The time series convergence of dispersion in financial analysts' forecasts

Finansanalytiker har en stor betydelse för finansmarknaderna, speciellt igenom att förmedla information genom resultatprognoser. Typiskt är att analytiker i viss grad är oeniga i sina resultatprognoser, och det är just denna oenighet analytiker emellan som denna avhandling studerar. Då ett företag rapporterar förluster tenderar oenigheten gällande ett företags framtid att öka. På ett intuitivt plan är det lätt att tolka detta som ökad osäkerhet. Det är även detta man finner då man studerar analytikerrapporter - analytiker ser ut att bli mer osäkra då företag börjar gå med förlust, och det är precis då som även oenigheten mellan analytikerna ökar. De matematisk-teoretiska modeller som beskriver analytikers beslutsprocesser har däremot en motsatt konsekvens - en ökad oenighet analytiker emellan kan endast uppkomma ifall analytikerna blir säkrare på ett individuellt plan, där den drivande kraften är asymmetrisk information. Denna avhandling löser motsägelsen mellan ökad säkerhet/osäkerhet som drivkraft bakom spridningen i analytikerprognoser. Genom att beakta mängden publik information som blir tillgänglig via resultatrapporter är det inte möjligt för modellerna för analytikers beslutsprocesser att ge upphov till de nivåer av prognosspridning som kan observeras i data. Slutsatsen blir därmed att de underliggande teoretiska modellerna för prognosspridning är delvis bristande och att spridning i prognoser istället mer troligt följer av en ökad osäkerhet bland analytikerna, i enlighet med vad analytiker de facto nämner i sina rapporter. Resultaten är viktiga eftersom en förståelse av osäkerhet runt t.ex. resultatrapportering bidrar till en allmän förståelse för resultatrapporteringsmiljön som i sin tur är av ytterst stor betydelse för prisbildning på finansmarknader. Vidare används typiskt ökad prognosspridning som en indikation på ökad informationsasymmetri i redovisningsforskning, ett fenomen som denna avhandling därmed ifrågasätter.

Comparison of Pavlovian serial conditional discrimination in rats and hamsters in the same experimental situation

The present study compares behavioral changes between two distinct rodent groups, hamsters (Mesocricetus auratus) and Wistar rats, when submitted in the same homogeneous experimental situations to a serial conditional discrimination procedure which involves water deprivation and the processing of temporal variables. Both hamsters and rats acquired serial positive conditional discrimination as indicated by higher frequencies of magazine-oriented behavior during the tone followed by reinforcement (T+) and preceded by the feature stimulus light (L) and during the empty interval, than during the tone alone not followed by reinforcement (T-). Rats' frequencies of magazine-oriented behavior were high during T+ and T-, initially during training, and decreased during T- as the training progressed. However, the hamsters' frequencies of magazine-oriented behavior started very low and increased only during T+ as the training progressed. Comparison of the frequencies of magazine-oriented behavior during the empty interval in relation to the frequencies during the preceding L period showed that rats' frequencies remained very high and hamsters' frequencies increased during training. These results suggest that rats and hamsters have different behavioral strategies for the acquisition of a conditional discrimination. The results of the comparisons made in these experiments support the view of the importance of an ecological psychology approach to the understanding of complex learning in animals.

Time Varying Conditional Correlation Between Stock And Bond Returns: Evidence From CIVETS Nations

This thesis estimates long-run time variant conditional correlation between stock and bond returns of CIVETS (Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa) nations. Further, aims to analyse the presence of asymmetric volatility effect in both asset returns, as well as, obverses increment or decrement in conditional correlation during pre-crisis and crisis period, which lead to make a reliable diversification decision. The Constant Conditional Correlation (CCC) GARCH model of Bollerslev (1990), the Dynamic Conditional Correlation (DCC) GARCH model (Engle 2002), and the Asymmetric Dynamic Conditional Correlation (ADCC) GARCH model of Cappiello, Engle, and Sheppard (2006) were implemented in the study. The analyses present strong evidence of time-varying conditional correlation in CIVETS markets, excluding Vietnam, during 2005-2013. In addition, negative innovation effects were found in both conditional variance and correlation of the asset returns. The results of this study recommend investors to include financial assets from these markets in portfolios, in order to obtain better stock-bond diversification benefits, especially during high volatility periods.

Russian and Eastern European Dynamic Conditional Correlations in light of the Russian crisis of 2014-2015

This thesis studies the impact of the latest Russian crisis on global markets, and especially Central and Eastern Europe. The results are compared to other shocks and crises over the last twenty years to see how significant they have been. The cointegration process of Central and Eastern European financial markets is also reviewed and updated. Using three separate conditional correlation GARCH models, the latest crisis is not found to have initiated similar surges in conditional correlations to previous crises over the last two decades. Market cointegration for Central and Eastern Europe is found to have stalled somewhat after initial correlation increases post EU accession.

Operant serial feature-positive conditional discrimination with composite features and different topography responses

In a serial feature-positive conditional discrimination procedure the properties of a target stimulus A are defined by the presence or not of a feature stimulus X preceding it. In the present experiment, composite features preceded targets associated with two different topography operant responses (right and left bar pressing); matching and non-matching-to-sample arrangements were also used. Five water-deprived Wistar rats were trained in 6 different trials: X-R®Ar and X-L®Al, in which X and A were same modality visual stimuli and the reinforcement was contingent to pressing either the right (r) or left (l) bar that had the light on during the feature (matching-to-sample); Y-R®Bl and Y-L®Br, in which Y and B were same modality auditory stimuli and the reinforcement was contingent to pressing the bar that had the light off during the feature (non-matching-to-sample); A- and B- alone. After 100 training sessions, the animals were submitted to transfer tests with the targets used plus a new one (auditory click). Average percentages of stimuli with a response were measured. Acquisition occurred completely only for Y-L®Br+; however, complex associations were established along training. Transfer was not complete during the tests since concurrent effects of extinction and response generalization also occurred. Results suggest the use of both simple conditioning and configurational strategies, favoring the most recent theories of conditional discrimination learning. The implications of the use of complex arrangements for discussing these theories are considered.

Variance reduction properties of dynamic hedging models - empirical evidence from BRICS countries

This Master’s Thesis analyses the effectiveness of different hedging models on BRICS (Brazil, Russia, India, China, and South Africa) countries. Hedging performance is examined by comparing two different dynamic hedging models to conventional OLS regression based model. The dynamic hedging models being employed are Constant Conditional Correlation (CCC) GARCH(1,1) and Dynamic Conditional Correlation (DCC) GARCH(1,1) with Student’s t-distribution. In order to capture the period of both Great Moderation and the latest financial crisis, the sample period extends from 2003 to 2014. To determine whether dynamic models outperform the conventional one, the reduction of portfolio variance for in-sample data with contemporaneous hedge ratios is first determined and then the holding period of the portfolios is extended to one and two days. In addition, the accuracy of hedge ratio forecasts is examined on the basis of out-of-sample variance reduction. The results are mixed and suggest that dynamic hedging models may not provide enough benefits to justify harder estimation and daily portfolio adjustment. In this sense, the results are consistent with the existing literature.

Tests of Conditional Asset Pricing Models in the Brazilian Stock Market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the 1976-1992 period. We also test a conditional APT model by using the difference between the 30-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. The conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from a total of 25 securities exchanged on the Brazilian markets. The inclusion of this second factor proves to be crucial for the appropriate pricing of the portfolios.

Tests of Conditional Asset Pricing Models in the Brazilian Stock Market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the 1976-1992 period. We also test a conditional APT model by using the difference between the 30-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. the conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from a total of 25 securities exchanged on the Brazilian markets. the inclusion of this second factor proves to be crucial for the appropriate pricing of the portfolios.

Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We establish the asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.d.s. errors subject to possible conditional heteroskedasticity of unknown form. These proposals are the fixed-design wild bootstrap, the recursive-design wild bootstrap and the pairwise bootstrap. In a simulation study all three procedures tend to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors. In contrast, standard residual-based bootstrap methods for models with i.i.d. errors may be very inaccurate if the i.i.d. assumption is violated. We conclude that in many empirical applications the proposed robust bootstrap procedures should routinely replace conventional bootstrap procedures for autoregressions based on the i.i.d. error assumption.

Estimation of State Space Models and Stochastic Volatility

Ma thèse est composée de trois chapitres reliés à l'estimation des modèles espace-état et volatilité stochastique. Dans le première article, nous développons une procédure de lissage de l'état, avec efficacité computationnelle, dans un modèle espace-état linéaire et gaussien. Nous montrons comment exploiter la structure particulière des modèles espace-état pour tirer les états latents efficacement. Nous analysons l'efficacité computationnelle des méthodes basées sur le filtre de Kalman, l'algorithme facteur de Cholesky et notre nouvelle méthode utilisant le compte d'opérations et d'expériences de calcul. Nous montrons que pour de nombreux cas importants, notre méthode est plus efficace. Les gains sont particulièrement grands pour les cas où la dimension des variables observées est grande ou dans les cas où il faut faire des tirages répétés des états pour les mêmes valeurs de paramètres. Comme application, on considère un modèle multivarié de Poisson avec le temps des intensités variables, lequel est utilisé pour analyser le compte de données des transactions sur les marchés financières. Dans le deuxième chapitre, nous proposons une nouvelle technique pour analyser des modèles multivariés à volatilité stochastique. La méthode proposée est basée sur le tirage efficace de la volatilité de son densité conditionnelle sachant les paramètres et les données. Notre méthodologie s'applique aux modèles avec plusieurs types de dépendance dans la coupe transversale. Nous pouvons modeler des matrices de corrélation conditionnelles variant dans le temps en incorporant des facteurs dans l'équation de rendements, où les facteurs sont des processus de volatilité stochastique indépendants. Nous pouvons incorporer des copules pour permettre la dépendance conditionnelle des rendements sachant la volatilité, permettant avoir différent lois marginaux de Student avec des degrés de liberté spécifiques pour capturer l'hétérogénéité des rendements. On tire la volatilité comme un bloc dans la dimension du temps et un à la fois dans la dimension de la coupe transversale. Nous appliquons la méthode introduite par McCausland (2012) pour obtenir une bonne approximation de la distribution conditionnelle à posteriori de la volatilité d'un rendement sachant les volatilités d'autres rendements, les paramètres et les corrélations dynamiques. Le modèle est évalué en utilisant des données réelles pour dix taux de change. Nous rapportons des résultats pour des modèles univariés de volatilité stochastique et deux modèles multivariés. Dans le troisième chapitre, nous évaluons l'information contribuée par des variations de volatilite réalisée à l'évaluation et prévision de la volatilité quand des prix sont mesurés avec et sans erreur. Nous utilisons de modèles de volatilité stochastique. Nous considérons le point de vue d'un investisseur pour qui la volatilité est une variable latent inconnu et la volatilité réalisée est une quantité d'échantillon qui contient des informations sur lui. Nous employons des méthodes bayésiennes de Monte Carlo par chaîne de Markov pour estimer les modèles, qui permettent la formulation, non seulement des densités a posteriori de la volatilité, mais aussi les densités prédictives de la volatilité future. Nous comparons les prévisions de volatilité et les taux de succès des prévisions qui emploient et n'emploient pas l'information contenue dans la volatilité réalisée. Cette approche se distingue de celles existantes dans la littérature empirique en ce sens que ces dernières se limitent le plus souvent à documenter la capacité de la volatilité réalisée à se prévoir à elle-même. Nous présentons des applications empiriques en utilisant les rendements journaliers des indices et de taux de change. Les différents modèles concurrents sont appliqués à la seconde moitié de 2008, une période marquante dans la récente crise financière.

Conditional Expected Utility

Let 'epsilon' be a class of event. Conditionally Expected Utility decision makers are decision makers whose conditional preferences ≿E, E є 'epsilon', satisfy the axioms of Subjective Expected Utility theory (SEU). We extend the notion of unconditional preference that is conditionally EU to unconditional preferences that are not necessarily SEU. We give a representation theorem for a class of such preferences, and show that they are Invariant Bi-separable in the sense of Ghirardato et al.[7]. Then, we consider the special case where the unconditional preference is itself SEU, and compare our results with those of Fishburn [6].