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The purpose of this paper is to study the pricing efficiency of convertible bonds and arbitrage opportunities between the convertible bonds and the underlying stocks thus improve market efficiency.

Using nonparametric fixed effect panel data model, the authors build pricing model of convertible bonds and obtain fitted value for them. Then the authors constructs simultaneous confidence band for the smooth function to identify mispricing and study the pricing efficiency and arbitrage opportunities of convertible bonds.

Result shows, convertible bonds’ prices largely depend on stock prices. Pricing efficiency does not improve during the past few years as there are quite a few trading opportunities. Arbitrage opportunities increase as the stock prices approach it maxima, and selling opportunities for convertible bonds surpass buying opportunities which indicates that investors use market neutral strategies to arbitrage. Pricing efficiencies varies a lot and it is affected by the features of the stocks and convertible bonds. Index stocks eligible for margin trading with high liquidity enjoy higher pricing efficiency.

The study does not take into account trading cost and risk management measures.

Arbitrage between the underlying and the convertible bonds is profitable and contributes to pricing efficiency therefore should be encouraged. The regulator should pay attention to the extreme mispricing of the underlying and convertible bonds which cannot be corrected by the market as there might be manipulation.

Since traditional pricing methods are based on the framework of non-arbitrage equilibrium with the assumption of balanced and perfect market, there are many restrictions in the pricing process and the practical utility is somewhat limited, and the impractical assumptions lead to model risk. This study uses nonparametric regression to study the pricing of convertible bonds thus circumvents the problem of model risk. Simultaneous confidence band for smooth function identifies mispricing and explicitly reflects the variation of pricing efficiency as well as signalizes trading opportunities. Application of nonparametric regression and simultaneous confidence band in derivative pricing is advantageous in accuracy and simplicity.

On October 22, 2004, junior trader Mary Lucas was browsing through the recent trading activities of a few convertible bonds the firm held. First Convergence Inc. was a hedge fund specializing in convertible arbitrage founded by three Wall Street traders in 2002. Prior to starting at the firm, she had known little about convertible bonds. Now she stayed late almost every day in order to learn as much about the business as possible. Suddenly, she noticed something unusual about the trading of a convertible bond issued by Countrywide Financial Corporation (NYSE:CFC). Although the average daily trading volume on this bond had been only three thousand during the previous month, it had shot up to fifty thousand in the last three days. Lucas remembered this particular bond. In fact, First Convergence was actually holding a slightly different convertible bond (known as the liquid yield option note or LYON) issued by the same company. On August 20, Countrywide had offered to exchange the new convertible bond for the original LYON. First Convergence had accepted the exchange offer, thus ending up with the new convertible bond. At that time, Lucas was asked to help evaluate the offer, so she was familiar with the features of both bonds. “What's happening?” she asked herself. She quickly checked the recent price movement on Countrywide's stock. The stock had plunged 11.5 percent on Wednesday, October 20, after the company announced earnings below analysts' expectations. On the same day, trading on the convertible shot up. These two events must be related. But how? Is there a potential investment opportunity?

Understanding various features of a convertible bond; identifying and exploiting an arbitrage opportunity

In this chapter we concentrate at the most popular model for convertible bond (CB) valuation in a one-factor, stochastic underlying stock price setting. Through the last decade, the Tsiveriotis–Fernandes model (1998) has become a widely commented model that involves the state of default of the issuer of the CB. A routine approach to the solution of this model is the usage of methods of finite difference schemes (FDS). However, for many people trained in finance these methods are not very intuitive and they tend to ignore them and prefer to use binomial-tree approach as more intuitive technique. For that reason, our primary focus is to highlight the answer of the so far unanswered question: Does the binomial-tree approach to CBs provide accurate pricing, hedging, and risk assessment? We show on a set of representative examples that by using binomial-tree methodology one is unable to provide a consistent analysis of the pricing, hedging, and risk assessment. We start the chapter with the basics of CBs and CB market. We then explain the implementation of TF model within binary-tree approach. We conclude the chapter with performance valuation of binomial-tree approach showing unexpected behavior in practice areas such as pricing (profile of CB's price versus underlying stock price), hedging (performance of CB's delta, gamma, and convertible arbitrage strategy versus underlying stock), and risk assessment (Monte Carlo VaR with respect to the underlying).

Freeport-McMoRan's acquisition of Phelps Dodge created the world's largest publicly traded copper company. JPMorgan and Merrill Lynch advised the acquirer and arranged $17.5 billion in debt financing and $1.5 billion in credit facilities. In addition, these two firms underwrote $5 billion in equity capital through simultaneous offerings of Freeport-McMoRan common shares and mandatory convertible preferred shares. These financings created an optimal capital structure for the company that resulted in stronger credit ratings. The activities of the equity capital markets and sales groups at the underwriting firms are explored and the structure and benefits of mandatory convertible preferred shares is explained.

To understand the role of investment banks in advising a large corporation regarding an acquisition and related financings in the capital markets. As part of this, the activities of an investment banking firm's equity capital markets group and their underwriting risks are analyzed. Finally, the structure of a mandatory convertible security is reviewed in terms of benefits to both issuers and investors.

Purpose – This research pinpoints the limitations of conventional models for evaluating the performance of hedge funds and attempts to provide a new framework for modeling the dynamics of risk structures of hedge funds.

Methodology/approach – This chapter aims to explore how the systematic risk exposures of hedge funds vary over time and depend on exogenous variables that managers are supposed to use in their dynamic investment strategies. To achieve this, we used a Bayesian time-varying CAPM-based beta model within a state space technology.

Findings – The results showed that the volatility, term spread rate, and shocks in liquidity influence significantly on the time variation of hedge funds. Besides, the dynamics of beta indicates that the transmission channels of systematic risk are mainly the leverage levels of hedge funds and liquidity shocks.

Originality/value of chapter – These results are original because they help to explain how expected and unexpected hedge fund returns are correlated with the systematic risk factors via the beta dynamics.

The purpose of this paper is to examine the possibility of creating hedge funds “clones” using liquid exchange traded instruments.

Authors analyze the performance of fixed weight and extended Kalman filter generated clone portfolios (EKF) for 14 hedge fund strategies from February 2004 to September 2009. EKF approach does not indeed impose any normality constraints on the error terms which allow the filter to find the optimal recursive process by itself. Such models could adjust even faster to sudden shifts in market conditions vs a standard Kalman filter.

For five strategies out of 14, this work finds that EKF clones outperform their corresponding indices. Thus, for certain strategies, the possibility of cloning hedge fund returns is indeed real. Results should be however considered with caution.

This paper suggests that the most important benefits of clones are to serve as benchmarks and to help investors to better understand the various risk factors that impact hedge fund returns.

Rather than using fixed‐weight and rolling windows approaches (as Hasanhodzic and Lo), this work considers an extended version of the Kalman filter, a computational algorithm that better captures the time changing dynamics of hedge fund returns. Also, in order to be practical, this research considers investable factors and that the models themselves could not be constant over time.

Purpose – The purpose of this chapter is to estimate non-Gaussian distributions by means of Johnson distributions. An empirical illustration on hedge fund returns is detailed.

Methodology/approach – To fit non-Gaussian distributions, the chapter introduces the family of Johnson distributions and its general extensions. We use both parametric and non-parametric approaches. In a first step, we analyze the serial correlation of our sample of hedge fund returns and unsmooth the series to correct the correlations. Then, we estimate the distribution by the standard Johnson system of laws. Finally, we search for a more general distribution of Johnson type, using a non-parametric approach.

Findings – We use data from the indexes Credit Suisse/Tremont Hedge Fund (CSFB/Tremont) provided by Credit Suisse. For the parametric approach, we find that the SU Johnson distribution is the most appropriate, except for the Managed Futures. For the non-parametric approach, we determine the best polynomial approximation of the function characterizing the transformation from the initial Gaussian law to the generalized Johnson distribution.

Originality/value of chapter – These findings are novel since we use an extension of the Johnson distributions to better fit non-Gaussian distributions, in particular in the case of hedge fund returns. We illustrate the power of this methodology that can be further developed in the multidimensional case.

The author suggests an empirical model to analyze the investment style of individual hedge funds and fund of funds. This approach is based on a mixture of the style analysis approach suggested by Sharpe [1988], the factor push approach used in stress testing, and historical simulation. The parameter estimates from this model are inputs in the Value‐at‐Risk analysis for a sample of 2,934 funds over the 1994–2000 period. The in‐sample and out‐of‐sample results suggest that the proposed approach is useful and may constitute a valuable tool for assessing the investment style and risk of hedge funds.