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ABSTRACT: Studies into the impact of top manager change on organization performance have revealed inconsistent findings. Using longitudinal data over a 12-year period on football organizations, we test for the short-term and long-term effects of manager change in comparison to the tenures of incumbent top managers. We find that long incumbent tenures are associated with performance far above the average. But when looking at change events, contrary to theoretical expectations, we find that change in the short term leads to a brief reprieve in poor performance only for performance to deteriorate in the long term as underlying weaknesses once again take hold. Our findings reveal the illusion of a short-term reprieve and the long-term consequences of this illusion. We map several implications for research and practice from our work.
Wiley-Blackwell: British Journal of Management. 05/2010;
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ABSTRACT: Volatility models such as GARCH, although misspecified with respect to the data-generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non-linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non-linear transformations of these forecasts will be biased estimators of the same non-linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non-linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non-linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in-sample and out-of-sample forecasts of three non-linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial. Copyright © 2006 John Wiley & Sons, Ltd.
Journal of Forecasting 10/2006; 25(7):481 - 494. · 0.93 Impact Factor
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ABSTRACT: Several recent empirical tests of the Capital Asset Pricing Model have been based on the conditional relationship between betas and market returns. This paper shows that this method needs reconsideration. An adjusted version of this test is presented. It is then demonstrated that the adjusted technique has similar, or lower, power to the more easily implemented CAPM test of Fama and MacBeth (1973) if returns are normally distributed. Copyright 2006 The Authors Journal compilation (c) 2006 Blackwell Publishing Ltd.
Journal of Business Finance & Accounting 01/2006; 33(7-8):1213-1239. · 0.69 Impact Factor
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International Journal of Forecasting. 02/2002; 18(3):409-419.
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ABSTRACT: A common approach to estimating the conditional volatility of short horizon asset returns is to use an exponentially weighted moving average (EWMA) of squared past returns. The EWMA estimator is based on the maximum likelihood estimator of the variance of the normal distribution, and is thus optimal when returns are conditionally normal. However, there is ample evidence that the conditional distribution of short horizon financial asset returns is leptokurtic, and so the EWMA estimator will generally be inefficient in the sense that it will attach too much weight to extreme returns. In this paper, we propose an alternative EWMA estimator that is robust to leptokurtosis in the conditional distribution of portfolio returns. The estimator is based on the maximum likelihood estimator of the standard deviation of the Laplace distribution, and is a function of an exponentially weighted moving average of the absolute value of past returns, rather than their squares. We employ the robust EWMA e...
03/2001;
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ABSTRACT: Preprint of an article published in Journal of Forecasting, 25(7), 481-494, November 2006; available online at http://www.interscience.wiley.com/ Many applications in finance use a non-linear transformation of the variance of returns. While the sample variance is an unbiased and consistent estimator of the population variance of returns, non-linear transformations of the sample variance will be consistent but biased. For estimates of non-linear transformations of the unconditional variance, this will rarely be a problem in practice, since sample sizes employed in finance are typically large. However, estimators of the conditional variance typically use sample sizes that are effectively much smaller, particularly those that apply an exponential weighting to returns such as GARCH or EMWA. Consequently, the bias is likely to be more important in estimating non-linear transformations of the conditional variance. In this paper, we derive a simple analytical approximation for the unconditional bias in estimators of non-linear transformations of the conditional variance, under the assumption that returns are conditionally normally distributed, and that the true conditional variance is generated by an arbitrary stochastic volatility model. As an illustration, we estimate the bias inherent in the RiskMetrics approach to the calculation of value at risk.
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ABSTRACT: The question of rules versus discretion has generated a great deal of debate in many areas of the social sciences. Recently, much of the discussion among academics and stakeholders about the assessment of research in UK higher education institutions has focused on the means that should be used to determine research quality. We present a model of committee decision-making when both rules and discretion are available. Some of the predictions of the model are tested empirically using the UK RAE 2001 results.
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ABSTRACT: This paper explores possible differences amongst two prominent generations in India by investigating managerial values, managerial practices and the possible impact of emerging managerial values on managerial practices. The findings of this study extend previous research on managerial values in emerging economies. The study provides evidence to suggest that young managers in India emphasize different managerial values and practices, and that managerial values have a significant impact on managerial practices. Implications of the results for scholars and practitioners are considered and future research directions identified.
Journal of World Business.
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ABSTRACT: While many recent empirical studies of the CAPM have used conditional beta tests, this technique has recently been shown to have several weaknesses. Here we introduce a new, more robust, net beta test which shares a number of characteristics with conditional beta tests. The method is extended to the multi-factor case when there are mimicking portfolios of assets for the underlying factors, including the Fama–French three-factor model. We demonstrate theoretically, by simulation and using market data that the net beta estimators have lower standard errors than those generated by the standard Fama–MacBeth test.
International Review of Financial Analysis.
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ABSTRACT: A common approach to forecasting the value at risk (VaR) of a portfolio is to assume a parametric density function for portfolio returns, and to estimate the parameters of the density function by maximum likelihood using historical data. In order to allow for volatility clustering in short horizon returns, this approach is typically combined with a conditional variance model such as EWMA or GARCH. However, these models implicitly assume that while the volatility of returns may be time-varying, the kurtosis of the return distribution is constant, at least over the estimation sample. In this paper, we show that the EWMA variance estimator can be obtained as a special case of a more general, exponentially weighted maximum likelihood (EWML) procedure that potentially allows for time-variation not only in the variance of the return distribution, but also in its higher moments. We use EWML to forecast VaR allowing for time-variation in both the variance and the kurtosis of daily equity returns. Our results show that the EWML based VaR forecasts are generally more accurate than those generated by both the EWMA and GARCH models, particularly at high VaR confidence levels.
International Journal of Forecasting.
