This dissertation consists of two chapters that examine the construction of financial market volatility indexes and their forecasting efficiency across predictive regression models. Each of the chapter is devoted to diferent volatility measures which are related and evaluated in thframework of forecasting regressions. The first chapter studies the sampling and liquidity issues in constructing volatility indexes, VIX and VXO, in emerging options market like Taiwan. VXO and VIX have been widely used to measure the 22-day forward volatility of the market. However, for an emerging market, VXO and VIX are difficult to measure with accuracy when tradings of the second and next to second nearby options are illiquid. The chapter proposes four methods to sample the option prices across liquidity proxies ¡V five different days of rollover rules ¡V for option trades to construct volatility index series. The paper finds that, based on the sampling method of the average of all midpoints of bid and ask quote option prices, the volatility indexes constructed by minute-tick data have less missing data and more efficient in volatility forecast than the method suggested by CBOE. Additionally, illiquidity in emerging options market does not, based on different rollover rules, lead to substantial biases in the forecasting effectiveness of the volatility indexes.Finally, the forecasting ability of VIX, in terms of naive forecasts and forecasting regressions, is superior to VXO in Taiwan. The second chapter uses high-frequency intraday volatility as a benchmark to measure the efficacy of model-free and model-based econometric models. The realized volatility computed from intraday data has been widely regarded as a more accurate proxy for market volatility than squared daily returns. The chapter adopts several time series models to assess the fore-casting efficiency of future realized volatility in Taiwan stock market. The paper finds that, for 1-day directional accuracy forecast performance, semiparametric fractional autoregressive model (SEMIFAR, Beran and Ocker, 2001) ranks highest with 78.52% hit accuracy, followed by multiplicative error model (MEM, Engle, 2002), and augmented GJR-GARCH model. For 1-day forecasting errors evaluated by root mean squared errors (RMSE), GJR-GARCH model augmented with high-low range volatility ranks highest, followed by SEMIFAR and MEM model, both of which, however, outperform augmented GJR-GARCH by the measure of mean absolute value (MAE) and p-statistics (Blair et al., 2001).

This study tests if historical volatility- and implied volatility has significant predictive power over future realized volatility and if so which one of the two is the superior predictor. The study is conducted by using historical volatility of the OMXS30 and implied volatility from OMXS30 call options during the period 2012-2023. Three regressions have been made to test the research questions, two simple linear regression and one multiple linear regression. The results of the study showed that both historical- and implied volatility had significant predictive power over future realized volatility with implied being the superior one with a higher correlation coefficient. The multiple regression showed that both the independent variables were important and both of them explained different parts of the data, which means that they have complementary abilities and that both should be used when assessing the forecast of realized volatility.

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[pt] Algumas literaturas sugerem que a volatilidade implícita das opções de compra de ações não deve ser utilizada como estimador para a volatilidade futura. Contudo, estudos recentes e aplicados ao mercado brasileiro de ações comprovaram que em determinados casos existe relação entre a volatilidade implícita e a volatilidade real (ou realizada). Isso significa dizer que a primeira traz informações sobre a última. Nesse contexto, o objetivo deste estudo é comparar a volatilidade implícita de dois modelos de apreçamento de opções com a volatilidade realizada. Entre os modelos de Black-Scholes (1973) e Corrado-Su (1996), utilizando dados de opções de Petrobras e Vale do Rio Doce, foram calculados, através do erro quadrático, aqueles resultados que mais se aproximaram da volatilidade realizada. Estes resultados trazem indícios de que o modelo de Black-Scholes, em média, foi superior ao Corrado-Su no período que vai de janeiro de 2005 a julho de 2009. Porém, o último, por levar em consideração a assimetria e a curtose da distribuição de retornos, chegou mais perto da volatilidade realizada apenas em alguns momentos específicos das economias brasileira e mundial. / [en] Several authors have proposed that implied volatility from purchase options should not be used as an estimate for future volatility. However, recent studies applied to the Brazilian stock market proved that in certain cases there is relation between implied volatility and realized volatility. This means that the first one provides information on the last. In this context, the objective of this study is to compare implied volatilities from two different option pricing models against the realized volatility. The models are Black-Scholes (1973) and Corrado-Su (1996). Working with purchase options on Petrobras and Vale do Rio Doce, it was calculated the difference, by quadratic error, between the implied volatility of these models and the realized volatility. After this, it was checked those results that came closer to the realized volatility. The results provide evidence that the Black-Scholes model, on average, has higher performance than Corrado-Su from January 2005 to July 2009. However, Corrado-Su by taking into account the asymmetry and kurtosis of the distribution of returns came closer to the realized volatility only in specific moments of the Brazilian and global economies.

Neste trabalho realizamos um amplo estudo de simulação com o objetivo principal de avaliar o desempenho de carteiras de mínima variância global construídas com base em modelos de previsão da volatilidade que utilizam dados de alta frequência (em comparação a dados diários). O estudo é baseado em um abrangente conjunto de dados financeiros, compreendendo 41 ações listadas na BM&FBOVESPA entre 2009 e 2017. Nós avaliamos modelos de previsão de volatilidade que são inspirados na literatura ARCH, mas que também incluem medidas realizadas. Eles são os modelos GARCH-X, HEAVY e Realized GARCH. Seu desempenho é comparado com o de carteiras construídas com base na matriz de covariância amostral, métodos de encolhimento e DCC-GARCH, bem como com a carteira igualmente ponderada e o índice Ibovespa. Uma vez que a natureza do trabalho é multivariada, e a fim de possibilitar a estimação de matrizes de covariância de grandes dimensões, recorremos à especificação DCC. Utilizamos três frequências de rebalanceamento (diária, semanal e mensal) e quatro conjuntos diferentes de restrições sobre os pesos das carteiras. A avaliação de desempenho baseia-se em medidas econômicas tais como retornos anualizados, volatilidade anualizada, razão de Sharpe, máximo drawdown, Valor em Risco, Valor em Risco condicional e turnover. Como conclusão, para o nosso conjunto de dados o uso de retornos intradiários (amostrados a cada 5 e 10 minutos) não melhora o desempenho das carteiras de mínima variância global. / In this work we perform an extensive backtesting study targeting as a main goal to assess the performance of global minimum variance (GMV) portfolios built on volatility forecasting models that make use of high frequency (compared to daily) data. The study is based on a broad intradaily financial dataset comprising 41 assets listed on the BM&FBOVESPA from 2009 to 2017. We evaluate volatility forecasting models that are inspired by the ARCH literature, but also include realized measures. They are the GARCH-X, the High-Frequency Based Volatility (HEAVY) and the Realized GARCH models. Their perfomances are benchmarked against portfolios built on the sample covariance matrix, covariance matrix shrinkage methods, DCC-GARCH as well as the naive (equally weighted) portfolio and the Ibovespa index. Since the nature of this work is multivariate and in order to make possible the estimation of large covariance matrices, we resort to the Dynamic Conditional Correlation (DCC) specification. We use three different rebalancing schemes (daily, weekly and monthly) and four different sets of constraints on portfolio weights. The performance assessment relies on economic measures such as annualized portfolio returns, annualized volatility, Sharpe ratio, maximum drawdown, Value at Risk, Expected Shortfall and turnover. We also account for transaction costs. As a conclusion, for our dataset the use of intradaily returns (sampled every 5 and 10 minutes) does not enhance the performance of GMV portfolios.

Many past literatures have examined the predictive power of implied volatility versus that of historical volatility, but they have showed divergent conclusions. One of the major differences among these studies is the methods that they used to obtain implied volatility. The VIX index, introduced in 1993, provides a model-free and directly observable source of implied volatility data. The VIX futures is an actively traded VIX derivative product, and its prices are believed to contain market’s expectation about future volatility. By analyzing the relationship between the VIX futures prices and the realized volatilities of the 30-day period that these VIX futures contracts cover, this paper finds that the VIX futures contracts with shorter maturities have predictive power on future realized volatility, but they are upwardly biased estimates. The predictive power, however, decreases as the time to maturity increases. The outstanding VIX futures contracts with the nearest expiration dates outperform GARCH estimates based on historical return data at predicting future realized volatility.

The purpose of this thesis is to evaluate five different volatility forecasting models that are used to calculate financial market risk. The models are used on both daily exchange rates and high-frequency intraday data from four different series. The results show that time series models fitted to high-frequency intraday data together with a critical value taken from the empirical distribution displayed the best forecasts overall.

In this paper we use model-free estimates of daily exchange rate volatilities employing high-frequency intraday data, known as Realized Volatility, which is then forecasted with ARMA-models and used to produce one-day-ahead Value-at-Risk predictions. The forecasting accuracy of the method is contrasted against the more widely used ARCH-models based on daily squared returns. Our results indicate that the ARCH-models tend to underestimate the Value-at-Risk in foreign exchange markets compared to models using Realized Volatility

I compared various MIDAS (mixed data sampling) regression models to predict volatility from one week to one month with different regressors based on the records of Chinese Shanghai composite index. The main regressors are in 2 types, one is the realized power (involving 5-min absolute returns), the other is the quadratic variation, computed by squared returns. And realized power performs best at all the forecast horizons. I also compare the effect of lag numbers in regression, form 1 to 200, and it doesn’t change much after 50. In 3 week and month predict horizons, the fitness result with different lag numbers has a waving type among all the regressors, that implies there exists a seasonal effect which is the same as predict horizons in the lagged variables. At last,the out-of -sample and in-sample result of RV and RAV are quite similar, but in sometimes, out-of sample performs better.