How VaR Modeling Gone Wrong Cut Pay of Wall Street’s Most Powerful CEO, JPMorgan Chase’s Jamie Dimon, by Half

Posted on January 17, 2013. Filed under: Financial Crisis, Fixed Income, Operations, Securities, Strategies | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

JPMorgan Chase’s Jamie Dimon (Eric Piermont / AFP/Getty)

JPMorgan Chase’s Jamie Dimon (Eric Piermont / AFP/Getty)

President Barack Obama’s favorite Wall Street CEO, JPMorgan Chase’s Jamie Dimon is facing more than having his pay cut in half to $11.5 million for 2012, compared with $23 million a year earlier. He is facing the prospect of seeing his reputation, cemented until now by his smart handling of the bank during the financial crisis, becoming tarnished as he has been deemed ultimate responsible for the banks’ loss of more than $6.2 billion in the first nine months of 2012 on bets by U.K. trader Bruno Iksil, nicknamed the London Whale, who operated under Chief Investment Officer Ina Drew.

The Chief Investment Office (CIO) was supposed to manage excess cash while minimizing risk using credit derivatives as part of a hedging strategy; instead, their trades became so large that the bank couldn’t easily unwind them. At the bottom of this miscalculation were blunders in the development, testing and approval of a new VaR model to measure the risk of their Synthetic Credit Portfolio.

VaR (Value at Risk) is a metric that attempts to estimate the risk of loss on a portfolio of assets. A portfolio’s VaR represents an estimate of the maximum expected mark-to-market loss over a specified time period, generally one day, at a stated confidence level, assuming historical market conditions. Through January 2012, according to the 129-page report from a task force led by Michael Cavanagh, co-head of the firm’s corporate and investment bank, the VaR for the Synthetic Credit Portfolio was calculated using a “linear sensitivity model,” also known within the Firm as the “Basel I model,” because it was used for purposes of Basel I capital calculations and for external reporting purposes. The Basel I model captured the major risk facing the Synthetic Credit Portfolio at the time, which was the potential for loss attributable to movements in credit spreads.

However, the model was limited in the manner in which it estimated correlation risk: that is, the risk that defaults of the components within the index would correlate. As the value of the tranche positions in the Synthetic Credit Portfolio increased, this limitation became more significant, as the value of these positions was driven in large part by the extent to which the positions in the index were correlated to each other. The main risk with the tranche positions was that regardless of credit risk in general, defaults might be more or less correlated.

This limitation meant that the Basel I model likely would not comply with the requirements of Basel II.5, which originally had been expected to be formally adopted in the United States at the end of 2011. One of the traders responsible for the Synthetic Credit Portfolio therefore instructed an expert in quantitative finance within the Quantitative Research team for CIO to develop a new VaR model for the Synthetic Credit Portfolio that would comply with the requirements of Basel II.5. They believed that the Basel I model was too conservative, that it was producing a higher VaR than was appropriate.

Early in the development process, CIO considered and rejected a proposal to adopt the VaR model used by the Investment Bank’s credit hybrids business for the Synthetic Credit Portfolio. Because the Investment Bank traded many customized and illiquid CDSs, its VaR model mapped individual instruments to a combination of indices and single name proxies, which CIO Market Risk viewed as less accurate for CIO’s purposes than mapping to the index as a whole. He believed that, because the Synthetic Credit Portfolio, unlike the Investment Bank, traded indices and index tranches, the Investment Bank’s approach was not appropriate for CIO. The Model Review Group agreed and, in an early draft of its approval of the model, described CIO’s model as “superior” to that used by the Investment Bank.

The Model Review Group, charged with the formal approval of the model, performed only limited back-testing, comparing the VaR under the new model computed using historical data to the daily profit-and-loss over a subset of trading days during a two-month period, not even close to a typically required period of 264 previous trading days, a year. In addition, they were pressured by the CIO to accelerate its review, overlooking operational flaws apparent during the approval process; for instance, it was found later that the model operated through a series of Excel spreadsheets, which had to be completed manually, by a process of copying and pasting data from one spreadsheet to another. The Model Review Group discovered that, for purposes of a pricing step used in the VaR calculation, CIO was using something called the “West End” analytic suite rather than Numerix, an approved vendor model. CIO assured the Model Review Group that both valuations were in “good agreement.”

Sandy Weill, left, and Jamie Dimon, then with American Express, at a conference in California in 1983.   (Photo: Roger Ressmeyer/Corbis)

Sandy Weill, left, and Jamie Dimon, then with American Express, at a conference in California in 1983. (Photo: Roger Ressmeyer/Corbis)

On January 30, the Model Review Group finally authorized CIO Market Risk to use the new VaR model which would utilize the Gaussian Copula model, a commonly accepted model used to map the approximate correlation between two variables, to calculate hazard rates and correlations. A hazard rate is the probability of failure per unit of time of items in operation, sometimes estimated as a ratio of the number of failures to the accumulated operating time for the items. For purposes of the model, the hazard rate estimated the probability of default for a unit of time for each of the underlying names in the portfolio.

Once in operation, a spreadsheet error caused the VaR for April 10 to fail to reflect the day’s $400 million loss in the Synthetic Credit Portfolio. This error was noticed, first by personnel in the Investment Bank, and by the modeler and CIO Market Risk, and was corrected promptly. Because it was viewed as a one-off error, it did not trigger further inquiry. Later in May, in response to further losses in the Synthetic Credit Portfolio, a review of the West End calculated discovered that it was using the Uniform Rate model rather than Gaussian Copula model, contrary to the Model Review Group approval.

Although this error did not have a significant effect on the VaR, an operational error was found in the calculation of the relative changes in hazard rates and correlation estimates. Specifically, after subtracting the old rate from the new rate, the spreadsheet divided the result by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a factor of two and of lowering the VaR, minimizing the estimate of the potential loss in the Synthetic Credit Portfolio, which ultimately grew to more than $6.2 billion. Despite this humongous loss, JPMorgan Chase disclosed full-year 2012 record net income of $21.3 billion on revenue of $99.9 billion, guaranteeing Dimon’s survival at the helm for the moment.

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The Perennial Profit Challenge: 2013 Roadmap for World Exchanges

Posted on December 16, 2012. Filed under: Exchanges, Practitioners, Regulations, Securities and Exchange Commission, Technology | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

Edgar Perez and Sir Richard Branson

Edgar Perez and Sir Richard Branson

Financial exchanges play a vital role in economic development as one of the primary tools for the allocation of capital in both developed economies and emerging ones. The indices created using the platforms provided by global exchanges are used by the financial services industry and the government as barometers of economic health and a predictor of national financial well-being.

However, the exchanges model has changed dramatically over the past decade starting with demutualization. The first wave began with the Stockholm Stock Exchange (STO) in 1993 and included the Bombay Stock Exchange (BSE) in 1995 and Borsa Italiana (BVME) in 1997. Demutualization was followed by a second stage in which a number of exchanges became publicly traded and profit-seeking companies listed on their own platform, with the Australian Securities Exchange (ASX) being the first to follow this model in 1998. Such restructurings are still taking place in exchanges all over the world.

Exchanges have come under increasing regulatory attention. In the US, for instance, the Securities and Exchange Commission is expanding an enforcement probe into a broader look at how exchanges develop new products, communicate with investors and provide incentives to trade; this was sparked partly by an SEC probe into trading order types apparently benefiting high-speed traders, whose activity comprises more than half of all stock-trading volume.

As companies exercise more flexibility in seeking to raise capital outside their national boundaries, the environment has become even more competitive for exchanges. Furthermore, they are hugely capital intensive (mostly due to the IT infrastructure required for increasingly high frequency trading), reason why some exchanges are looking to grow through acquisitions in order to enjoy greater economies of scale.

While these challenges are common to exchanges worldwide, the impact on their bottom lines has been rather diverse. For instance, the Philippines Stock Exchange (PSE) doubled its profit in the first nine months of 2012 compared to last year. While the exchange benefited tremendously from the favorable economic environment and sky-high optimism in the country, there were a number of reforms implemented by the PSE, including the rollout of a new trading system, extension of trading hours and implementation of multiple regulatory and governance enhancements.

London Stock Exchange (LSE) reported a profit for the first half of the year nearly unchanged from last year as strong performance in information services helped offset weak capital markets. The exchange highlighted the benefits of its increasingly diversified international group and the growth from its Information, Post Trade and Technology businesses; the exchange reported a 66 percent increase in Information Services revenue, while Capital Markets revenue dropped 19 percent.

On the other side of the spectrum, NYSE Euronext, the operator of the New York Stock Exchange and other stock exchanges, announced that its third-quarter profit fell 46 percent, which the company attributed to reduced average daily trading volumes, primarily related to its derivatives business. It said its results last year were helped by the extreme volatility of the markets in Europe and the United States due to debt concerns. Certainly, volatility has declined considerably since then, reaching multi-year lows in August 2012.

Exchanges are responding to this increasing competition in a number of ways. Negotiating mergers has been the first option considered by a number of companies, only to be derailed in some cases by regulators or rebuffed by targets. NYSE Euronext face resistance from European regulators on its proposed combination with Deutsche Boerse; ASX’s agreement with Singapore Exchange (SGX) fell through as well; LSE dropped its bid for Toronto Stock Exchange (TSX) after its owners spurned them in favor of the bid from a group of Canadian banks and pensions. However, that doesn’t mean that exchanges will not attempt to find combinations that don’t run afoul of regulations, just because mergers almost in all cases strive to provide an avenue to widen their business model and to exploit economies of scale, economies of network, cross selling opportunities and trading hours; for instance in Asia, Tokyo and Hong Kong shortened their midday halt to one hour last year, while Singapore scrapped its lunch break altogether, joining Australia, South Korea and India on the list of exchanges that have uninterrupted trading days.

Second, developing cutting edge-edge technology and its further commercialization is paving the way to extract additional profits from investments already paid. For instance, LSE leveraged its IT investments with the adoption of an outsourced managed services model that allowed the exchange to run other exchanges, such as the Johannesburg Stock Exchange, using its own platform. Building a major technology franchise through outsourcing was vital for the LSE if it was to continue to compete with the likes of NYSE Euronext and Nasdaq OMX, which had extended their brand and influence in several emerging markets through major technology deals.

Finally, exchanges are standing up to the challenge of diversifying their business model. Exchanges that were primarily focused on cash trading decided to integrate services such as the trading of derivative financial instruments markets. As it was the case for LSE, information services delivered in machine-readable format are providing growth opportunities for exchanges worldwide; RapiData, company acquired by Nasdaq OMX, enabled the company to deliver U.S. government and other economic news directly from the source to customers interested in receiving information in an electronic feed, giving them instant access to events that are incorporated into algorithmic trading systems. The perennial appetite of high-frequency and algorithmic trading firms for faster access to trading data is also encouraging exchanges to provide colocation services that bring all participants equal access to their matching engines. Ultimately, exchanges will be forced to explore all upstream and downstream opportunities in the production chain of the exchange industry, from the above mentioned information services upstream to the integration of clearing and settlement services downstream.

Revenues at exchanges will need to evolve from its reliance on volume-dependent fees and commissions for a range of activities (including trading, listing, clearing, settlement, depository, custody and nominee services) to uncorrelated income sources that might not have existed just a few years ago; the infrastructure they have, the data they manage and proximity to their matching engine are all key assets that need to be fully exploited if exchanges are to succeed in 2013 and beyond.

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Long-Term Investors’ Fears Driving Increased Volatility

Posted on August 29, 2011. Filed under: Economy, Financial Crisis, Flash Crash, Strategies, Technology | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

The Speed Traders' Edgar Perez with BNN's Kim Parlee

The Speed Traders' Edgar Perez with BNN's Kim Parlee

For Edgar Perez, author of The Speed Traders, An Insider’s Look at the New High-Frequency Trading Phenomenon That is Transforming the Investing World (, increased volatility experienced by financial markets is being driven by long-term investors’ fears. Mr. Perez, who was recently featured on BNN’s Business Day and interviewed by Kim Parlee, reflected that similar concerns drove volatility to record heights during the Great Depression and Black Monday.

The stock market crash on October 29, 1929 set in motion a series of events that led to the Great Depression, but in fact, the American economy and global economy had been in turmoil six months prior to Black Tuesday, and a variety of factors before and after that fateful date in October caused and exacerbated the Great Depression. While America prospered during the 1920s, most of Europe, still reeling from the devastation of World War I, fell into economic decline. America soon became the world’s banker, and as Europe started defaulting on loans and buying less American products, the Great Depression spread. With only loose stock market regulations in place before the Great Depression, investors were able speculate wildly, buying stocks on margin, needing only 10% of the price of a stock to be able to complete the purchase. Rampant speculation led to falsely high stock prices, and when the stock market began to tumble in the months leading up to the October 1929 crash, speculative investors couldn’t make their margin calls, and a massive sell-off began. While the great rise in the stock market (from 181 points in early 1928 to 381 points in September 1929) was fueled by optimism and false hope, the plunge was flamed by stark fear.

Similar situation happened on Black Monday, the name given to Monday, October 19, 1987, when stock markets around the world ‘crashed’, shedding a huge value in a very short period. The crash began in Hong Kong, spread west through international time zones to Europe, hitting the United States after other markets had already declined by a significant margin. At the time, economists feared that if the U.S. economy faltered, the entire world economy would stumble and fall into recession again, as it had in 1981–82. Many observers now believe the panic of Black Monday simply reflected a spreading fear that the world situation was rapidly becoming unmanageable.

Fast forward to 2011, CNN’s Richard Quest concludes too that the causes of this latest crisis are fear, worry and concern, three uncomfortable bedfellows that have wreaked havoc on the world’s financial markets. “What pushed everyone over the edge was the debt ceiling debacle and the downgrading of U.S. debt by ratings agency Standard & Poor’s, that was followed by a 630 point fall in the Dow Jones index.”

Fear that the world situation is becoming unmanageable is driving long-term investors to dump equities and look for protection in less risky instruments, ironically, recently S&P downgraded U.S. treasuries. Economists at JPMorgan, in their weekly reprise of economic developments, blamed the recent global stock selloff on “a sense of policy paralysis in the U.S. and Europe, which has driven home the point that there is no cavalry to ride to the rescue.” While the sentiment is the same as in the 20s, 1987 and now, certain market participants will always look for a culprit, role played by high-frequency trading this time. No doubt if another crisis comes our way in the future, another group will receive the blame, only to be absolved by financial historians.

The Speed Traders' Edgar Perez on Canada's BNN's Business Day

The Speed Traders' Edgar Perez on BNN's Business Day

BNN’s Business Day puts a spotlight on the stocks and stories expected to move the markets, and then switches to minute-by-minute coverage throughout the trading day in Canada and the U.S. Kim Parlee, Marty Cej, Frances Horodelski, and Martin Baccardax along with BNN‘s team of reporters and expert guests provide comprehensive reporting along with the best background and analysis in the business. Business News Network (BNN) is the Canadian English language cable television business channel; BNNbroadcasts programming related to business and financial news and analysis.

The Speed Traders, published by McGraw-Hill Inc., is the most comprehensive, revealing work available on the most important development in trading in generations. High-frequency trading will no doubt play an ever larger role as computer technology advances and the global exchanges embrace fast electronic access. The Speed Traders explains everything there is to know about how today’s high-frequency traders make millions—one cent at a time. In this new title, The Speed Traders, Mr. Perez opens the door to the secretive world of high-frequency trading. Inside, prominent figures drop their guard and speak with unprecedented candidness about their trade. For more about The Speed Traders, readers can visit its Facebook and Twitter pages, as well as the most popular online retailers, including Amazon, Barnes & Noble and Borders, among others.

Mr. Perez is widely regarded as the pre-eminent networker in the specialized area of high-frequency trading. He has been featured on CNBC Cash Flow with Oriel Morrison (, BNN Business Day with Kim Parlee (, with Gregg Greenberg (, and Channel NewsAsia Cent & Sensibilities with Lin Xue Ling, and engaged as speaker at Harvard Business School’s 17th Annual Venture Capital & Private Equity Conference, High-Frequency Trading Leaders Forum 2011 (New York, Chicago, Hong Kong, Sao Paulo, Singapore), CFA Singapore, Hong Kong Securities Institute, Courant Institute of Mathematical Sciences at New York University (New York), Global Growth Markets Forum (London), Technical Analysis Society (Singapore), Middle East Hedge Funds Investors Summit 2012 (Riyadh, Saudi Arabia), among other global forums.

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