Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-08-28T13:57:07Z No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-06T13:18:03Z (GMT) No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-06T13:18:12Z (GMT) No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) / Made available in DSpace on 2017-09-06T13:28:02Z (GMT). No. of bitstreams: 1 DissNLO.pdf: 2139734 bytes, checksum: 9d9000013e5ab1fd3e860be06fc72737 (MD5) Previous issue date: 2017-06-01 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / We present two different approaches to obtain a probability density function for the stock?s future price: a predictive distribution, based on a Bayesian time series model, and the implied distribution, based on Black & Scholes option pricing formula. Considering the Black & Scholes model, we derive the necessary conditions to obtain the implied distribution of the stock price on the exercise date. Based on predictive densities, we compare the market implied model (Black & Scholes) with a historical based approach (Bayesian time series model). After obtaining the density functions, it is simple to evaluate probabilities of one being bigger than the other and to make a decision of selling/buying a stock. Also, as an example, we present how to use these distributions to build an option pricing formula. / Apresentamos duas abordagens para obter uma densidade de probabilidades para o preço futuro de um ativo: uma densidade preditiva, baseada em um modelo Bayesiano para série de tempo e uma densidade implícita, baseada na fórmula de precificação de opções de Black & Scholes. Considerando o modelo de Black & Scholes, derivamos as condições necessárias para obter a densidade implícita do preço do ativo na data de vencimento. Baseando-se nas densidades de previsão, comparamos o modelo implícito com a abordagem histórica do modelo Bayesiano. A partir destas densidades, calculamos probabilidades de ordem e tomamos decisões de vender/comprar um ativo. Como exemplo, apresentamos como utilizar estas distribuições para construir uma fórmula de precificação.

Apresentamos duas abordagens para obter uma densidade de probabilidades para o preço futuro de um ativo: uma densidade preditiva, baseada em um modelo Bayesiano para série de tempo e uma densidade implícita, baseada na fórmula de precificação de opções de Black & Scholes. Considerando o modelo de Black & Scholes, derivamos as condições necessárias para obter a densidade implícita do preço do ativo na data de vencimento. Baseando-­se nas densidades de previsão, comparamos o modelo implícito com a abordagem histórica do modelo Bayesiano. A partir destas densidades, calculamos probabilidades de ordem e tomamos decisões de vender/comprar um ativo. Como exemplo, apresentamos como utilizar estas distribuições para construir uma fórmula de precificação. / We present two different approaches to obtain a probability density function for the stocks future price: a predictive distribution, based on a Bayesian time series model, and the implied distribution, based on Black & Scholes option pricing formula. Considering the Black & Scholes model, we derive the necessary conditions to obtain the implied distribution of the stock price on the exercise date. Based on predictive densities, we compare the market implied model (Black & Scholes) with a historical based approach (Bayesian time series model). After obtaining the density functions, it is simple to evaluate probabilities of one being bigger than the other and to make a decision of selling/buying a stock. Also, as an example, we present how to use these distributions to build an option pricing formula.

Submitted by Paulo Guitti Fernandes Oliveira (pguitti@hotmail.com) on 2012-12-16T13:27:51Z No. of bitstreams: 1 Dissertacao_V_FINAL.pdf: 462199 bytes, checksum: 7596783f5352fad0a9bf47c47ac91300 (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2012-12-17T11:32:27Z (GMT) No. of bitstreams: 1 Dissertacao_V_FINAL.pdf: 462199 bytes, checksum: 7596783f5352fad0a9bf47c47ac91300 (MD5) / Made available in DSpace on 2012-12-17T11:33:16Z (GMT). No. of bitstreams: 1 Dissertacao_V_FINAL.pdf: 462199 bytes, checksum: 7596783f5352fad0a9bf47c47ac91300 (MD5) Previous issue date: 2012-12-06 / A identificação das principais variáveis que influenciam a volatilidade implícita das opções de juros (IDI) pode ser de grande valia para os agentes do mercado financeiro. Sendo assim, o presente trabalho procura determinar quais divulgações econômicas - dentre elas, as alterações das taxas de juros da economia brasileira pelo COPOM (Comitê de Política Monetária onde são tomadas as decisões sobre a nova taxa de juros básica), a divulgação de seus documentos oficiais de comunicação (Ata e Relatório Trimestral de Inflação), e até as surpresas de dados macroeconômicos, como a variação do PIB, a variação da produção industrial e das vendas no varejo - alteram de forma significativa a variável de estudo. Para isso, foi utilizado um teste de evento, considerando-se o período de análise de agosto de 2007 a maio de 2012, analisando as opções com vencimento em 126, 189 e 252 dias úteis, possuindo deltas de 25%, 50% e 75%. De todas as variáveis analisadas, a principal variável de destaque é a decisão do COPOM, que altera de forma significativa a volatilidade implícita dessas opções de juros. / Setting the main variables that influence the implied volatility of the interest rate options (IDI) could be of great value to financial market participants. Therefore, this study looks for determining which economic releases - among them, changes in interest rates of the Brazilian economy by COPOM (Monetary Policy Committee, responsible for interest rate decisions), the release of their official documents of communication (Minutes and Quarterly Inflation Report) and even the surprises of macroeconomic data, as GDP growth, industrial production and retail sales - significantly change the studied variable. The present study will use an event test, considering the period from August 2007 to May 2012, analyzing the options maturing in 126, 189 and 252 days, with deltas of 25%, 50% and 75%. Among all the variables analyzed, the most important variable is the COPOM decision, which significantly changes the implied volatility of these interest rate options.

Synthetic short positions constructed by equity options and stock loan short sells are linked by arbitrage. This thesis analyses the link by considering the implied volatility surface (IVS) at 80%, 100%, and 120% moneyness, and stock loan variables such as benchmark rate (rt), utilization, short interest, and transaction trends to inspect time-dependent structures between the two assets. By applying multiple multivariate time-series analyses in terms of vector autoregression (VAR) and the recurrent neural networks long short-term memory (LSTM) and gated recurrent units (GRU) with a sliding window methodology. This thesis discovers linear and complex relationships between the IVS and stock loan data. The three-day-ahead out-of-sample LSTM forecast of IV at 80% moneyness improved by including lagged values of rt and yielded 19.6% MAPE and forecasted correct direction 81.1% of samples. The corresponding 100% moneyness GRU forecast was also improved by including stock loan data, at 10.8% MAPE and correct directions for 60.0% of samples. The 120% moneyness VAR forecast did not improve with stock loan data at 26.5% MAPE and correct directions for 66.2% samples. The one-month-ahead rt VAR forecast improved by including a lagged IVS, at 25.5% MAPE and 63.6% correct directions. The presented data was optimal for each target variable, showing that the application of LSTM and GRU was justified. These results indicate that considering stock loan data when forecasting IVS for 80% and 100% moneyness is advised to gain exploitable insights for short-term positions. They are further validated since the different models yielded parallel inferences. Similar analysis with other equity is advised to gain insights into the relationship and improve such forecasts. / Syntetiska kortpositioner konstruerade av aktieoptioner och blankning med aktielån är kopplade med arbitrage. Denna tes analyserar kopplingen genom att överväga den implicerade volatilitetsytan vid 80%, 100% och 120% moneyness och aktielånvariabler såsom referensränta rt, låneutnyttjande, låneintresse, och transaktionstrender för att granska tidsberoende strukturer mellan de två tillgångarna. Genom att tillämpa multipel multidimensionell tidsserieanalys såsom vektorautoregression (VAR) och de rekursiva neurala nätverken long short-term memory (LSTM) och gated recurrent units (GRU). Tesen upptäcker linjära och komplexa samband mellan implicerade volatilitetsytor och aktielånedata. Tre dagars LSTM-prognos av implicerade volatiliteten vid 80% moneyness förbättrades genom att inkludera fördröjda värden av rt och gav 19,6% MAPE och prognostiserade korrekt riktning för 81,1% av prover. Motsvarande 100% moneyness GRU-prognos förbättrades också genom att inkludera aktielånedata, resulterande i 10,8% MAPE och korrekt riktning för 60,0% av prover. VAR-prognosen för 120% moneyness förbättrades inte med alternativa data på 26,5% MAPE och korrekt riktning för 66,2% av prover. En månads VAR-prognos för rt förbättrades genom att inkludera en fördröjd implicerad volatilitetsyta, resulterande i 25,5% MAPE och 63,6% korrekta riktningar. Presenterad statistik var optimala för dessa variabler, vilket visar att tillämpningen av LSTM och GRU var motiverad. Därav rekommenderas det att inkludera aktielånedata för prognostisering av implicerade volatilitetsytor för 80% och 100% moneyness, speciellt för kortsiktiga positioner. Resultaten valideras ytterligare eftersom de olika modellerna gav dylika slutsatser. Liknande analys med andra aktier är rekommenderat för att få insikter i förhållandet och förbättra sådana prognoser.

The implied volatility surface plays an important role for Front office and Risk Management functions at Nasdaq and other financial institutions which require mark-to-market of derivative books intraday in order to properly value their instruments and measure risk in trading activities. Based on the aforementioned business needs, being able to calibrate an end of day implied volatility surface based on new market information is a sought after trait. In this thesis a statistical learning approach is used to calibrate the implied volatility surface intraday. This is done by using OMXS30-2019 implied volatility surface data in combination with market information from close to at the money options and feeding it into 3 Machine Learning models. The models, including Feed Forward Neural Network, Recurrent Neural Network and Gaussian Process, were compared based on optimal input and data preprocessing steps. When comparing the best Machine Learning model to the benchmark the performance was similar, indicating that the calibration approach did not offer much improvement. However the calibrated models had a slightly lower spread and average error compared to the benchmark indicating that there is potential of using Machine Learning to calibrate the implied volatility surface. / Implicita volatilitetsytor är ett viktigt vektyg för front office- och riskhanteringsfunktioner hos Nasdaq och andra finansiella institut som behöver omvärdera deras portföljer bestående av derivat under dagen men också för att mäta risk i handeln. Baserat på ovannämnda affärsbehov är det eftertraktat att kunna kalibrera de implicita volatilitets ytorna som skapas i slutet av dagen nästkommande dag baserat på ny marknadsinformation. I denna uppsats används statistisk inlärning för att kalibrera dessa ytor. Detta görs genom att uttnytja historiska ytor från optioner i OMXS30 under 2019 i kombination med optioner nära at the money för att träna 3 Maskininlärnings modeller. Modellerna inkluderar Feed Forward Neural Network, Recurrent Neural Network och Gaussian Process som vidare jämfördes baserat på data som var bearbetat på olika sätt. Den bästa Maskinlärnings modellen jämfördes med ett basvärde som bestod av att använda föregående dags yta där resultatet inte innebar någon större förbättring. Samtidigt hade modellen en lägre spridning samt genomsnittligt fel i jämförelse med basvärdet som indikerar att det finns potential att använda Maskininlärning för att kalibrera dessa ytor.

Options are an important part in today's financial market. It's therefore of high importance to be able to understand when options are overvalued and undervalued to get a lead on the market. To determine this, the relation between the volatility of the underlying asset, called realized volatility, and the market's expected volatility, called implied volatility, can be analyzed. In this thesis five models were investigated for modeling the relation between implied and realized volatility. The five models consisted of one Ornstein–Uhlenbeck model, two autoregressive models and two artificial neural networks. To analyze the performance of the models, different accuracy measures were calculated for out-of-sample forecasts. Signals from the models were also calculated and used in a simulated options trading environment to get a better understanding of how well they perform in trading applications. The results suggest that artificial neural networks are able to model the relation more accurately compared to more traditional time series models. It was also shown that a trading strategy based on forecasting the relation was able to generate significant profits. Furthermore, it was shown that profits could be increased by combining a forecasting model with a signal classification model. / Optioner är en viktig del i dagens finansiella marknad. Det är därför viktigt att kunna förstå när optioner är över- och undervärderade för att vara i framkant av marknaden. För att bestämma detta kan relationen mellan den underliggande tillgångens volatilitet, kallad realiserad volatilitet, och marknadens förväntade volatilitet, kallad implicit volatilitet, analyseras. I den här avhandlingen undersöktes fem modeller för att modellera relationen mellan implicit och realiserad volatilitet. De fem modellerna var en Ornstein–Uhlenbeck modell, två autoregressiva modeller samt två artificiella neurala nätverk. För att analysera modellernas prestanda undersöktes olika nogrannhetsmått för prognoser från modellerna. Signaler från modellerna beräknades även och användes i en simulerad optionshandelsmiljö för att få en bättre förståelse för hur väl de presterar i en handelstillämpning. Resultaten tyder på att artificiella neurala nätverk kan modellera relationen bättre än mer traditionella tidsseriemodellerna. Det visades även att en handelsstrategi baserad på prognoser av relationen kunde generera en signifikant vinst. Det visades dessutom att vinster kunde ökas genom att kombinera en prognosmodell med en modell som klassificerar signaler.

[pt] Esta pesquisa investiga primeiramente, por meio de regressões, a relação entre as volatilidades implícitas das opções e os retornos futuros de 20, 40 e 60 dias das ações subjacentes no mercado acionário brasileiro. Essas regressões são então submetidas a testes de heterocedasticidade para garantir que não são regressões espúrias. Por fim, submetemos os resultados a um teste de robustez que confirma as regressões válidas e verifica a presença de autocorrelação nas séries de retornos futuros. O período analisado é de janeiro de 2011 a dezembro de 2021 em um total de onze anos completos. Apesar de apresentarem coeficientes de regressão significativos, nem todas essas regressões passam pelos testes, e sempre deve-se ter cautela ao usar uma volatilidade implícita de opção como sendo capaz de prever retornos das ações subjacentes no mercado brasileiro. / [en] This research first investigates, through regressions, the relationship between the implied volatilities of options and the future returns of 20, 40 and 60 days of the underlying stocks within the Brazilian stock market. These regressions are then subjected to heteroscedasticity tests to ensure that they are not spurious regressions. Finally, we submit the results to robustness tests to confirm the valid regressions and verify the presence of autocorrelation in the series of future returns. The period under analysis is from January 2011 to December 2021, totalling 11 years. Despite having significant regression coefficients, not all of these regressions pass the tests, and one should always exercise caution when using an option implied volatility as a predictor of underlying equity returns in the Brazilian market.

La tesi comprende tre saggi sul ruolo della volatilità implicita per le banche. La tesi è organizzata in tre capitoli. Capitolo I - studia il ruolo di skew e spread della volatilità implicita nel determinare i rendimenti delle azioni bancarie. Capitolo II - analizza gli effetti degli skew della volatilità implicita e della realized volatility sulla leva finanziaria delle banche. Capitolo III - si focalizza sul rapporto tra il coefficiente di liquidità delle banche e le misure per il rischio estratte dalla volatilità (skew, spread, realized volatility). / The thesis comprehends three essays on option implied volatility risk measures for banks. The thesis is organized in three chapters. Chapter I - studies the informational content for banks' stock returns in option's implied volatilities skews and spread. Chapter II - analyzes the effect of volatility risk measures (volatility skew and realized volatility) on banks' leverage. Chapter III - studies the relationship between banks' liquidity ratio and volatility risk measures.

本研究探討選擇權交易量之大小對於波動度預測之效率性所造成之對偶效果(dual effect)，驗證〝正常的高交易量〞與〝異常的高交易量〞對於波動度預測能力是否有不同的影響。本研究採用panel data之資料型態，以LIFFE上市的個股買權為對象，資料長度為三年左右。主要欲探討之假說為： 1.一般而言，交易量大的選擇權，其波動度估計誤差較交易量小的選擇權來得小。 2.相對於平日水準而言，某日交易量異常高的選擇權將有較大的波動度估計誤差。 本研究所使用的波動度預測模型為隱含波動度(ISD)，採用的是最接近到期月份及最接近價平的合約。實證以組合迴歸、固定效果模型、隨機效果模型分別估計之，加以比較。結果發現固定效果模型為較佳之解釋模型，然而結果顯示交易量的對偶效果並不明確影響波動度預測誤差，故推測有某種影響公司間差異的因素，即公司間之異質性，比相對交易量更容易影響波動度預測之誤差。另外，透過組間與組內效果之分析，發現不論是長期還是短期，由於公司間的異質性存在，使得相對交易量對於波動度預測誤差均無明顯影響。 / The purpose of this research is to study the dual effect on the efficiency of volatility forecasting which is caused by the volume of option market, with the intent to test whether〝normal high volume〞and〝abcdrmal high volume〞cause different results on the ability of volatility forecasting. The data used is in the form of panel data. It is drawn from LIFFE, and has a length of about three years. The hypotheses to be examined in this study are：1. High-average-volume options have smaller volatility forecasting errors than low-average-volume options; 2. Options have larger volatility forecasting errors on abcdrmally-high-volume days than on normal-volume days. In this research, volatility is forecasted by implied standard deviation (ISD) which is implied in the at-the-money and the nearest expiry month options. Pooled regression、fixed effect model、and random effect model methods were applied. The results show that the fixed effect model made the best analysis amongst the three models. However, the result does not support the hypotheses made above, which means that volume does not have much influence on volatility forecasting error. It is inferred that there exists some other factors which could cause the difference between firms, namely heterogeneity, and these factors have much more powerful influence over volatility forecasting error than volume. Finally, it was found that no matter for long run or short run, because of the existence of heterogeneity, relative volume doesn’t have obvious influence on volatility forecasting errors when analyzing the difference between the between-individual effect and the within-individual effect.

Les stratégies de volatilité ont connu un rapide essor suite à la crise financière de 2008. Or, les récentes performances catastrophiques de ces instruments indiciels ont remis en question leurs contributions en couverture de portefeuille. Mes travaux de thèse visent à repenser, réinventer la philosophie des stratégies de volatilité. Au travers d'une analyse empirique préliminaire reposant sur la théorie de l'utilité espérée, le chapitre 1 dresse le diagnostic des stratégies traditionnelles de volatilité basées sur la couverture de long-terme par la réplication passive de la volatilité implicite. Il montre que, bien que ce type de couverture bat la couverture traditionnelle, elle s'avère inappropriée pour des investisseurs peu averses au risque.Le chapitre 2 ouvre la voie à une nouvelle génération de stratégies de volatilité, actives, optionnelles et basées sur l'investissement factoriel. En effet, notre décomposition analytique et empirique du smile de volatilité implicite en primes de risque implicites, distinctes et investissables permet de monétiser de manière active le portage de risques d'ordres supérieurs. Ces primes de risques mesurent l'écart de valorisation entre les distributions neutres au risque et les distributions physiques.Enfin, le chapitre 3 compare notre approche investissement factoriel avec les stratégies de volatilité employées par les hedge funds. Notre essai montre que nos stratégies de primes de risque d'assurance sont des déterminants importants dans la performance des hedge funds, tant en analyse temporelle que cross-sectionnelle. Ainsi, nous mettons en évidence dans quelle mesure l'alpha provient en réalité de la vente de stratégies d'assurance contre le risque extrême. / Volatility strategies have flourished since the Great Financial Crisis in 2008. Nevertheless, the recent catastrophic performance of such exchange-traded products has put into question their contributions for portfolio hedging and diversification. My thesis work aims to rethink and reinvent the philosophy of volatility strategies.From a preliminary empirical study based on the expected utility theory, Chapter 1 makes a diagnostic of traditional volatility strategies, based on buy-and-hold investments and passive replication of implied volatility. It exhibits that, although such portfolio hedging significantly outperforms traditional hedging, it appears strongly inappropriate for risk-loving investors.Chapter 2 paves the way for a new generation of volatility strategies, active, option-based and factor-based investing. Indeed, our both analytical and empirical decomposition of implied volatility smiles into a combination of implied risk premia, distinct and tradeable, enables to harvest actively the compensation for bearing higher-order risks. These insurance risk premia measure the pricing discrepanciesbetween the risk-neutral and the physical probability distributions.Finally, Chapter 3 compares our factor-based investing approach to the strategies usually employed in the hedge fund universe. Our essay clearly evidences that our tail risk premia strategies are incremental determinants in the hedge fund performance, in both the time-series and the cross-section of returns. Hence, we exhibit to what extent hedge fund alpha actually arises from selling crash insurance strategies against tail risks.