This report investigates the relationship between the yield volatility and the price volatility in the Swedish market. The method given in our report can be used to analyze any market with appropriate data set. We have used a time-series data of interest rate yield curves from Swedish government bonds. The curves are bootstrapped from the bills and bonds. The linear interpolation on these curves results in the nodes i.e. 1Y, 2Y,..., 10Y. We also need prices for instruments. A good choice is to use the synthetic government bonds namely SE GVB 2Y, SE GVB 5Y, and SE GVB 10Y. They are issued every day with maturity 2, 5, and 10 years. We also use the time-series of these bonds. These bonds have a yearly coupon of 6%. We can get zero-coupon values of these bonds by stripping their coupons using the interest rate yield curves. We have time-series data of zero-coupon prices with maturities 2, 5, and 10 years and time-series data of interest rates with the same tenors. We can use our data to calculate their respective volatilities to investigate how they are related to each other.

Agency theory suggests that information asymmetry between mutual fund managers and mutual fund investors can be mitigated if managers are compensated for the private information that influences mutual fund risk and performance. This study investigates the role of active management in influencing returns and return volatility of mutual funds. Chapter 1 investigates whether real estate mutual funds (REMFs) outperform Carhart’s (1997) four-factor and index benchmarks using daily return data from the CRSP survivorship bias-free mutual fund database from September 1998 to December 2013. We employ generalized autoregressive conditionally heteroscedastic (GARCH) volatility models to estimate more precise alphas than those generated in the extant studies. We document that risk-adjusted alphas of actively managed REMFs are statistically and economically significant, reflecting the informational advantage and skills of active managers. We also show that actively managed REMFs outperform the real estate index benchmark (Ziman Real Estate Index) and generate a yearly buy-and-hold abnormal return of 3.64%. Active management, therefore, provides value beyond the diversification benefits that can be generated by investing into the real estate index. While active managers of REMFs generate abnormal returns (gross of expenses), they capture the entire amount themselves, sharing none with investors (net of expenses). Accordingly, the average abnormal return to investors is close to zero due to expenses associated with REMFs, such as management fees, 12b-1 fees, waivers, and reimbursements. Finally, we find that passively managed REMFs do not generate abnormal risk-adjusted alphas in Carhart’s (1997) four-factor model. Chapter 2 examines managed volatility mutual funds (MVMFs) that utilize a range of investment strategies focused on portfolio volatility. These funds have increased in popularity in the wake of the financial crisis (December 2007 to June 2009) which introduced considerable volatility into the markets. We test whether MVMFs provide better performance during periods of recessions and expansions as compared to conventional mutual funds (MFs). We obtain several interesting results. First, MVMFs underperform compared to conventional MFs by more than 2% during the entire sample period. Second, MVMFs outperform conventional MFs in recessions by over 4% annually. Third, MVMFs underperform conventional MFs by more than 2.5% during expansions. Our results suggest that MVMFs can benefit investors during periods of recessions at the cost of performing worse during expansions. Chapter 3 studies MF return volatility patterns by testing a host of hypotheses for MFs with various style objectives. To conduct the tests, we use daily returns data from the CRSP survivorship bias-free mutual fund database from September 1998 to December 2013. We examine volatility patterns across the following nine styles: Passively Managed, Actively Managed, Sector, Capitalization, Growth and Income, Income, Growth, Hedged, and Dedicated Short Bias. We employ the exponential generalized autoregressive conditionally heteroscedastic (EGARCH) volatility model. Several results are obtained. First, we show that the financial crisis of 2007-2009 had a positive or a negative impact on volatility, depending on the investment style. Second, MF volatility behavior exhibits significant cluster effects in all styles, indicating that larger return shocks lead to greater increases in return volatility. Third, shock-persistence patterns differ across various MF styles with shocks to Dedicated Short Bias MFs being the least persistent and Capitalization and Growth and Income being the most persistent. Lastly, there is considerable negative asymmetry in MF return volatility changes in response to good and bad news in the sense that negative shocks to MF returns increase volatility more than positive shocks of the same magnitude for many Actively Managed MF styles. Significant negative asymmetry of this type makes the industry vulnerable to market downturns and should be addressed by regulators, MF managers, and investors. / Business Administration/Finance

Reporting regarding issues that are related to Corporate Social Responsibility have come into more and more focus lately. Most countries currently have a limited or no mandatory regulations regarding what should be included in either an annual report or in a stand-alone report in terms of CSR. However Denmark is one of the pioneers regarding mandatory CSR regulations and as such has certain rules and regulations that their companies have to follow. Even if today’s regulations are heavily focused on financial information that companies have to disclose there also exists regulations regarding non-financial information. As with the financial crisis that occurred in the early 21st century that led to stricter disclosures requirements for financial information we see a need for regulating non-financial information and especially CSR information. We have been able to see that some companies have been able to manipulate their CSR report so as to put themselves in a good light. Therefore the question arises if mandatory CSR disclosure will have any influence on the stock market.The purpose of this study was to examine if Swedish companies and the Swedish stock market could benefit from having mandatory CSR regulations, similar to those that exist in Denmark. We sought to examine if fulfilling certain amount of CSR criteria would reduce the volatility of a company’s stock price.In order for us to achieve the purpose of our research we had to conduct an experiment on the Swedish companies. In order for us to conduct the experiment we firstly had to select what type of research we would conduct and what type of research was most suitable for our research. In order for us to achieve an answer to our research question and to be able to fulfill the purpose of our research we decided to conduct a quantitative research. We have chosen to utilize the quantitative research approach as this would allow gathering sufficient data from existing databases and reports. The database that we chose to utilize in order for us to find our sample population was NASDAQ OMX Nordic where the companies had be listed as of 2015-03-31 as well as having financial data for the entire year of 2014, meaning between 2014-01-01 and 2014-12-31. NASDAQ OMX Nordic was also used in order for us to find market indexes. In order for us to able to answer our research question we developed three different hypotheses based on our theoretical framework that would later be tested.From the testing of our hypotheses we could determine that there is a relationship between the amount of CSR that a company reports, in terms of how many of our CSR criteria they fulfill, and the historical volatility of the company’s stock price. We were also able to determine that there exists a relationship between the amount of CSR that a company reports and the level of Beta that a company has. This implied that the Swedish stock market could benefit from mandatory CSR regulation as it would reduce the volatility which would also be beneficial for the company’s different stakeholders.

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

In this work, neural networks are used to forecast daily Realized Volatility of the EUR/USD, GBP/USD and USD/CHF currency pairs time series. Their performan-ce is benchmarked against nowadays popular Hetero-genous Autoregressive model of Realized Volatility (HAR) and traditional ARIMA models. As a by-product of our research, we introduce a simple yet effective enhancement to HAR model, naming the new model HARD extension. Forecasting performance tests of HARD model are conducted as well, promoting it to become a reference benchmark for neural networks and ARIMA.

No description available.

Financial time series studies indicate that the lognormal assumption for the return of an underlying security is often violated in practice. This is due to the presence of time-varying volatility in the return series. The most common departures are due to a fat left-tail of the return distribution, volatility clustering or persistence, and asymmetry of the volatility. To account for these characteristics of time-varying volatility, many volatility models have been proposed and studied in the financial time series literature. Two main conditional-variance model specifications are the autoregressive conditional heteroscedasticity (ARCH) and the stochastic volatility (SV) models. The SV model, proposed by Taylor (1986), is a useful alternative to the ARCH family (Engle (1982)). It incorporates time-dependency of the volatility through a latent process, which is an autoregressive model of order 1 (AR(1)), and successfully accounts for the stylized facts of the return series implied by the characteristics of time-varying volatility. In this thesis, we review both ARCH and SV models but focus on the SV model and its variations. We consider two modified SV models. One is an autoregressive process with stochastic volatility errors (AR--SV) and the other is the Markov regime switching stochastic volatility (MSSV) model. The AR--SV model consists of two AR processes. The conditional mean process is an AR(p) model , and the conditional variance process is an AR(1) model. One notable advantage of the AR--SV model is that it better captures volatility persistence by considering the AR structure in the conditional mean process. The MSSV model consists of the SV model and a discrete Markov process. In this model, the volatility can switch from a low level to a high level at random points in time, and this feature better captures the volatility movement. We study the moment properties and the likelihood functions associated with these models. In spite of the simple structure of the SV models, it is not easy to estimate parameters by conventional estimation methods such as maximum likelihood estimation (MLE) or the Bayesian method because of the presence of the latent log-variance process. Of the various estimation methods proposed in the SV model literature, we consider the simulated maximum likelihood (SML) method with the efficient importance sampling (EIS) technique, one of the most efficient estimation methods for SV models. In particular, the EIS technique is applied in the SML to reduce the MC sampling error. It increases the accuracy of the estimates by determining an importance function with a conditional density function of the latent log variance at time t given the latent log variance and the return at time t-1. Initially we perform an empirical study to compare the estimation of the SV model using the SML method with EIS and the Markov chain Monte Carlo (MCMC) method with Gibbs sampling. We conclude that SML has a slight edge over MCMC. We then introduce the SML approach in the AR--SV models and study the performance of the estimation method through simulation studies and real-data analysis. In the analysis, we use the AIC and BIC criteria to determine the order of the AR process and perform model diagnostics for the goodness of fit. In addition, we introduce the MSSV models and extend the SML approach with EIS to estimate this new model. Simulation studies and empirical studies with several return series indicate that this model is reasonable when there is a possibility of volatility switching at random time points. Based on our analysis, the modified SV, AR--SV, and MSSV models capture the stylized facts of financial return series reasonably well, and the SML estimation method with the EIS technique works very well in the models and the cases considered.

Financial time series studies indicate that the lognormal assumption for the return of an underlying security is often violated in practice. This is due to the presence of time-varying volatility in the return series. The most common departures are due to a fat left-tail of the return distribution, volatility clustering or persistence, and asymmetry of the volatility. To account for these characteristics of time-varying volatility, many volatility models have been proposed and studied in the financial time series literature. Two main conditional-variance model specifications are the autoregressive conditional heteroscedasticity (ARCH) and the stochastic volatility (SV) models. The SV model, proposed by Taylor (1986), is a useful alternative to the ARCH family (Engle (1982)). It incorporates time-dependency of the volatility through a latent process, which is an autoregressive model of order 1 (AR(1)), and successfully accounts for the stylized facts of the return series implied by the characteristics of time-varying volatility. In this thesis, we review both ARCH and SV models but focus on the SV model and its variations. We consider two modified SV models. One is an autoregressive process with stochastic volatility errors (AR--SV) and the other is the Markov regime switching stochastic volatility (MSSV) model. The AR--SV model consists of two AR processes. The conditional mean process is an AR(p) model , and the conditional variance process is an AR(1) model. One notable advantage of the AR--SV model is that it better captures volatility persistence by considering the AR structure in the conditional mean process. The MSSV model consists of the SV model and a discrete Markov process. In this model, the volatility can switch from a low level to a high level at random points in time, and this feature better captures the volatility movement. We study the moment properties and the likelihood functions associated with these models. In spite of the simple structure of the SV models, it is not easy to estimate parameters by conventional estimation methods such as maximum likelihood estimation (MLE) or the Bayesian method because of the presence of the latent log-variance process. Of the various estimation methods proposed in the SV model literature, we consider the simulated maximum likelihood (SML) method with the efficient importance sampling (EIS) technique, one of the most efficient estimation methods for SV models. In particular, the EIS technique is applied in the SML to reduce the MC sampling error. It increases the accuracy of the estimates by determining an importance function with a conditional density function of the latent log variance at time t given the latent log variance and the return at time t-1. Initially we perform an empirical study to compare the estimation of the SV model using the SML method with EIS and the Markov chain Monte Carlo (MCMC) method with Gibbs sampling. We conclude that SML has a slight edge over MCMC. We then introduce the SML approach in the AR--SV models and study the performance of the estimation method through simulation studies and real-data analysis. In the analysis, we use the AIC and BIC criteria to determine the order of the AR process and perform model diagnostics for the goodness of fit. In addition, we introduce the MSSV models and extend the SML approach with EIS to estimate this new model. Simulation studies and empirical studies with several return series indicate that this model is reasonable when there is a possibility of volatility switching at random time points. Based on our analysis, the modified SV, AR--SV, and MSSV models capture the stylized facts of financial return series reasonably well, and the SML estimation method with the EIS technique works very well in the models and the cases considered.

[pt] O presente trabalho busca explorar a previsibilidade na dinâmica temporal em modelos lineares de superfícies de volatilidade implícita estimados para opções de compra de ações brasileiras. Resultados de estudos anteriores, sob a abordagem usualmente empregada de estimação de modelos lineares em função do preço de exercício e do tempo até o vencimento a partir de dados de corte transversal sobre cada contrato disponível em dado instante, como Dumas, Fleming e Whaley (1998), revelam grande instabilidade nos coeficientes estimados ao longo do tempo. Por conseguinte, a incapacidade desta perspectiva em descrever a dinâmica intertemporal da estrutura, contrariando a observação empírica de volatilidade variável no tempo. A partir destas evidências e das conclusões de Heston e Nandi (2000), que reportaram significativa dependência da trajetória para a volatilidade dos retornos do índice S&P 500, Gonçalves e Guidolim (2006), propuseram um modelo em dois estágios, que aplica vetores autoregressivos para capturar a presença de variação temporal dos coeficientes de um modelo linear. A contribuição deste trabalho está em aplicar o Modelo proposto à realidade do mercado brasileiro de opções de ações, incipiente em liquidez e horizonte de negociação se comparado ao mercado norte americano, adaptando critérios a fim de validar sua aplicabilidade neste contexto em termos estatísticos e econômicos. Os resultados comprovam a superioridade desta abordagem em relação a outras comparáveis na literatura, mas não a capacidade de gerar retornos acima da média na presença de custos de transação contra a referência natural da taxa livre de risco. O que sugere a adequação à hipótese de eficiência de mercado. / [en] O The present study aims to explore predictability in temporal dynamics regarding linear models of the implied volatility surfaces estimated for Brazilian stocks options. Previous results, by usual approach of fitting linear models linking implied volatility to time to maturity and moneyness, available for each cross-section of option contracts at a point in time, as in Dumas, Fleming and Whaley (1998), suggest that estimated parameters of such models are highly unstable over time. Therefore, this approach isn t capable of replicating various IVS s shapes, contrary to the empirical evidence of implied volatility varying with options strike price and date of expiration. Based on these evidences and in Heston and Nandi (2000), that exploit the information on path-dependency in volatility contained in the spot S&P 500 index, Gonçalves e Guidolim (2006) proposed a two-stage approach to modeling and forecasting the S&P 500 index options IVS. In the second-stage they model the dynamics of the cross-sectional first-stage coefficients by means of vector autoregression models. The contribution of this work is to apply the proposed model to the reality of the Brazilian stock options, incipient in terms of liquidity and trading horizon dimensions when compared to the U.S. market, adapting criterians to validate its applicability in this context in statistical and economical sense. The results demonstrate the superiority of this approach over comparable literature, but not the ability to generate abnormal profits in the presence of transaction costs in excess of the benchmark of the risk-free rate. This indicates adaptation to the market efficiency hypothesis.

Submitted by Marcia Bacha (marcia.bacha@fgv.br) on 2012-03-07T12:45:08Z No. of bitstreams: 1 20120306084421880.pdf: 1716342 bytes, checksum: e7f9f7df4b67ff4e12f57770620942d8 (MD5) / Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2012-03-07T12:50:42Z (GMT) No. of bitstreams: 1 20120306084421880.pdf: 1716342 bytes, checksum: e7f9f7df4b67ff4e12f57770620942d8 (MD5) / Made available in DSpace on 2012-03-07T12:51:26Z (GMT). No. of bitstreams: 1 20120306084421880.pdf: 1716342 bytes, checksum: e7f9f7df4b67ff4e12f57770620942d8 (MD5) / Com origem no setor imobiliário americano, a crise de crédito de 2008 gerou grandes perdas nos mercados ao redor do mundo. O mês de outubro do mesmo ano concentrou a maior parte da turbulência, apresentando também uma explosão na volatilidade. Em meados de 2006 e 2007, o VIX, um índice de volatilidade implícita das opções do S&P500, registrou uma elevação de patamar, sinalizando o possível desequilíbrio existente no mercado americano. Esta dissertação analisa se o consenso de que a volatilidade implícita é a melhor previsora da volatilidade futura permanece durante o período de crise. Os resultados indicam que o VIX perde poder explicativo ao se passar do período sem crise para o de crise, sendo ultrapassado pela volatilidade realizada. / Started in the U.S. housing sector, the credit crisis of 2008 caused great damage in markets around the world. The effects were concentrated in October of the same year, which also showed an explosion in volatility. In mid-2006 and mid-2007, the VIX, an index of implied volatility of options on the S&P500, recorded a rise in level signaling the possible imbalance in the U.S. market. This dissertation examines whether the consensus that implied volatility is the best predictor of future volatility remains during the crisis. The results indicate that the VIX loses explanatory power to move from a period of economic stability for a period of crisis, been surpassed by the realized volatility.