Submitted by Eduardo Augusto Aun (auneduardo@gmail.com) on 2013-01-16T19:48:06Z No. of bitstreams: 1 DISSERTAÇÃO EDUARDO A AUN 2012 MPFE FGV.pdf: 1487732 bytes, checksum: 0fa68b5193a17357238c3ff083146e3d (MD5) / Approved for entry into archive by Eliene Soares da Silva (eliene.silva@fgv.br) on 2013-01-16T19:49:13Z (GMT) No. of bitstreams: 1 DISSERTAÇÃO EDUARDO A AUN 2012 MPFE FGV.pdf: 1487732 bytes, checksum: 0fa68b5193a17357238c3ff083146e3d (MD5) / Made available in DSpace on 2013-01-16T19:55:33Z (GMT). No. of bitstreams: 1 DISSERTAÇÃO EDUARDO A AUN 2012 MPFE FGV.pdf: 1487732 bytes, checksum: 0fa68b5193a17357238c3ff083146e3d (MD5) Previous issue date: 2012-12-18 / Este estudo compara previsões de volatilidade de sete ações negociadas na Bovespa usando 02 diferentes modelos de volatilidade realizada e 03 de volatilidade condicional. A intenção é encontrar evidências empíricas quanto à diferença de resultados que são alcançados quando se usa modelos de volatilidade realizada e de volatilidade condicional para prever a volatilidade de ações no Brasil. O período analisado vai de 01 de Novembro de 2007 a 30 de Março de 2011. A amostra inclui dados intradiários de 5 minutos. Os estimadores de volatilidade realizada que serão considerados neste estudo são o Bi-Power Variation (BPVar), desenvolvido por Barndorff-Nielsen e Shephard (2004b), e o Realized Outlyingness Weighted Variation (ROWVar), proposto por Boudt, Croux e Laurent (2008a). Ambos são estimadores não paramétricos, e são robustos a jumps. As previsões de volatilidade realizada foram feitas através de modelos autoregressivos estimados para cada ação sobre as séries de volatilidade estimadas. Os modelos de variância condicional considerados aqui serão o GARCH(1,1), o GJR (1,1), que tem assimetrias em sua construção, e o FIGARCH-CHUNG (1,d,1), que tem memória longa. A amostra foi divida em duas; uma para o período de estimação de 01 de Novembro de 2007 a 30 de Dezembro de 2010 (779 dias de negociação) e uma para o período de validação de 03 de Janeiro de 2011 a 31 de Março de 2011 (61 dias de negociação). As previsões fora da amostra foram feitas para 1 dia a frente, e os modelos foram reestimados a cada passo, incluindo uma variável a mais na amostra depois de cada previsão. As previsões serão comparadas através do teste Diebold-Mariano e através de regressões da variância ex-post contra uma constante e a previsão. Além disto, o estudo também apresentará algumas estatísticas descritivas sobre as séries de volatilidade estimadas e sobre os erros de previsão. / This study compares volatility forecasts of seven publicly traded companies using 2 different models of realized volatility and 3 models of conditional volatility. The intention is to find empirical evidence as to the difference in results that are achieved when using models of realized volatility and conditional volatility to predict the volatility of shares in Brazil. The sample period runs from 1 November 2007 to 30 March 2011. The sample includes 5 minutes intraday data. The realized volatility estimators that are considered in this study are the Bi-Power Variation (BPVar) developed by Barndorff-Nielsen and Shephard (2004b), and Weighted Realized Outlyingness Variation (ROWVar) proposed by Boudt, Croux and Laurent (2008a) . Both estimators are non-parametric, and are robust to jumps. The realized volatility forecasts were made by autoregressive models estimated for each share on the estimated volatility series. The conditional variance models considered here are the GARCH (1,1), the GJR (1,1), having asymmetries in its construction, and FIGARCH-CHUNG (1, d 1), having long memory. The sample was divided into two, one for the estimation period from 01 November 2007 to 30 December 2010 (779 trading days) and one for the validation period of 03 January 2011 to 31 March 2011 (61 trading days). The out of sample forecasts were made to 1 day ahead, and the models were reestimated at each step, including one more variable in the sample after each prediction. The predictions will be compared using the Diebold-Mariano test and through regressions of the variance ex-post against a constant and the prediction. Moreover, the study also shows some descriptive statistics on the estimated volatility series and on the forecasting errors.

Dans cette thèse, nous considérons deux types d'application des effets de rétroaction en finance. Ces effets entrent en jeu quand des participants de marché exécutent des séquences de transactions ou prennent part à des réactions en chaîne, ce qui engendre des pics d'activité. La première partie présente un modèle d'exécution optimale dynamique en présence d'un flux stochastique et exogène d'ordres de marché. Nous partons du modèle de référence d'Obizheva et Wang, qui définit un cadre d'exécution optimale avec un impact de prix mixte. Nous y ajoutons un flux d'ordres modélisé à l'aide de processus de Hawkes, qui sont des processus à sauts présentant une propriété d'auto-excitation. A l'aide de la théorie du contrôle stochastique, nous déterminons la stratégie optimale de manière analytique. Puis nous déterminons les conditions d'existence de Stratégies de Manipulation de Prix, telles qu'introduites par Huberman et Stanzl. Ces stratégies peuvent être exclues si l'auto-excitation du flux d'ordres se compense exactement avec la résilience du prix. Dans un deuxième temps, nous proposons une méthode de calibration du modèle, que nous appliquons sur des données financières à haute fréquence issues de cours d'actions du CAC40. Sur ces données, nous trouvons que le modèle explique une partie non-négligeable de la variance des prix. Une évaluation de la stratégie optimale en backtest montre que celle-ci est profitable en moyenne, mais que des coûts de transaction réalistes suffisent à empêcher les manipulations de prix. Ensuite, dans la deuxième partie de la thèse, nous nous intéressons à la modélisation de la volatilité intra-journalière. Dans la littérature, la plupart des modèles de volatilité rétroactive se concentrent sur l'échelle de temps journalière, c'est-à-dire aux variations de prix d'un jour sur l'autre. L'objectif est ici d'étendre ce type d'approche à des échelles de temps plus courtes. Nous présentons d'abord un modèle de type ARCH ayant la particularité de prendre en compte séparément les contributions des rendements passés intra-journaliers et nocturnes. Une méthode de calibration de ce modèle est étudiée, ainsi qu'une interprétation qualitative des résultats sur des rendements d'actions américaines et européennes. Dans le chapitre suivant, nous réduisons encore l'échelle de temps considérée. Nous étudions un modèle de volatilité à haute fréquence, dont l'idée est de généraliser le cadre des processus Hawkes pour mieux reproduire certaines caractéristiques empiriques des marchés. Notamment, en introduisant des effets de rétroaction quadratiques inspirés du modèle à temps discret QARCH nous obtenons une distribution en loi puissance pour la volatilité ainsi que de l'asymétrie temporelle / In this thesis we study feedback effects in finance and we focus on two of their applications. These effects stem from the fact that traders split meta-orders sequentially, and also from feedback loops. Therefore, one can observe clusters of activity and periods of relative calm. The first part introduces an dynamic optimal execution framework with an exogenous stochastic flow of market orders. Our starting point is the well-known model of Obizheva and Wang which defines an execution framework with both permanent and transient price impacts. We modify the price model by adding an order flow based on Hawkes processes, which are self-exciting jump processes. The theory of stochastic control allows us to derive the optimal strategy as a closed formula. Also, we discuss the existence of Price Manipulations Strategies in the sense of Huberman and Stanzl which can be excluded from the model if the self-exciting property of the order flow exactly compensates the resilience of the price. The next chapter studies a calibration protocol for the model, which we apply to tick-by-tick data from CAC40 stocks. On this dataset, the model is found to explain a significant part of the variance of prices. We then evaluate the optimal strategy with a series of backtests, which show that it is profitable on average, although realistic transaction costs can prevent manipulation strategies. In the second part of the thesis, we turn to intra-day volatility modeling. Previous works from the volatility feedback literature mainly focus on the daily time scale, i.e. on close-to-close returns. Our goal is to use a similar approach on shorter time scales. We first present an ARCH-type model which accounts for the contributions of past intra-day and overnight returns separately. A calibration method for the model is considered, that we use on US and European stocks, and we provide some qualitative insights on the results. The last chapter of the thesis is dedicated to a high-frequency volatility model. We introduce a continuous-time analogue of the QARCH framework, which is also a generalization of Hawkes processes. This new model reproduces several important stylized facts, in particular it generates a time-asymmetric and fat-tailed volatility process

Volatility-managed portfolios offer mixed returns in an international setting based on ex-ante information. The results of this paper further strengthen the theory that the variability of excess returns from volatility-management are more dependent on underlying investor strategy rather than differences of global markets. We find that momentum strategies, as measured by the winners-minus-losers, are universally (except Japan) benefitted from volatility-management with an excess return between 6.96% and 14.28% annually across different regions/cross-sections garnered by the managed portfolio controlled against the Fama and French (2015) five-factor model. Value and profitability factors show mixed results with the beneficial performance in about half of the examined regions respectively. We prove that these relationships are robust through periods of market-wide crashes (Dotcom-bubble and financial crises of 2007/2008), tighter leverage constraints (≤1, ≤1.5) show however that the excess returns are dampened, concluding that access to leverage is a fundamental aspect of employing volatility-management to most portfolios. The results of this research paper expand previous literature of volatility-management by broadening the strategy to global markets.

Doctor of Philosophy / Department of Agricultural Economics / Ted C. Schroeder / Risk is an inevitable part of agricultural production and all producers face various forms of risk. Output price has been shown to be the major contributor to the risk in cattle feeding, yet few choose to manage this risk. This study used subjective price expectations and price distributions of survey participants to determine how producer's expectations compare with that of the market. In addition, demographic information gathered from survey participants allowed for further examination as to how these factors effect price outlook and variability. Data used for this study were gathered through survey responses from Kansas State University Extension meeting and workshop participants and other meetings targeted to livestock producers. First, data were aggregated and analyzed at a group level. Only two of the twelve price forecast were significantly lower than the futures settlement price. On the other hand, all but one of the aggregated group volatility expectations was different. Typically nearby contract price risk expectation was underestimated and distant contract price risk expectation was overestimated. Individual respondent's discreet stated price and price distribution information was fitted to a continuous distribution and an implied mean and standard deviation were determined. These were compared to market price and price risk data. Respondent's expectation of price was significantly lower than the market for distant months for five of the six groups. Individual volatilities resulting from each fitted distribution were significantly lower from the volatility measure resulting from Black's model. Demographic data were estimated to show the impact of this information on overall error of price forecast and price risk expectations. Those living outside the Northeast and Northern Plains tended to have larger error in their expectation of price volatility. Larger backgrounding operations reported lower price variance error and selling more fed cattle each year increased price risk expectation error. Lastly, prior use of risk management tools for the most part did not have an impact on error in either price expectation or price volatility expectation.

The indirect effect of atmospheric aerosol on climate remains a large source of uncertainty in anthropogenic climate change prediction. An important fraction of this uncertainty arises from the impacts of organic aerosol on cloud droplet formation. Conventional thinking says that organic aerosol hygroscopicity, typically represented by the hygroscopicity parameter κ, increases with oxidation, most commonly represented by the oxygen to carbon ratio of the aerosol, O:C. Furthermore, these quantities are expected to increase as aerosol volatility decreases. Results indicate that the link between organic aerosol hygroscopicity and oxidation is not always straightforward, and in some cases, the average carbon oxidation state OSc appears to be a better indicator of oxidation than the oxygen to carbon ratio, O:C. In chamber and ambient studies, the least volatile fraction of the aerosol also appeared to be the least hygroscopic, contradictory to current thinking; however, in both cases, thermally-denuded aerosol showed greater oxidation, in terms of OSc, than non-denuded aerosol. When these findings are placed in the context of numerous published studies from a variety of different environment, the overall trend of increasing organic hygroscopicity with O:C still holds. This is also true for volatilized aerosol, though the magnitude of organic hygroscopicity is generally lower than that of non-denuded aerosol.

The purpose of this thesis is to investigate the financial volatility dynamics through the GARCH modelling framework. We use univariate and multivariate GARCH-type models enriched with long memory, asymmetries and power transformations. We study the financial time series volatility and co-volatility taking into account the structural breaks detected and focusing on the effects of the corresponding financial crisis events. We conclude to provide a complete framework for the analysis of volatility with major policy implications and benefits for the current risk management practices. We first investigate the volume-volatility link for different investor categories and orders, around the Asian crisis applying a univariate dual long memory model. Our analysis suggests that the behaviour of volatility depends upon volume, but also that the nature of this dependence varies with time and the source of volume. We further apply the vector AR-DCC-FIAPARCH and the UEDCC-AGARCH models to several stock indices daily returns, taking into account the structural breaks of the time series linked to major economic events including crisis shocks We find significant cross effects, time-varying shock and volatility spillovers, time-varying persistence in the conditional variances, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks and the power of returns that best fits the volatility pattern. We observe higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets, a continuous herding investors’ behaviour, as the in-crisis correlations remain high, and a higher level of correlations during the recent financial crisis than during the Asian. Finally, we study the High-frEquency-bAsed VolatilitY (HEAVY) models that combine daily returns with realised volatility. We enrich the HEAVY equations through the HYAPARCH formulation to propose the HYDAP-HEAVY (HYperbolic Double Asymmetric Power) and provide a complete framework to analyse the volatility process.

For the purpose of modelling and prediction of volatility, the family of Stochastic Volatility (SV) models is an alternative to the extensively used ARCH type models. SV models differ in their assumption that volatility itself follows a latent stochastic process. This reformulation of the volatility process makes however model estimation distinctly more complicated for the SV type models, which in this paper is conducted through Markov Chain Monte Carlo methods. The aim of this paper is to assess the standard SV model and the SV model assuming t-distributed errors and compare the results with their corresponding GARCH(1,1) counterpart. The data examined cover daily closing prices of the Swedish stock index OMXS30 for the period 2010-01-05 to 2016- 03-02. The evaluation show that both SV models outperform the two GARCH(1,1) models, where the SV model with assumed t-distributed error distribution give the smallest forecast errors.

No description available.

There is a large body of research that indicates that international equity markets co-move over time. This co-movement manifests in various instruments, ranging from equities and bonds to soft commodities. However, this co-movement is more prevalent over crisis periods and can be seen in returns and volatility transmission effects. The recent financial crisis demonstrated that no local market is immune to transmission effects from international markets. South African financial market participants, such as investors and policymakers, have a vested interest in understanding how the equity market in particular and the economy in general react to international financial crises. This study aims to contribute an improved understanding of how the South African equity market interacts with international equity markets, by identifying the degree of price and volatility transmission before, during, and after an international financial crisis. This was done by investigating the possibility of changes in price and volatility transmissions from the Asian financial crisis (1997–1998), the dotcom bubble (2000–2001) and the more recent subprime financial crisis (2007–2009). An Exponential Generalized Autoregressive Conditional Heteroskedasticity (E-GARCH) model was employed within the framework of an Aggregate Shock model. The results indicate that during the international financial crises studied, the JSE All Share Index was directly affected through contagion effects inherent in the returns of the originating crisis country. Volatility transmissions during international financial crises came directly from the originating crisis country. Finally, the FTSE 100 Index was the main exporter of price and volatility transmission to the JSE All Share Index. / Thesis (M.Com. (Risk management))--North-West University, Potchefstroom Campus, 2012

The thesis evaluates the benefit of allowing for long memory volatility dynamics in forecasts of the variance-covariance matrix for asset allocation. First, I compare the forecast performance of multivariate long memory conditional volatility models (the long memory EWMA, long memory EWMA-DCC, FIGARCH-DCC and Component GARCH-DCC models) with that of short memory conditional volatility models (the short memory EWMA and GARCH-DCC models), using the asset allocation framework of Engle and Colacito (2006). The research reports two main findings. First, for longer horizon forecasts, long memory volatility models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory volatility models. Second, the two parsimonious long memory EWMA models outperform the other models – both short memory and long memory – in a majority of cases across all forecast horizons. These results apply to both low and high dimensional covariance matrices with both low and high correlation assets, and are robust to the choice of estimation window. The research then evaluates the application of multivariate long memory conditional volatility models in dynamic asset allocation, applying the volatility timing procedure of Fleming et al. (2001). The research consistently identifies the economic gains from incorporating long memory volatility dynamics in investment decisions. Investors are willing to pay to switch from the static to the dynamic strategies, and especially from the short memory volatility timing to the long memory volatility timing strategies across both short and long investment horizons. Among the long memory conditional volatility models, the two parsimonious long memory EWMA models, again, generally produce the most superior portfolios. When transaction costs are taken into account, the gains from the daily rebalanced dynamic portfolios deteriorate; however, it is still worth implementing the dynamic strategies at lower rebalancing frequencies. The results are robust to estimation error in expected returns, the choice of risk aversion coefficients and the use of a long-only constraint. To control for estimation error in forecasts of the long memory high dimensional covariance matrix, the research develops a dynamic long memory factor (the Orthogonal Factor Long Memory, or OFLM) model by embedding the univariate long memory EWMA model of Zumbach (2006) into an orthogonal factor structure. The factor-structured OFLM model is evaluated against the six above multivariate conditional volatility models in terms of forecast performance and economic benefits. The results suggest that the OFLM model generally produces impressive forecasts over both short and long forecast horizons. In the volatility timing framework, portfolios constructed with the OFLM model consistently dominate the static and other dynamic volatility timing portfolios in all rebalancing frequencies. Particularly, the outperformance of the factor-structured OFLM model to the fully estimated LM-EWMA model confirms the advantage of the factor structure in reducing estimation error. The factor structure also significantly reduces transaction costs, making the dynamic strategies more feasible in practice. The dynamic factor long memory volatility model also consistently produces more superior portfolios than those produced by the traditional unconditional factor and the dynamic factor short memory volatility models.