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on Financial Markets |
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Issue of 2007‒06‒02
eight papers chosen by |
| By: | Caio Ibsen R. Almeida; José Valentim M. Vicente |
| Abstract: | This paper analyzes how including options in the estimation of a dynamic term structure model impacts the way it captures term structure movements. Two versions of a multi-factor Gaussian model are compared: One adopting only bonds data, and the other adopting a joint dataset of bonds and options. Term structure movements extracted under each version behave distinctly, with slope and curvature presenting higher mean reversion rates when options are adopted. The composition of bond risk premium is also affected, with considerably more weight attributed to the level factor when options are included. The inclusion of options in the estimation of the dynamic model also improves the pricing of out-of-sample options. |
| Date: | 2006–12 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:128&r=fmk |
| By: | Philippe Bacchetta (Study Center Gerzensee, University of Lausanne, Swiss Finance Institute & CEPR); Elmar Mertens (Study Center Gerzensee & University of Lausanne); Eric VanvWincoop (University of Virginia & NBER) |
| Abstract: | There is widespread evidence of excess return predictability in financial markets.vIn this paper we examine whether this predictability is related to expectational errors. To consider this issue, we use data on survey expectations of market participants in the stock market, the foreign exchange market, and the bond and money markets in various countries. We find that the predictability of expectational errors coincides with the predictability of excess returns: when a variable predicts expectational errors in a given market, it typically predicts the excess return as well. Understanding expectational errors appears crucial for explaining excess return predictability. |
| Keywords: | excess returns, expectations survey, predictability |
| JEL: | F31 G12 G14 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp15&r=fmk |
| By: | Frankel, David M. |
| Abstract: | A theory is developed that explains how the stock market can crash in the absence of news about fundamentals, and why crashes are more common than frenzies. A crash occurs via the interaction of rational and naive investors. Naive traders believe in a simple (but reasonable) statistical model of stock prices: that prices follow a random walk with serially correlated volatility. They predict future volatility adaptively, as a weighted average of past squared price changes. In a crash, the naive traders lower their demand in response to the apparent increase in volatility. This lowers the risk bearing capacity of the market, so that the lower crash price clears the market. Unlike other explanations of market crashes, this mechanism is fundamentally asymmetric: the stock price cannot rise sharply, so frenzies or bubbles cannot occur. |
| Keywords: | Stock market crashes, adaptive expectations, volatility feedback, excess volatility. |
| JEL: | G1 |
| Date: | 2007–05–24 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genres:12817&r=fmk |
| By: | Tony Berrada (University of Lausanne and Swiss Finance Institute) |
| Abstract: | We consider a pure exchange economy with incomplete information. Some agents in the economy display learning bias and over- or underreact to the arrival of new information. We study, by simulation, the distribution of irrational agents’ consumption shares. We find that over a reasonable horizon (50 years) under- or over-reaction has little impact on an agent’s consumption share, when parameters of the model are chosen to fit aggregate consumption data in the US. We also show that agents’impact on prices is increasing in their consumption share and conclude that biased agents can significantly influence equilibrium quantities. |
| Keywords: | Bounded rationality, incomplete information, equilibrium |
| JEL: | G12 |
| Date: | 2003–08 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp07&r=fmk |
| By: | François-Serge Lhabitant (University of Lausanne, HEC and EDHEC Business School) |
| Abstract: | Hedge fund indices have grown in numbers over the recent years and made their presence widespread through a number of providers. Assets linked to hedge fund indices currently exceed $12 billion, and the debate is now focusing on whether they should be considered as eligible assets for UCITS III funds. The consequences of a positive or negative answer from regulators are extremely important. In particular, a positive answer would imply that any non-approved offshore hedge fund can be indirectly distributed to any retail investors via an UCITS III vehicle, as long as this fund belongs to a hedge fund index. The problem is that existing hedge fund indices are fundamentally different from indices of traditional assets. In this paper, we review non-investable hedge fund indices, the various steps of their construction and the numerous performance biases that affect their returns. These biases are so important that in our view, the majority of existing hedge fund indices are not representative of the hedge fund universe – at best, they represent a biased sample of funds that have agreed to report to a database or an index provider. The case of the so-called investable hedge fund indices, which are often presented as an alternative to actively managed funds of hedge funds, is not much better. Our observations reveal that existing investable indices are less representative of the hedge fund universe and more biased than their non-investable cousins. They are, in essence, funds of hedge funds managed according to arbitrary rules and primarily designed to support high-fee tracking products. As a result of their numerous biases, lack of representativity and/or construction, our view is that existing hedge fund indices do not fulfil the three basic criteria required to become UCITS III eligible – sufficient diversification, ability to serve as an adequate benchmark and appropriate publication. We therefore suggest excluding them from the list of UCITS III eligible assets. Of course, in the future, this position could be revised once quality hedge fund indices are available and fulfil the aforementioned three basic criteria. |
| Keywords: | hedge funds, UCITS, regulation, indices |
| JEL: | G1 G18 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp14&r=fmk |
| By: | Antonio Garcia Pascual; Elina Ribakova; Renzo G. Avesani |
| Abstract: | The rapid mortgage credit growth experienced in recent years in mature and emerging countries has raised some stability concerns. Many European credit institutions in mature markets have reacted by increasing securitization, particularly via mortgage covered bonds. From the issuer's perspective, these instruments have become an attractive funding source and a tool for assetliability management; from the investor's perspective, covered bonds enjoy a favorable risk-return profile and a very liquid market. In this paper, we examine the two largest "jumbo" covered bond markets, Germany and Spain. We show how movements in covered bond prices can be used to analyze the credit developments of the underlying issuer and the quality of its mortgage portfolio. Our analysis also suggests that mortgage covered bonds could be of interest to other mature and emerging markets facing similar risks related to mortgage credit. |
| Keywords: | Bonds , Germany , Spain , Credit , Financial institutions , Economic indicators , |
| Date: | 2007–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:07/20&r=fmk |
| By: | Jaqueline Terra Moura Marins; Eduardo Saliby; Joséte Florencio do Santos |
| Abstract: | As in any Monte Carlo application, simulation option valuation produces imprecise estimates. In such an application, Descriptive Sampling (DS) has proven to be a powerful Variance Reduction Technique. However, this performance deteriorates as the probability of exercising an option decreases. In the case of out of the money options, the solution is to use Importance Sampling (IS). Following this track, the joint use of IS and DS is deserving of attention. Here, we evaluate and compare the benefits of using standard IS method with the joint use of IS and DS. We also investigate the influence of the problem dimensionality in the variance reduction achieved. Although the combination IS+DS showed gains over the standard IS implementation, the benefits in the case of out-of-the-money options were mainly due to the IS effect. On the other hand, the problem dimensionality did not affect the gains. Possible reasons for such results are discussed. |
| Date: | 2006–09 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:116&r=fmk |
| By: | Jaqueline Terra Moura Marins; Eduardo Saliby |
| Abstract: | Monte Carlo simulation is implemented in some of the main models for estimating portfolio credit risk, such as CreditMetrics, developed by Gupton, Finger and Bhatia (1997). As in any Monte Carlo application, credit risk simulation according to this model produces imprecise estimates. In order to improve precision, simulation sampling techniques other than traditional Simple Random Sampling become indispensable. Importance Sampling (IS) has already been successfully implemented by Glasserman and Li (2005) on a simplified version of CreditMetrics, in which only default risk is considered. This paper tries to improve even more the precision gains obtained by IS over the same simplified CreditMetrics' model. For this purpose, IS is here combined with Descriptive Sampling (DS), another simulation technique which has proved to be a powerful variance reduction procedure. IS combined with DS was successful in obtaining more precise results for credit risk estimates than its standard form. |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:132&r=fmk |