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    ABSTRACT: This paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. We solve the model by means of the so-called expected future value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive approach. We compare our revenues to those obtained by additive bid prices, such as the bid prices derived from the deterministic equivalent model (DEM) of the compact representation of the SP model. Our computational experience shows that the revenues obtained by our approach are better for middle-range values of the load factor of demand, whereas the differences among all the approaches we have tested are insignificant for extreme values. Moreover, our approach requires significantly less computation time than does the optimization of DEM by plain use of optimization engines. Problem instances with 72 pairs of bundle-fare classes have been solved in less than one minute, with 800 pairs in less than five minutes, and with 4,000 pairs in less than one hour. The time taken by DEM was, in general, of one order of magnitude higher. Finally, for the three largest problem instances, and after two hours, the expected revenue returned by DEM was below that obtained by EFV by 13.47%1 17.14%, and 38.94%, respectively.
    Full-text · Article · Oct 2013 · Transportation Science
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    ABSTRACT: This paper proposes value-at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast-combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models—a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns. Copyright © 2011 John Wiley & Sons, Ltd.
    Full-text · Article · Jan 2013 · Journal of Forecasting
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    ABSTRACT: Over the last decade, I have put together a new theory of leadership. This paper describes its four propositions, which are consistent with the research literature but which lead to conclusions that are not commonly held and seldom put into practice. The first proposition is a model describing the territory of leadership that is different from either the Leadership Qualities Framework, 2006 or the Medical Leadership Competency Framework, 2010, both of which have been devised specifically for the NHS (National Health Service). The second proposition concerns the ill-advised attempt of individuals to become expert in all aspects of leadership: complete in themselves. The third suggests how personality and capability are related. The fourth embraces and recommends the notion of complementary differences among leaders. As the NHS seeks increasing leadership effectiveness, these propositions may need to be considered and their implications woven into the fabric of NHS leader selection and development. Primary Health Care research, like all fields of collective human endeavour, is eminently in need of sound leadership and the same principles that facilitate sound leadership in other fields is likely to be relevant to research teams.
    Preview · Article · Oct 2012 · Primary Health Care Research & Development
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