to buy or sell the defaulted bonds. An investor’s market order on the bond must be in the opposite direction of his CDS position, and no larger in magnitude. The sum of these market orders, called “open interest,” is sold in the second stage, which is a uniform-price auction. Importantly, only one-sided limit orders are allowed in the second stage. If the open interest is to buy, only limit sell orders are allowed; if the open interest is to sell, only limit buy orders are allowed. The market-clearing price in the second stage is the “official” recovery rate of the defaulted bond for settling CDS.

From 2006 to November 2013, this auction procedure has settled more than 140 defaults, including those of Lehman Brothers, Fannie Mae, General Motors, and Greece, among others.

Through a formal auction model, we show that the biased design comes from the restrictions imposed on the two stages of CDS auctions. To get the intuition, consider a CDS buyer. A CDS buyer naturally wishes to sell the defaulted bonds to minimize the uncertainty in the auction final price; he can eliminate this price risk by selling an amount equal to his CDS position. If this CDS buyer also has a low value for owning the bonds (for information or hedging motives), he would want to sell more. But the auction procedure forbids him from selling these additional quantities. A supply to sell is therefore suppressed in the first stage. In the second stage, this supply is suppressed again if the open interest is to sell (as only buy limit orders are allowed). Information from a suppressed demand cannot come into the price. Therefore, price is systematically biased, and allocations of bonds are inefficient.

This is not the end of the story. In an earlier version of the same research paper, we show that, if CDS traders are large, they also have a strong incentive to manipulate the final price auction price—to get favorable settlement payments on their CDS positions. Manipulation also leads to price biases.

The administrators of CDS auctions are aware of the biased prices; as a remedy, they impose a price cap or a price floor, depending on dealers’ quotes and the direction of the open interests. But this measure is imperfect and can backfire. We find that although a price cap or floor can correct price biases, it can also make bond allocations even less efficient.

What is a better solution then? We show that a simple, unconstrained double auction delivers better price discovery and allocative efficiency. A double auction for settling CDS is similar to the open and close auctions on stock exchanges. Since double auctions have done well in equity markets, why not consider it for CDS auctions?

For details of this research, see Songzi Du and Haoxiang Zhu, “Are CDS Auctions Biased and Inefficient?”. An earlier version of this paper is summarized by FT Alphaville (part 1, part 2).

Haoxiang  Zhu is Assistant Professor of Finance at the MIT Sloan School of Management