This thesis studies realized volatility (RV), implied volatility (IV) and their applications in stochastic volatility models. The first essay uses both daytime and overnight high-frequency price data for equity index futures to estimate the RV of the S\&P500 and NASDAQ 100 indexes. Empirical results reveal strong inter-correlation between the regular-trading-time and after-hour RVs, as well as a significant predictive power of overnight RV on daytime RV and vice versa. We propose a new day-night realized stochastic volatility (DN-SV-RV) model, where the daytime and overnight returns are jointly modeled with their RVs, and their latent volatilities are correlated. The newly proposed DN-SV-RV model has the best out-of-sample return distribution forecasts among the models considered. The second essay extends the realized stochastic volatility model by jointly estimating return, RV and IV. We examine how RV and IV enhance the estimation of the latent volatility process for both the S\&P500 index and individual stocks. The third essay re-examines asymmetric stochastic volatility (ASV) models with different return-volatility correlation structures given RV and IV. We show by simulation that estimating the ASV models with return series alone may infer erroneous estimations of the correlation coefficients. The incorporation of volatility measures helps identify the true return-volatility correlation within the ASV framework. Empirical evidence on global equity market indices verifies that ASV models with additional volatility measures not only obtain significantly different estimations of the correlations compared to the benchmark ASV models, but also improve out-of-sample return forecasts. / Thesis / Doctor of Philosophy (PhD)

Models of the term structure of interest rates play a central role in the modern theory of pricing bonds and other interest rate claims. Term structure models based on the principle of no-arbitrage, especially those of the Heath-Jarrow-Morton (1992) class, have become very popular recently, both with academics and practitioners. Surprisingly however, although the implied volatility function plays a crucial role in these no-arbitrage term structure models, there is little systematic evidence to guide optimal model specification within this broad class. We study the implied volatility in the Heath-Jarrow-Morton framework using Eurodollar futures options data. We estimate a daily time series of forward rates within the HJM framework such that, by construction, the predicted futures prices from our model exactly match the observed futures prices. Next, we estimate a daily time series of volatility parameters such that the sum of squared errors between futures options prices predicted by the model and observed futures options prices is minimized. We use the six different volatility specifications suggested by Amin and Morton (1994) within the HJM class of models to price interest rate claims. Since the volatilities are the only unobservables, we use these models to infer the volatilities from the market prices of Eurodollar futures options over the 1987-1998 periods. The minimized sum of squared errors in the option prices is used as the measure of accuracy of each specific model. Each model differs from the others in its ability to match the market option prices and the time required for the computation. We compare the performances of the six volatility specifications in the accuracy-versus-computation time tradeoff. We document the systematic biases between the model and market prices as a function of option type, maturity, and moneyness. We also examine alternative numerical implementations of HJM models using the six volatility specifications. In particular, we analyze the impact on accuracy and computation time of using different numbers of time-steps. We also examine the effect of using time-steps of varying lengths within the same estimation procedure, and of ordering the time-steps in different ways. / Ph. D.

This study examines the relationship between company management guidance, and ex-ante crash risk over the duration of 2008(Jan 2006-Dec 2009) financial crisis using the implied volatility skew, which is based upon ex-ante volatility implied by the pricing model developed by Black-Scholes (1973). The study finds that over the duration of this crisis period, management guidance decreases with a rise in ex-ante crash risk. Further, the study provides evidence on the relationship of management guidance and earnings volatility, and how that is affected by a firm's industry product concentration based on the Herfindahl-Hirschman Index (HHI) score.

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.

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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.

Submitted by RICARDO MEDEIROS DOS SANTOS DA SILVA (rmedeiros@gmail.com) on 2015-09-21T23:50:40Z No. of bitstreams: 1 Implied Hazard Rates Analysis Through Brazilian Corporate Debt.pdf: 1429524 bytes, checksum: f2517a6c1a79a440b38d24a6957278bf (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2015-09-21T23:57:40Z (GMT) No. of bitstreams: 1 Implied Hazard Rates Analysis Through Brazilian Corporate Debt.pdf: 1429524 bytes, checksum: f2517a6c1a79a440b38d24a6957278bf (MD5) / Made available in DSpace on 2015-09-22T14:02:44Z (GMT). No. of bitstreams: 1 Implied Hazard Rates Analysis Through Brazilian Corporate Debt.pdf: 1429524 bytes, checksum: f2517a6c1a79a440b38d24a6957278bf (MD5) Previous issue date: 2015-08-26 / The Brazilian corporate debt market is mostly underdeveloped. Most of the participants do not explore and trade in the secondary market, which is specially the case for debentures. In spite of this fact, there are a myriad of tools that could help market participants analyze credit risk, which could make them more willing to trade these risks in the secondary market. This dissertation provides an arbitrage-free model that extracts the implied Risk- Neutral Mean Loss Rates from market prices. It is a reduced form version of the model proposed by Duffie and Singleton (1999) and defines the term-structure of interest rates as a Piece-Wise Constant Function. Through this model, we were able to analyze the implied Risk-Neutral Mean Loss curve through different instruments of Brazilian corporate issuers, using bonds, CDS and debentures. We were able to compare the different curves and decide, in each case analyzed, which of them are best to take on the company’s credit risk, via bonds, CDS or debentures. / No Brasil, o mercado de crédito corporativo ainda é sub-aproveitado. A maioria dos participantes não exploram e não operam no mercado secundário, especialmente no caso de debêntures. Apesar disso, há inúmeras ferramentas que poderiam ajudar os participantes do mercado a analisar o risco de crédito e encorajá-los a operar esses riscos no mercado secundário. Essa dissertação introduz um modelo livre de arbitragem que extrai a Perda Esperada Neutra ao Risco Implícita nos preços de mercado. É uma forma reduzida do modelo proposto por Duffie and Singleton (1999) e modela a estrutura a termo das taxas de juros através de uma Função Constante por Partes. Através do modelo, foi possível analisar a Curva de Perda Esperada Neutra ao Risco Implícita através dos diferentes instrumentos de emissores corporativos brasileiros, utilizando Títulos de Dívida, Swaps de Crédito e Debêntures. Foi possível comparar as diferentes curvas e decidir, em cada caso analisado, qual a melhor alternativa para se tomar o risco de crédito da empresa, via Títulos de Dívida, Debêntures ou Swaps de Crédito.

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.

Master of Science / Department of Agricultural Economics / Ted C. Schroeder / Glynn T. Tonsor / Cattle feeders face significant profit risk when placing cattle on feed. Risks arise from both financial and biological sources. To date, few standardized measures exist to measure current risks against historic levels, or to obtain forward looking risk estimates. Those that do exist could benefit from updates and inclusion of additional risk elements. This study measures the risk of expected profits when cattle are placed on feed. This study creates a forward-looking estimate of expected feedlot profits using futures and options market data as price forecasts. Joint probability distributions are created for prices and cattle performance variables affecting feedlot profit margins. Monte Carlo simulation techniques are then employed to generate probability distributions of expected feedlot profits. Results show cattle feeding is a risky business and cattle feeders have been placing cattle on feed facing significantly negative expected returns since June, 2010. This assessment of negative expected profits is consistent with other findings. Over the study’s 2002 to 2013 time frame, the relative risk to cattle feeding profits accounted for by feed costs has been increasing, while the relative risk levels from feeder cattle and fed cattle prices remain steady. Additionally, the probability of realized per-head profits greater than $100 has been decreasing since 2009 and the probability of realized per-head profits less than $-100 has been increasingly rapidly.

本文主要探討股票選擇權的隱含波動率是否能夠有效反應公司的財務風險與違約風險，並使用Merton (1974)與Black and Scholes (1973)的選擇權評價模型推導出每日的負債權益比率，作為公司財務風險的代理變數；違約風險的代理變數則是使用Bandyopadhyay (2007)的風險中立違約機率與真實世界違約機率。首先，本文觀察到隱含波動率和股票報酬率之間的確存在負向關係，除此之外，也發現非系統隱含波動率與股票報酬率之間也有負向關係。進一步研究非系統隱含波動率是否能夠反應公司風險，結果顯示當公司的財務風險與違約風險增加時，非系統隱含波動率會上升。最後，本文比較非系統隱含波動率與GARCH模型的波動率對公司財務風險與違約風險的資訊內涵，並執行包圍檢定、工具變數兩階段迴歸分析與非包覆模型的檢定，發現非系統隱含波動率的資訊內涵無法包圍GARCH模型的波動率，但兩者的資訊內涵互相交集。