Option market data are quoted in terms of option prices and are fragmented into over 100 individual contract files per day for each symbol. Traders and quantitative analysts compare values of options in terms of implied volatilities. The current project refactors fragmented option price data into implied volatility files organized by stock symbols and expiration dates. Each resulting file comprises the temporal evolution of daily volatility smile curves for every day prior to expiration. Possible analysis enabled by the refactored data is demonstrated.

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The 2008 financial crisis provides a valuable opportunity to study empirical data of market volatility during severe financial crisis. In this thesis, we study the implied volatility of VIX options during the crisis (2008) and a relatively calm period (2011). We present a method of calculating the implied volatility of VIX options and fit the implied volatilities using a 4th degree spline interpolation and propose method of extracting risk neutral density from fitted data. We analyze the slope and the level of the fitted implied volatility of VIX options during those periods. The results show that the level of the implied volatility of VIX options is higher and the slope is flatter during the distressed market compared to the relative calm periods.

The purpose of this thesis is to study if and how well implied volatility can predict realised volatility and returns on the OMXS30 index one month in the future. The findings are put in relation to how historical volatility can predict realised volatility and how changes in implied volatility can predict returns. The study covers the time period from 10th of May 2012 to 9th of February 2020 and the implied volatility used in the study is derived from an unweighted average of OMXS30 call and put option implied volatility. Six different OLS-regressions are performed to study the prediction capability of implied volatility. This study finds support of implied volatility to be a statistically significant estimate for future realised returns in a univariate regression. However, our results show that historical volatility performs slightly better predictions of realised volatility than implied volatility. These are contradictory results to the majority of the papers studied in this thesis. These papers share the common notion that implied volatility is superior to historical volatility in predicting realised volatility. Further our results show that implied volatility nor change in implied volatility are significant estimates to future realised returns and perform poorly as predictors. This result is supported by the larger part of previous research, which found implied volatility to be a weak predictor of returns.

This study compares different methods of forecasting price volatility in the crude oil futures market using daily data for the period November 1986 through March 1997. It compares the forward-looking implied volatility measure with two backward-looking time-series measures based on past returns - a simple historical volatility estimator and a set of estimators based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) class of models. Tests for the relative information content of implied volatilities vis-à-vis GARCH time series models are conducted within-sample by estimating nested conditional variance equations with returns information and implied volatilities as explanatory variables. Likelihood ratio tests indicate that both implied volatilities and past returns contribute volatility information. The study also checks for and confirms that the conditional Generalized Error Distribution (GED) better describes fat-tailed returns in the crude oil market as compared to the conditional normal distribution. Out-of-sample forecasts of volatility using the GARCH GED model, implied volatility, and historical volatility are compared with realized volatility over two-week and four-week horizons to determine forecast accuracy. Forecasts are also evaluated for predictive power by regressing realized volatility on the forecasts. GARCH forecasts, though superior to historical volatility, do not perform as well as implied volatility over the two-week horizon. In the four-week case, historical volatility outperforms both of the other measures. Tests of relative information content show that for both forecast horizons, a combination of implied volatility and historical volatility leaves little information to be added by the GARCH model. / Master of Arts

Yes / This paper studies the predictive ability of corridor implied volatility (CIV) measure. It is motivated by the fact that CIV is measured with better precision and reliability than the model-free implied volatility due to the lack of liquid options in the tails of the risk-neutral distribution. By adding CIV measures to the modified GARCH specifications, the out-of-sample predictive ability of CIV is measured by the forecast accuracy of conditional volatility. It finds that the narrowest CIV measure, covering about 10% of the RND, dominate the 1-day ahead conditional volatility forecasts regardless of the choice of GARCH models in high volatile period; as market moves to non volatile periods, the optimal width broadens. For multi-day ahead forecasts narrow and mid-range CIV measures are favoured in the full sample and high volatile period for all forecast horizons, depending on which loss functions are used; whereas in non turbulent markets, certain mid-range CIV measures are favoured, for rare instances, wide CIV measures dominate the performance. Regarding the comparisons between best performed CIV measures and two benchmark measures (market volatility index and at-the-money Black–Scholes implied volatility), it shows that under the EGARCH framework, none of the benchmark measures are found to outperform best performed CIV measures, whereas under the GARCH and NAGARCH models, best performed CIV measures are outperformed by benchmark measures for certain instances.

<p>Volatility forecast plays a central role in the financial decision making process. An intrinsic purpose of any investor is profit earning. For that purpose investors need to estimate the risk. One of the most efficient</p><p>methods to this end is the volatility estimation. In this theses I compare the CBOE Volatility Index, (VIX) with the actual volatility on an index at the Nordic Market. The actual volatility is defined as the one-day-ahead prediction as calculated by using the GARCH(1,1) model. By using the VIX model I performed consecutive predictions 30 days ahead between February the 2nd, 2007 to March</p><p>the 6th, 2007. These predictions were compared with the GARCH(1,1) one-day-ahead predictions for the same period. To my knowledge, such comparisons have not been performed earlier on the Nordic Market. The conclusion of the study was that the VIX predictions tends to higher values then the GARCH(1,1) predictions except for large prices upward jumps, which indicates that the VIX is not able to predict future shocks.</p><p>Except from these jumps, the VIX more often shows larger value than the GARCH(1,1). This is interpreted as an uncertainly of the prediction. However, the VIX predictions follows the actual volatility reasonable</p><p>well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.</p>

Volatility forecast plays a central role in the financial decision making process. An intrinsic purpose of any investor is profit earning. For that purpose investors need to estimate the risk. One of the most efficient methods to this end is the volatility estimation. In this theses I compare the CBOE Volatility Index, (VIX) with the actual volatility on an index at the Nordic Market. The actual volatility is defined as the one-day-ahead prediction as calculated by using the GARCH(1,1) model. By using the VIX model I performed consecutive predictions 30 days ahead between February the 2nd, 2007 to March the 6th, 2007. These predictions were compared with the GARCH(1,1) one-day-ahead predictions for the same period. To my knowledge, such comparisons have not been performed earlier on the Nordic Market. The conclusion of the study was that the VIX predictions tends to higher values then the GARCH(1,1) predictions except for large prices upward jumps, which indicates that the VIX is not able to predict future shocks. Except from these jumps, the VIX more often shows larger value than the GARCH(1,1). This is interpreted as an uncertainly of the prediction. However, the VIX predictions follows the actual volatility reasonable well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.

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This thesis investigates implied volatility in general classes of stock price models. To begin with, we take a very general view. We find that implied volatility is always, everywhere, and for every expiry well-defined only if the stock price is a non-negative martingale. We also derive sufficient and close to necessary conditions for an implied volatility surface to be free from static arbitrage. In this context, free from static arbitrage means that the call price surface generated by the implied volatility surface is free from static arbitrage. We also investigate the small time to expiry behaviour of implied volatility. We do this in almost complete generality, assuming only that the call price surface is non-decreasing and right continuous in time to expiry and that the call surface satisfies the no-arbitrage bounds (S-K)+≤ C(K, τ)≤ S. We used S to denote the current stock price, K to be a option strike price, τ denotes time to expiry, and C(K, τ) the price of the K strike option expiring in τ time units. Under these weak assumptions, we obtain exact asymptotic formulae relating the call price surface and the implied volatility surface close to expiry. We apply our general asymptotic formulae to determining the small time to expiry behaviour of implied volatility in a variety of models. We consider exponential L??vy models, obtaining new and somewhat surprising results. We then investigate the behaviour close to expiry of stochastic volatility models in the at-the-money case. Our results generalise what is already known and by a novel method of proof. In the not at-the-money case, we consider local volatility models using classical results of Varadhan. In obtaining the asymptotics for local volatility models, we use a representation of the European call as an integral over time to expiry. We devote an entire chapter to representations of the European call option; a key role is played by local time and the argument of Klebaner. A novel alternative that is especially useful in the local volatility case is also presented.