Mathematical Analysis of Average Rates of Return and Investment Decisions: The Missing Link

Carlo Alberto Magni. Mathematical Analysis of Average Rates of Return and Investment Decisions: The Missing Link. The Engineering Economist, 2014; 59 (3) DOI: 10.1080/0013791X.2014.881174

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Author(s)

Carlo Alberto Magni

Abstract

This article expands Teichroew, Robichek, and Montalbano’s (TRM; 1965a, 1965b) rate-of-return model into a complete and general model of economic profitability for investment decision making. Specifically, TRM’s assumptions are relaxed and a project rate of return is derived, expressing the project’s overall economic profitability; direct relations among rates, costs of capital, and net present value are supplied. The various value drivers are identified and isolated, and the net present value (NPW) is decomposed into financing NPV and investment NPV. The approach allows for any pattern of financing rates, investment rates, and costs of capital. Relations with old literature and new literature on rates of return are shown: the link between them is obtained by making use of the mean operator (i.e., affine combinations of rates) and via the one-to-one correspondence between rates and invested capitals.

Risk models-at-risk

Christophe M. Boucher, Jón Daníelsson, Patrick S. Kouontchou, Bertrand B. Maillet. Risk models-at-risk. The Journal of Banking & Finance, 2014; 44 DOI: 10.1016/j.jbankfin.2014.03.019

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Author(s)

Christophe M. Boucher, Jón Daníelsson, Patrick S. Kouontchou, Bertrand B. Maillet

Abstract

The experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risks. A key reason for this is that risk measures are subject to a model risk due, e.g. to specification and estimation uncertainty. While regulators have proposed that financial institutions assess the model risk, there is no accepted approach for computing such a risk. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting the imperfect risk forecasts by outcomes from backtesting frameworks, considering the desirable quality of VaR models such as the frequency, independence and magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections.

The impact of competition and information on intraday trading

Katya Malinova, Andreas Park. The impact of competition and information on intraday trading. The Journal of Banking & Finance, 2014; 44 DOI: 10.1016/j.jbankfin.2014.03.026

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Author(s)

Katya Malinova, Andreas Park

Abstract

In a dynamic model of financial market trading multiple heterogeneously informed traders choose when to place orders. Better informed traders trade immediately, worse informed delay – even though they expect the market to move against them. This behavior generates intraday patterns with decreasing spreads, decreasing probability of informed trading (PIN), and increasing volume. We predict that policies that foster market entry improve the welfare of uninformed traders and lead to increased market participation by incumbent traders. Technological advances that lead to better signal processing also encourage market participation and increase volume but at the expense of uninformed traders’ welfare.