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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 Best High Frequency Trading Book Now Available in Mandarin, Courtesy of Chinese Financial Publishing House

Posted on July 9, 2012. Filed under: Book Review, Exchanges, Flash Crash, Operations, Practitioners, Securities | Tags: , , , , , , , , , , , , , , , , , , |

The Speed Traders, An Insider’s Look at the New High-Frequency Trading Phenomenon That is Transforming the Investing World

The Speed Traders

The Speed Traders, Edgar Perez’s ground breaking work on high frequency trading, is now available in Mandarin, Courtesy of Chinese Financial Publishing House.  Unlike other works about HFT, Perez’s book provides readers with fresh, candid insight from the industry’s top HFT players.

Praise for The Speed Traders:

“Edgar’s book is fantastic . . . I recommend it highly.”
—Bart Chilton, Commissioner, United States Commodity Futures Trading Commission (CFTC)

“I have interviewed the most successful high-frequency traders in New York and Chicago, but I have learned so much more by reading Perez’s book. He covers the most relevant topics we need to know today and tomorrow.”
—Mark Abeshouse, Chairman, Augustus Capital

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Caixin Media Covers The Speed Traders Workshop 2012 in China

Posted on June 14, 2012. Filed under: Exchanges, Operations, Practitioners, Private Equity, Securities, Strategies | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

The Speed Traders Workshop 2012

2012年6月第二个交易日,上证指数因为特殊的波动引发市场关注。来自监管当局的消息称,交易所已经注意到了市场中的高频交易,但未发现有市场操纵行为。

将通过计算机控制的高频交易(High-frequency Trading,简称HFT)行为与人为的操纵市场联系到一起引人无限遐思。高频交易到底是加剧市场波动还是缩短市场波动?如何监管高频交易?是否应该像 对“粮仓中的老鼠”一样,限制高频交易?这都是摆在全球监管者面前的一道难题。尤其对于散户众多的中国证券市场,高频交易又会成为何种角色?

“美国各界仍在争论,焦点是该不该对高频交易有限制,高频交易到底是利大于弊还是弊大于利。”纽约华尔街一家全球宏观型对冲基金数量交易组的数量投资经理杨旭对财新记者表示。

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The Speed Traders Workshop 2012: News Coverage From China Business Network (CBN)

Posted on June 13, 2012. Filed under: Economy, Exchanges, Operations, Practitioners, Private Equity, Regulations, Securities, Strategies | Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |

Mr. Edgar Perez, Director of The Speed Traders Workshop

Edgar Perez援引数据介绍指出,目前在美国证券市场中的整体成交金额中有56%来自高频交易,而这种交易手法也是伴随着科技的发展、市场的竞争、以及监管政策的变化,在证券市场中自然演进所出现的。

频交易研究专家Edgar Perez近日在与第一财经采访时表示,高频交易是一种专注于“速度(speed)”的投资方法,主要以先进的电脑技术和设备寻求在极短时间内的获利,然 而这种投资方法与巴菲特的“价值投资”哲学并不矛盾,亦有助投资者能跳出经济周期和宏观大环境的制约,寻找到不为外界环境所左右的“阿尔法”值(即超出市 场基准的收益回报)。

Edgar Perez援引数据介绍指出,目前在美国证券市场中的整体成交金额中有56%来自高频交易,而这种交易手法也是伴随着科技的发展、市场的竞争、以及监管政策的变化,在证券市场中自然演进所出现的。

高频交易主要以电脑完成交易,数据处理可以在毫秒(0.001秒)之间,人力根本无法与之匹配,因此该种交易主要依赖先进的科学技术和电脑算法。高频交易的主要策略包括电子化交易、趋势追踪、相对价值套利、流动性监测、新闻解读分析和投资基金方法等。

To read the full article please click here.

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