risk arbitrage

This digital document is a journal article from Journal of Mathematical Economics, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
We introduce and study the f”0-relevance property of a coherent measure of risk on a positions vector space with vector ordering. We show that it is equivalent to a special no arbitrage condition on bounded positions spaces. Continuity from below leads to representations of f”0-relevant coherent measures of risk based on equivalent functionals in Banach subspaces of the order dual. We define and describe f”0-martingales in a lattice, and present a solution to the hedging price problem: the asset price process is an order convergent f”0-martingale. Under the f”0-relevance hypothesis we study the relationship between worst conditional mean and value at risk.

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This digital document is a journal article from Journal of Accounting and Economics, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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We show that the accrual anomaly documented by Sloan (1996) [Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review 71: 289-315] is concentrated in firms with high idiosyncratic stock return volatility making it risky for risk-averse arbitrageurs to take positions in stocks with extreme accruals. Moreover, the accrual anomaly is found in low-price and low-volume stocks, suggesting that transaction costs impose further barriers to exploiting accrual mispricing.

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This digital document is a journal article from Decision Support Systems, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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Ever since the inception of Markowitz’s modern portfolio theory, static portfolio optimization techniques were gradually phased out by dynamic portfolio management due to the growth of popularity in automated trading. In view of the intensive computational needs, it is common to use machine learning approaches on Sharpe ratio maximization for implementing dynamic portfolio optimization. In the literature, return-based approaches which directly used security prices or returns to control portfolio weights were often used. Inspired by the arbitrage pricing theory (APT), some other efforts concentrate on indirect modelling using hidden factors. On the other hand, with regard to the proper risk measure in the Sharpe ratio, downside risk was considered a better substitute for variance. In this paper, we investigate how the Gaussian temporal factor analysis (TFA) technique can be used for portfolio optimization. Since TFA is based on the classical APT model and has the benefit of removing rotation indeterminacy via temporal modelling, using TFA for portfolio management allows portfolio weights to be indirectly controlled by several hidden factors. Moreover, we extend the approach to some other variants tailored for investors according to their investment objectives and degree of risk tolerance.

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This digital document is a journal article from Journal of Financial Economics, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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We extend the standard specification of the market price of risk for affine yield models, and apply it to U.S. Treasury data. Our specification often provides better fit, sometimes with very high statistical significance. The improved fit comes from the time-series rather than cross-sectional features of the yield curve. We derive conditions under which our specification does not admit arbitrage opportunities. The extension has extremely strong statistical significance for affine yield models with multiple square-root type variables. Although we focus on affine yield models, our specification can be used with other asset pricing models as well.

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This digital document is an article from Quarterly Journal of Business and Economics, published by University of Nebraska-Lincoln on January 1, 2003. The length of the article is 6960 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: Predicting successful takeovers and risk arbitrage.
Author: Ben Branch
Publication: Quarterly Journal of Business and Economics (Refereed)
Date: January 1, 2003
Publisher: University of Nebraska-Lincoln
Volume: 42 Issue: 1-2 Page: 3(16)

Distributed by Thomson Gale

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This digital document is a journal article from Journal of Accounting and Economics, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Transaction and holding costs make arbitrage costly. Mispricing exists to the extent that arbitrage costs prevent rational traders from fully eliminating inefficiencies. Although the relation between mispricing and transaction costs is well-known, the relation between mispricing and holding costs is misunderstood. One holding cost, idiosyncratic risk, is particularly misunderstood. Various myths are debunked, including the common myth that idiosyncratic risk matters because arbitrageurs only have access to a small number of projects [Shleifer and Vishny, 1997. The limits of arbitrage. The Journal of Finance 52, 35-55.]. The literature demonstrates that idiosyncratic risk is the single largest cost faced by arbitrageurs.

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This digital document is a journal article from Journal of Banking and Finance, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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A widespread approach in the implementation of asset pricing models is based on the periodic recalibration of its parameters and initial conditions to eliminate any conflict between model-implied and market prices. Modern no-arbitrage market models facilitate this procedure since their solution can usually be written in terms of the entire initial yield curve. As a result, the model fits (by construction) the interest rate term structure. This procedure is, however, generally time inconsistent since the model at time t=0 completely specifies the set of possible term structures for any t>0. In this paper, we analyze the pros and cons of this widespread approach in pricing and hedging, both theoretically and empirically. The theoretical section of the paper shows (a) under which conditions recalibration improves the hedging errors by limiting the propagation of an initial error, (b) that recalibration introduces time-inconsistent errors that violate the self-financing argument of the standard replication strategy. The empirical section of the paper quantifies the trade-off between (a) and (b) under several scenarios. First, we compare this trade-off for two economies with and without model specification error. Then, we discuss the trade-off when the underlying economy is not Markovian.

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