Fixed Income Trading Research Articles

Practice of Python in Programming and Optimization of Quantitative Analysis Model of Fixed Income

Python has become an indispensable tool in fixed income trading. This paper introduces the principles and general process of data analysis visualisation by analysing the basic operation and performance of personal investment and finance, combined with data analysis in the era of big data. The library of data analysis tools using Python has become an indispensable tool in fixed income trading. In fixed income trading, it supports fixed income traders to handle tasks such as bond pricing, interest rate modelling, and credit risk analysis Using Python's powerful data processing capabilities, traders can simplify data collection, analysis, and reporting to make better decisions. Based on the Python language, financial data is acquired from different platforms, processed and analysed step-by-step, ensuring that the model remains accurate and stable in different market conditions. Backtesting and ongoing performance evaluations have revealed that Python can be a valuable asset in the dynamic world of fixed income trading by refining trading strategies and effectively managing risk.

Investment Banking in 2008 (B): A Brave New World

In recent years Lehman Brothers, one of the five largest investment banks in the United States, had grown increasingly reliant on its fixed income trading and underwriting division, which served as the primary engine for its strong profit growth. The bank had also significantly increased its leverage over the same timeframe, going from a debt-to-equity ratio of 23.7x in 2003 to 35.2x in 2007. As leverage increased, the ongoing erosion of the mortgage-backed industry began to impact Lehman significantly and its stock price plummeted. Unfortunately, public outcry over taxpayer assumption of $29 billion in potential Bear losses made repeating such a move politically untenable. The surreal scene of potential buyers traipsing into an investment bank's headquarters over the weekend to consider various merger or spin-out scenarios repeated itself once again. This time, the Fed refused to back the failing bank's liabilities, attempting instead to play last-minute suitors Bank of America, HSBC, Nomura Securities, and Barclay's off each other, jawboning them by arguing that failing to step up to save Lehman would cause devastating counterparty runs on their own capital positions. The Fed's desperate attempts to arrange its second rescue of a major U.S. investment bank in six months failed when it refused to backstop losses from Lehman's toxic mortgage holdings. Complicating matters was Lehman's reliance on short-term repo loans to finance its balance sheet. Unfortunately, such loans required constant renewal by counterparties, who had grown increasingly nervous that Lehman would lose the ability to make good on its trades. With this sentiment swirling around Wall Street, Lehman was forced to announce the largest Chapter 11 filing in U.S. history, listing assets of $639 billion and liabilities of $768 billion. The second domino had fallen. It would not be the last.This case covers the period from the sale of Bear Stearns to JP Morgan to the conversion into bank holding companies by Goldman Sachs and Morgan Stanley, including the Lehman Brothers bankruptcy and the sale of Merrill Lynch to Bank of America. The case explains the new global paradigm for the investment banking industry, including increased regulation, fewer competitors, lower leverage, reduced proprietary trading, and-potentially-reduced profits.

The Behavior of Dealers and Clients on the European Corporate Bond Market: The Case of Multi-Dealer-to-Client Platforms

For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditionally quote-driven (i.e., dealer-driven) rather than order-driven, the market for corporate bonds is still mainly dominated by voice trading, but a lot of electronic platforms have emerged. These electronic platforms make it possible for buy-side agents to simultaneously request several dealers for quotes, or even directly trade with other buy-siders. The research presented in this paper is based on a large proprietary database of requests for quotes (RFQ) sent, through the multi-dealer-to-client (MD2C) platform operated by Bloomberg Fixed Income Trading, to one of the major liquidity providers in European corporate bonds. Our goal is (i) to model the RFQ process on these platforms and the resulting competition between dealers, and (ii) to use our model in order to implicit from the RFQ database the behavior of both dealers and clients on MD2C platforms.

Agents' Behavior on Multi-Dealer-to-Client Bond Trading Platforms

For the last two decades, most nancial markets have undergone an evolution toward electroni cation. The market for corporate bonds is one of the last ma jor nancial markets to follow this unavoidable path. Traditionally quote-driven (that is, dealer-driven) rather than order-driven, the market for corporate bonds is still mainly dominated by voice trading, but a lot of electronic platforms have emerged that make it possible for buy-side agents to simultaneously request several dealers for quotes, or even directly trade with other buy-siders. The research presented in this article is based on a large proprietary database of requests for quotes (RFQ) sent, through the multi-dealer-to-client (MD2C) platforms operated by Bloomberg Fixed Income Trading and Tradeweb, to one of the ma jor liquidity providers in European corporate bonds. Our goal is (i) to model the RFQ process on these platforms and the resulting competition between dealers, (ii) to use the RFQ database in order to implicit from our model the behavior of both dealers and clients on MD2C platforms, and (iii) to study the in uence of several bond characteristics on the behavior of market participants.

Labor, Language, and Finance Capital

Between the credit crunch and the greek debt crisis, the cohead of emea global finance kindly found time to walk me through the fixed-income trading floor of Barclays Capital. I noted no vestige of bull pen or bear pit in what seemed a high-end call center, but mainly I listened. At one point, in relation to derivatives trading, my guide spoke of “real money,” “less real money,” and “barely real money.” Perhaps prompted by the economic historian Robert Brenner's assurance that “the real economy” retains links to manufacture (1), I asked what made money real. The ensuing rebuke, graciously delivered, continues to give pause: “You sound like my father.” He had a point: given that I am a child of Britain's long postwar recovery through manufacture (there was no economic miracle here), might not my allegiance to a labor theory of value in matters literary be no more than an archaic, conditioned response? Karl Marx and Frederick Engels define language as “practical consciousness” made from “agitated layers of air” (51) and acting on a reality whose elements it renders inseparable from its processes. Their definition, in its subsumption of the intellectual in the laborious, might have been made for the academic son of a printer.

Adopting risk intelligence in today's volatile market

Risk intelligence refers to the ability to make informed decisions about risks based on historical, current and future views of a business. Its profile in the financial services industry has risen significantly since the collapse of Lehman's in 2008. Here, a lack of vital and updated risk information such as counterparty risk exposures, liquidity numbers and risk-based performance metrics for intraday trading made the ability to make informed decisions very difficult. As such, financial institutions (FIs) were in jeopardy of making the wrong decisions and in many cases no decisions were being taken. As these dramatic events showed, pinpointing where risk lies is not always straightforward and as a result many financial institutions now consider navigating the volatility of today's markets without the ability to understand and manage risk intraday to be very dangerous. Risk managers are now rapidly changing their approach to risk management with real-time risk intelligence becoming the goal for many FIs, particularly within the trading and capital markets arena. Many are looking to roll out real-time analytics across their operations including foreign exchange, interest-rate, credit, equities and fixed-income trading. To date only a handful of FIs have this capability and it is still limited, most are still struggling with the fundamentals of consolidated or enterprise risk management. Based on the recent Chartis Research report, Real-time Risk Intelligence — Leading Practices from Quartet FS (2010, London), the author suggests why conventional risk management methods no longer suffice and provides an overview of some of the difficulties in achieving real-time risk intelligence, as well as describing some of the new approaches forward-thinking FIs are taking in order to meet the requirements of today's market.

Hedge Fund Risk Factors and the Value at Risk of Fixed Income Trading Strategies

This article analyzes the risk characteristics for various hedge fund strategies specializing in fixed income instruments. Because some fixed income hedge fund strategies have exceptionally high autocorrelations in reported returns and this is taken as evidence of return smoothing, we first develop a method to completely eliminate any order of serial correlation across a wide array of time series processes. Once this is complete, we determine the underlying risk factors to the adjusted hedge fund returns and examine the incremental benefit attained from using nonlinear payoffs relative to the more traditional linear factors. The hedge fund indices have a very strong exposure to high-yield credit. In general, we find a marginal benefit to using the nonlinear risk factors in terms of the ability to explain reported returns. Finally, we examine the benefit of using various factor structures for estimating the value-at-risk of the hedge funds. We find that for some of the hedge fund strategies, downside risk estimates can be quite substantial. <b>TOPICS:</b>Factor-based models, analysis of individual factors/risk premia

A Critique of Modeled Credit Default Swap Duration

With the emergence and widespread adoption of credit derivatives, determining the price volatility of a credit default swap has become fundamental to fixed income trading and valuation. The author asserts that this problem has not received adequate treatment, and presents a brief case study that compares the modeled durations of three synthetic bonds with the duration of actual bonds. An alternative modeling approach is proposed that might better match the actual duration.

Where is the Efficient Frontier?

In a well developed financial market with liquid short term fixed income trading, the volatility of short term fixed income securities forms a continuous spectrum that converges to zero, the volatility of riskless asset. This means that the attainable combinations of risky assets contain the whole region under the capital market line and the capital market line is the efficient frontier for the risky assets.