917 resultados para expected returns


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For the last three decades, the Capital Asset Pricing Model (CAPM) has been a dominant model to calculate expected return. In early 1990% Fama and French (1992) developed the Fama and French Three Factor model by adding two additional factors to the CAPM. However even with these present models, it has been found that estimates of the expected return are not accurate (Elton, 1999; Fama &French, 1997). Botosan (1997) introduced a new approach to estimate the expected return. This approach employs an equity valuation model to calculate the internal rate of return (IRR) which is often called, 'implied cost of equity capital" as a proxy of the expected return. This approach has been gaining in popularity among researchers. A critical review of the literature will help inform hospitality researchers regarding the issue and encourage them to implement the new approach into their own studies.

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The proposed study will be displayed on a poster board that contains the research methods and results due to the use of music intervention with students with ASD.

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In this dissertation, I investigate three related topics on asset pricing: the consumption-based asset pricing under long-run risks and fat tails, the pricing of VIX (CBOE Volatility Index) options and the market price of risk embedded in stock returns and stock options. These three topics are fully explored in Chapter II through IV. Chapter V summarizes the main conclusions. In Chapter II, I explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. I estimate the structural parameters of the proposed model by maximum likelihood. I find that the stochastic volatility model with fat tails can, without resorting to high risk aversion, generate implied risk premium, expected risk free rate and their volatilities comparable to the magnitudes observed in data. In Chapter III, I examine the pricing performance of VIX option models. The contention that simpler-is-better is supported by the empirical evidence using actual VIX option market data. I find that no model has small pricing errors over the entire range of strike prices and times to expiration. In general, Whaley’s Black-like option model produces the best overall results, supporting the simpler-is-better contention. However, the Whaley model does under/overprice out-of-the-money call/put VIX options, which is contrary to the behavior of stock index option pricing models. In Chapter IV, I explore risk pricing through a model of time-changed Lévy processes based on the joint evidence from individual stock options and underlying stocks. I specify a pricing kernel that prices idiosyncratic and systematic risks. This approach to examining risk premia on stocks deviates from existing studies. The empirical results show that the market pays positive premia for idiosyncratic and market jump-diffusion risk, and idiosyncratic volatility risk. However, there is no consensus on the premium for market volatility risk. It can be positive or negative. The positive premium on idiosyncratic risk runs contrary to the implications of traditional capital asset pricing theory.

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The purpose of this study was to analyze the behavior of Sell-Side analysts and analysts propose a classification, considering the performance of the price forecasts and recom- mendations (sell-hold-buy) in the Brazilian stock market. For this, the first step was to analyze the consensus of analysts to understand the importance of this collective interven- tion in the market; the second was to analyze the analysts individually to understand how improve their analysis in time. Third was to understand how are the main methods of ranking used in markets. Finally, propose a form of classification that reflects the previous aspects discussed. To investigate the hypotheses proposed in the study were used linear models for panel to capture elements in time. The data of price forecasts and analyst recommendations individually and consensus, in the period 2005-2013 were obtained from Bloomberg R ○ . The main results were: (i) superior performance of consensus recommen- dations, compared with the individual analyzes; (ii) associating the number of analysts issuing recommendations with improved accuracy allows supposing that this number may be associated with increased consensus strength and hence accuracy; (iii) the anchoring effect of the analysts consensus revisions makes his predictions are biased, overvaluating the assets; (iv) analysts need to have greater caution in times of economic turbulence, noting also foreign markets such as the USA. For these may result changes in bias between optimism and pessimism; (v) effects due to changes in bias, as increased pessimism can cause excessive increase in purchase recommendations number. In this case, analysts can should be more cautious in analysis, mainly for consistency between recommendation and the expected price; (vi) the experience of the analyst with the asset economic sector and the asset contributes to the improvement of forecasts, however, the overall experience showed opposite evidence; (vii) the optimism associated with the overall experience, over time, shows a similar behavior to an excess of confidence, which could cause reduction of accuracy; (viii) the conflicting effect of general experience between the accuracy and the observed return shows evidence that, over time, the analyst has effects similar to the endowment bias on assets, which would result in a conflict analysis of recommendations and forecasts ; (ix) despite the focus on fewer sectors contribute to the quality of accuracy, the same does not occur with the focus on assets. So it is possible that analysts may have economies of scale when cover more assets within the same industry; and finally, (x) was possible to develop a proposal for classification analysts to consider both returns and the consistency of these predictions, called Analysis coefficient. This ranking resulted better results, considering the return / standard deviation.

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Peer reviewed

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Peer reviewed

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Acknowledgements: We thank INREV (the European Association for Investors in Non-Listed Real Estate Vehicles) for funding a previous version of this research and providing non-listed fund data as well as very useful comments. This version is published as Delfim, J.-C. and Hoesli, M., 2015, Risk Factor Analysis of European Non-Listed Real Estate Funds, Amsterdam: INREV. The usual disclaimer applies. We also thank three anonymous reviewers and the guest editor, Graeme Newell, for insightful remarks.

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This paper examines 'availability' and the input metrics of operational expenditure (OPEX) for wave energy projects and reports on a case study which assesses the impact of these inputs on project profit returns. Case study simulations modelled a 75 MW wave energy project at two locations; the west coast of Ireland and the north coast of Portugal. Access and availability with respect to weather windows at both locations are discussed and their impact on energy output and wave farm operations is quantified. The input metrics used to calculate OPEX of wave energy projects are defined as well as the impact of OPEX on project net present value (NPV) and internal rate of return (IRR). Results indicate that access and resultant availability factors have a significant impact on case study results by reducing energy output and correspondingly financial returns. Furthermore, the technology maturity level designated for a project also impacts on availability factors and consequently energy output and NPV. Case study profits proved to be very sensitive to annual OPEX, especially if overhaul and replacement costs were accounted for. As a result of the impact of 'availability' on project profit returns. Feed-in tariffs will need to be tailored to the location in question as well as the device technology maturity level, with case study simulations indicating that high FIT will be required to support early stage WEC projects. (C) 2012 Elsevier Ltd. All rights reserved.

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I study the link between capital markets and sources of macroeconomic risk. In chapter 1 I show that expected inflation risk is priced in the cross section of stock returns even after controlling for cash flow growth and volatility risks. Motivated by this evidence I study a long run risk model with a built-in inflation non-neutrality channel that allows me to decompose the real stochastic discount factor into news about current and expected cash flow growth, news about expected inflation and news about volatility. The model can successfully price a broad menu of assets and provides a setting for analyzing cross sectional variation in expected inflation risk premium. For industries like retail and durable goods inflation risk can account for nearly a third of the overall risk premium while the energy industry and a broad commodity index act like inflation hedges. Nominal bonds are exposed to expected inflation risk and have inflation premiums that increase with bond maturity. The price of expected inflation risk was very high during the 70's and 80's, but has come down a lot since being very close to zero over the past decade. On average, the expected inflation price of risk is negative, consistent with the view that periods of high inflation represent a "bad" state of the world and are associated with low economic growth and poor stock market performance. In chapter 2 I look at the way capital markets react to predetermined macroeconomic announcements. I document significantly higher excess returns on the US stock market on macro release dates as compared to days when no macroeconomic news hit the market. Almost the entire equity premium since 1997 is being realized on days when macroeconomic news are released. At high frequency, there is a pattern of returns increasing in the hours prior to the pre-determined announcement time, peaking around the time of the announcement and dropping thereafter.

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This paper examines the effects of higher-order risk attitudes and statistical moments on the optimal allocation of risky assets within the standard portfolio choice model. We derive the expressions for the optimal proportion of wealth invested in the risky asset to show they are functions of portfolio returns third- and fourth-order moments as well as on the investor’s risk preferences of prudence and temperance. We illustrate the relative importance that the introduction of those higher-order effects have in the decision of expected utility maximizers using data for the US.

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There is a growing literature which documents the importance of early life environment for outcomes across the life cycle. Research, including studies based on Irish data, demonstrates that those who experience better childhood conditions go on to be wealthier and healthier adults. Therefore, inequalities at birth and in childhood shape inequality in wellbeing in later life, and the historical evolution of the mortality and morbidity of children born in Ireland is important for understanding the current status of the Irish population. In this paper, I describe these patterns by reviewing the existing literature on infant health in Ireland over the course of the 20th century. Up to the 1950s, infant mortality in Ireland (both North and South) was substantially higher than in other developed countries, with a large penalty for those born in urban areas. The subsequent reduction in this penalty, and the sustained decline in infant death rates, occurred later than would be expected from the experience in other contexts. Using records from the Rotunda Lying-in Hospital in Dublin, I discuss sources of disparities in stillbirth in the early 1900s. Despite impressive improvements in death rates since that time, a comparison with those born at the end of the century reveals that Irish children continue to be born unequal. Evidence from studies which track people across the life course, for example research on the returns to birthweight, suggests that the economic cost of this early life inequality is substantial.

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Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

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The benefits of diversification from international real estate securities are generally well established. However, the drivers of international real estate securities returns are insufficiently understood. We jointly examine the empirical implications of three major international asset pricing models that account for broad macroeconomic risk factors. In addition, we develop the hypothesis that an indicator of mispriced credit is significant in explaining the time series variation in international real estate securities returns. We employ the returns generated by a large sample of firms from 20 countries over the period 1999 to 2011 to test our hypothesis. We find support for the predictions of the major international asset pricing models. We also find evidence in favour of our hypothesised link between local credit conditions and the performance of international real estate securities.