999 resultados para dynamic decomposition


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Summary Throughout my thesis, I elaborate on how real and financing frictions affect corporate decision making under uncertainty, and I explore how firms time their investment and financing decisions given such frictions. While the macroeconomics literature has focused on the impact of real frictions on investment decisions assuming all equity financed firms, the financial economics literature has mainly focused on the study of financing frictions. My thesis therefore assesses the join interaction of real and financing frictions in firms' dynamic investment and financing decisions. My work provides a rationale for the documented poor empirical performance of neoclassical investment models based on the joint effect of real and financing frictions on investment. A major observation relies in how the infrequency of corporate decisions may affect standard empirical tests. My thesis suggests that the book to market sorts commonly used in the empirical asset pricing literature have economic content, as they control for the lumpiness in firms' optimal investment policies. My work also elaborates on the effects of asymmetric information and strategic interaction on firms' investment and financing decisions. I study how firms time their decision to raise public equity when outside investors lack information about their future investment prospects. I derive areal-options model that predicts either cold or hot markets for new stock issues conditional on adverse selection, and I provide a rational approach to study jointly the market timing of corporate decisions and announcement effects in stock returns. My doctoral dissertation therefore contributes to our understanding of how under real and financing frictions may bias standard empirical tests, elaborates on how adverse selection may induce hot and cold markets in new issues' markets, and suggests how the underlying economic behaviour of firms may induce alternative patterns in stock prices.

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The network revenue management (RM) problem arises in airline, hotel, media,and other industries where the sale products use multiple resources. It can be formulatedas a stochastic dynamic program but the dynamic program is computationallyintractable because of an exponentially large state space, and a number of heuristicshave been proposed to approximate it. Notable amongst these -both for their revenueperformance, as well as their theoretically sound basis- are approximate dynamic programmingmethods that approximate the value function by basis functions (both affinefunctions as well as piecewise-linear functions have been proposed for network RM)and decomposition methods that relax the constraints of the dynamic program to solvesimpler dynamic programs (such as the Lagrangian relaxation methods). In this paperwe show that these two seemingly distinct approaches coincide for the network RMdynamic program, i.e., the piecewise-linear approximation method and the Lagrangianrelaxation method are one and the same.

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Several patient-related variables have already been investigated as predictors of change in psychodynamic psychotherapy. Defensive functioning is one of them. However, few studies have investigated adaptational processes, encompassing defence mechanisms and coping, from an integrative or comparative viewpoint. This study includes 32 patients, mainly diagnosed with adjustment disorder and undergoing time-limited psychodynamic psychotherapy lasting up to 40 sessions, and will focus on early change in defence and coping. Observer-rater methodology was applied to the transcripts of two sessions of the first part of the psychotherapeutic process. It is assumed that the contextual-relational variable of therapeutic alliance intervenes as moderator on change in adaptational processes. Results corroborated the hypothesis, but only for coping, whereas for defences, overall functioning remained stable over the first 20 sessions of psychotherapy. These results are discussed within the framework of disentangling processes underlying adaptation, i.e., related to issues on trait and state aspects, as well as the role of the therapeutic alliance.

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Many revenue management (RM) industries are characterized by (a) fixed capacities in theshort term (e.g., hotel rooms, seats on an airline flight), (b) homogeneous products (e.g., twoairline flights between the same cities at similar times), and (c) customer purchasing decisionslargely influenced by price. Competition in these industries is also very high even with just twoor three direct competitors in a market. However, RM competition is not well understood andpractically all known implementations of RM software and most published models of RM donot explicitly model competition. For this reason, there has been considerable recent interestand research activity to understand RM competition. In this paper we study price competitionfor an oligopoly in a dynamic setting, where each of the sellers has a fixed number of unitsavailable for sale over a fixed number of periods. Demand is stochastic, and depending on howit evolves, sellers may change their prices at any time. This reflects the fact that firms constantly,and almost costlessly, change their prices (alternately, allocations at a price in quantity-basedRM), reacting either to updates in their estimates of market demand, competitor prices, orinventory levels. We first prove existence of a unique subgame-perfect equilibrium for a duopoly.In equilibrium, in each state sellers engage in Bertrand competition, so that the seller withthe lowest reservation value ends up selling a unit at a price that is equal to the equilibriumreservation value of the competitor. This structure hence extends the marginal-value conceptof bid-price control, used in many RM implementations, to a competitive model. In addition,we show that the seller with the lowest capacity sells all its units first. Furthermore, we extendthe results transparently to n firms and perform a number of numerical comparative staticsexploiting the uniqueness of the subgame-perfect equilibrium.

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Perceptual maps have been used for decades by market researchers to illuminatethem about the similarity between brands in terms of a set of attributes, to position consumersrelative to brands in terms of their preferences, or to study how demographic and psychometricvariables relate to consumer choice. Invariably these maps are two-dimensional and static. Aswe enter the era of electronic publishing, the possibilities for dynamic graphics are opening up.We demonstrate the usefulness of introducing motion into perceptual maps through fourexamples. The first example shows how a perceptual map can be viewed in three dimensions,and the second one moves between two analyses of the data that were collected according todifferent protocols. In a third example we move from the best view of the data at the individuallevel to one which focuses on between-group differences in aggregated data. A final exampleconsiders the case when several demographic variables or market segments are available foreach respondent, showing an animation with increasingly detailed demographic comparisons.These examples of dynamic maps use several data sets from marketing and social scienceresearch.

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We present a novel numerical approach for the comprehensive, flexible, and accurate simulation of poro-elastic wave propagation in 2D polar coordinates. An important application of this method and its extensions will be the modeling of complex seismic wave phenomena in fluid-filled boreholes, which represents a major, and as of yet largely unresolved, computational problem in exploration geophysics. In view of this, we consider a numerical mesh, which can be arbitrarily heterogeneous, consisting of two or more concentric rings representing the fluid in the center and the surrounding porous medium. The spatial discretization is based on a Chebyshev expansion in the radial direction and a Fourier expansion in the azimuthal direction and a Runge-Kutta integration scheme for the time evolution. A domain decomposition method is used to match the fluid-solid boundary conditions based on the method of characteristics. This multi-domain approach allows for significant reductions of the number of grid points in the azimuthal direction for the inner grid domain and thus for corresponding increases of the time step and enhancements of computational efficiency. The viability and accuracy of the proposed method has been rigorously tested and verified through comparisons with analytical solutions as well as with the results obtained with a corresponding, previously published, and independently bench-marked solution for 2D Cartesian coordinates. Finally, the proposed numerical solution also satisfies the reciprocity theorem, which indicates that the inherent singularity associated with the origin of the polar coordinate system is adequately handled.

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This paper presents a dynamic choice model in the attributespace considering rational consumers that discount the future. In lightof the evidence of several state-dependence patterns, the model isfurther extended by considering a utility function that allows for thedifferent types of behavior described in the literature: pure inertia,pure variety seeking and hybrid. The model presents a stationaryconsumption pattern that can be inertial, where the consumer only buysone product, or a variety-seeking one, where the consumer buys severalproducts simultane-ously. Under the inverted-U marginal utilityassumption, the consumer behaves inertial among the existing brands forseveral periods, and eventually, once the stationary levels areapproached, the consumer turns to a variety-seeking behavior. An empiricalanalysis is run using a scanner database for fabric softener andsignificant evidence of hybrid behavior for most attributes is found,which supports the functional form considered in the theory.

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We see that the price of an european call option in a stochastic volatilityframework can be decomposed in the sum of four terms, which identifythe main features of the market that affect to option prices: the expectedfuture volatility, the correlation between the volatility and the noisedriving the stock prices, the market price of volatility risk and thedifference of the expected future volatility at different times. We alsostudy some applications of this decomposition.

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We present a polyhedral framework for establishing general structural properties on optimal solutions of stochastic scheduling problems, where multiple job classes vie for service resources: the existence of an optimal priority policy in a given family, characterized by a greedoid(whose feasible class subsets may receive higher priority), where optimal priorities are determined by class-ranking indices, under restricted linear performance objectives (partial indexability). This framework extends that of Bertsimas and Niño-Mora (1996), which explained the optimality of priority-index policies under all linear objectives (general indexability). We show that, if performance measures satisfy partial conservation laws (with respect to the greedoid), which extend previous generalized conservation laws, then theproblem admits a strong LP relaxation over a so-called extended greedoid polytope, which has strong structural and algorithmic properties. We present an adaptive-greedy algorithm (which extends Klimov's) taking as input the linear objective coefficients, which (1) determines whether the optimal LP solution is achievable by a policy in the given family; and (2) if so, computes a set of class-ranking indices that characterize optimal priority policies in the family. In the special case of project scheduling, we show that, under additional conditions, the optimal indices can be computed separately for each project (index decomposition). We further apply the framework to the important restless bandit model (two-action Markov decision chains), obtaining new index policies, that extend Whittle's (1988), and simple sufficient conditions for their validity. These results highlight the power of polyhedral methods (the so-called achievable region approach) in dynamic and stochastic optimization.

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This paper shows how recently developed regression-based methods for the decomposition ofhealth inequality can be extended to incorporate heterogeneity in the responses of health to the explanatory variables. We illustrate our method with an application to the GHQ measure of psychological well-being taken from the British Household Panel Survey. The results suggest that there is an important degree of heterogeneity in the association of health to explanatory variables across birth cohorts and genders which, in turn, accounts for a substantial percentage of the inequality in observed health.

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This paper argues that the strategic use of debt favours the revelationof information in dynamic adverse selection problems. Our argument is basedon the idea that debt is a credible commitment to end long term relationships.Consequently, debt encourages a privately informed party to disclose itsinformation at early stages of a relationship. We illustrate our pointwith the financing decision of a monopolist selling a good to a buyerwhose valuation is private information. A high level of (renegotiable)debt, by increasing the scope for liquidation, may induce the highvaluation buyer to buy early at a high price and thus increase themonopolist's expected payoff. By affecting the buyer's strategy, it mayreduce the probability of excessive liquidation. We investigate theconsequences of good durability and we examine the way debt mayalleviate the ratchet effect.

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The aim of this study was to assess whether Neisseria meningitidis, Listeria monocytogenes, Streptococcus pneumoniae and Haemophilus influenzae can be identified using the polymerase chain reaction technique in the cerebrospinal fluid of severely decomposed bodies with known, noninfectious causes of death or whether postmortem changes can lead to false positive results and thus erroneous diagnostic information. Biochemical investigations, postmortem bacteriology and real-time polymerase chain reaction analysis in cerebrospinal fluid were performed in a series of medico-legal autopsies that included noninfectious causes of death with decomposition, bacterial meningitis without decomposition, bacterial meningitis with decomposition, low respiratory tract infections with decomposition and abdominal infections with decomposition. In noninfectious causes of death with decomposition, postmortem investigations failed to reveal results consistent with generalized inflammation or bacterial infections at the time of death. Real-time polymerase chain reaction analysis in cerebrospinal fluid did not identify the studied bacteria in any of these cases. The results of this study highlight the usefulness of molecular approaches in bacteriology as well as the use of alternative biological samples in postmortem biochemistry in order to obtain suitable information even in corpses with severe decompositional changes.

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By means of classical Itô's calculus we decompose option prices asthe sum of the classical Black-Scholes formula with volatility parameterequal to the root-mean-square future average volatility plus a term dueby correlation and a term due to the volatility of the volatility. Thisdecomposition allows us to develop first and second-order approximationformulas for option prices and implied volatilities in the Heston volatilityframework, as well as to study their accuracy. Numerical examples aregiven.