The objective of this thesis is to explain the behaviour of stock returns around the disclosure of different types of information release by UK companies. Previous literature has documented the existence of both market anticipation and the lagged impounding of value relevant information. The main objective of this research is, therefore, to identify the conditions under which investors choose to be informed in anticipation of and in response to, a corporate disclosure. More specifically, we explain the behaviour of stock returns in terms of the costs and benefits which investors must consider when deciding whether to acquire and interpret information. The results indicate that market anticipation is an increasing function of firm size, the number of years a firm has been trading and the volatility of prior stock returns. However, increased voluntary disclosure by firms would appear to reduce the ability of investors to and anticipate and interpret information. The volatility of stock returns, prior to the disclosure, is nevertheless the main driving force behind the explanation of post-announcement drift. There are also indications that investors' initial reactions to both earnings and non-earnings news are not based on informed judgements, and that bad news is generally associated with greater uncertainty than good news. Bad news would appear to be more difficult to anticipate and interpret, relative to good news. On further examination, however, investor anticipation is shown to be largely based on information as opposed to uninformed trading.

One of the most challenging topics in financial time series analysis is the modeling of conditional variances of asset returns. Although conditional variances are not directly observable there are numerous approaches in the literature to overcome this problem and to predict volatilities on the basis of historical asset returns. The most prominent approach is the class of GARCH models where conditional variances are governed by a linear autoregressive process of past squared returns and variances. Recent research in this field, however, has focused on modeling asymmetries of conditional variances by means of non-linear models. While there is evidence that such an approach improves the fit to empirical asset returns, most non-linear specifications assume conditional normal distributions and ignore the importance of alternative models. Concentrating on the distributional assumptions is, however, essential since asset returns are characterized by excess kurtosis and hence fat tails that cannot be explained by models with suffcient heteroskedasticity. In this paper we take up the issue of returns' distributions and contrast it with the specification of non-linear GARCH models. We use daily returns for the Dow Jones Industrial Average over a large period of time and evaluate the predictive power of different linear and non-linear volatility specifications under alternative distributional assumptions. Our empirical analysis suggests that while non-linearities do play a role in explaining the dynamics of conditional variances, the predictive power of the models does also depend on the distributional assumptions. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

This thesis presents an empirical investigation of the behaviour of financial markets and also the relationship on the real economy. The thesis will focus on Ireland, a small open economy with increased dependence on international developments. Two important aspects of the Irish economy, the term structure of interest rates and impact of exchange rate volatility, will be analysed. The motivation for the analysis of the term structure of interest rates in part I is two fold. Central banks can control very short-term interest rates, but of course the real economy will only really be affected by the long-term interest rate. Therefore the transmission mechanism from monetary policy to the real economy will depend on the relationship between short-term interest rates and long-term interest rates, i.e. the term structure of interest rates. The second important issue is that of market efficiency, and whether asset prices and returns are correctly valued by the market. A number of different interest rate maturities will be used to test the Expectations Hypothesis (EH) of term structure. The EH will also be tested assuming constant and time varying term premia. The results give support for the EH, and fmd no evidence of a time varying term premium. Given the recent extraordinary growth in the share of Irish exports in GDP, the impact of exchange rate volatility on Irish exports is analysed in part 2. The moti vation behind part 2 is to test whether the resulting monetary union will lead to a rise in exports, as a result of the end of exchange rate risk. Using the cointegration-ECM methodology I fmd that in the long-run there is no significant effect on Irish exports to the UK, while there is actually a positive impact on exports to European countries (UK included). I tentatively conclude that in the long-run the involvement in a single European currency will have no impact on trade.

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.

The primary aim of this study is to investigate the stock return volatility surrounding management earnings forecasts. Disclosure by managers of expected earnings are particularly important communications, and as such, it is important to understand the capital market implications surrounding them. In doing so, the research questions are essentially aimed at examining the stock return volatility, first, at the release of a management earnings forecast, and second, at the eventual announcement of the realised earnings for that period. The first test investigates whether there is an increase in volatility surrounding a management earnings forecast for those firms who release them compared to a matched-firm sample of firms without a management earnings forecast at that date, and then further examines that result based on different forecast antecedents and forecast characteristics. Next, this study tests, for firms who do release a management earnings forecast during the year, whether stock volatility is lower than firms who do not release a management earnings forecast at the eventual earnings announcement date. In brief, the evidence using the Garman and Klass [1980] ???best analytic scale-invariant estimator??? of volatility in an Australian context, between 1993 and 2003, finds that stock return volatility is greater for bad news forecasts, forecasts of low specificity, and forecasts issued by firms perceived ex ante as being of lower credibility using both permutation analysis and modelling daily volatility. At the earnings announcement date, however, there is no evidence that stock return volatility is lower for firms that issue management earnings forecasts during the year. Overall, this result challenges the information asymmetry argument in the literature that disclosure will reduce volatility in the long-run.

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.

<p>The volatility of share prices is an important variable in most asset pricing models and option pricing formulas.Valuation of volatility of share prices have become a major challenge with the development of the knowledge-driven economy as evidence suggest that not all elements of company wealth are physical in nature.</p><p>The purpose of this project entitled “The intensity of the firm’s intangible asset on the volatility of their stock price” is to check if the intensity of intangible assets in a firm’s balance sheet affects the volatility of their stock price. A brief overview of intangible assets is also included in this study.</p><p>An OLS regression was run and the results of the entire data set gives a negative correlation between intensity of intangible assets and volatility of stock prices probably due to the fact that the volatility of the firm share prices are driven by uncertainty and expectation of future growth. An industry-grouping regression was carried out, the results shows that for basic pharmaceuticals there is a positive correlation between the intensity of intangible assets and their price volatility while the other three industry groups produce a negative correlation.</p><p>The study relies on secondary data of randomly selected fourty (40) publicly traded companies in Europe from four different industry groupings namely: manufacture of basic pharmaceuticals, manufacture of food products and beverages, information technology and manufacture of basic metals.</p>

This dissertation addresses various aspects of estimation and inference for multivariate stochastic processes with jumps. The first chapter develops an unbiased Monte Carlo estimator of the transition density of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. The density estimator proposed enables efficient parametric estimation of multivariate jump-diffusion models based on discretely observed data. Under mild conditions, the resulting parameter estimates have the same asymptotic behavior as maximum likelihood estimators as the number of data points grows, even when the sampling frequency of the data is fixed. In a numerical case study of practical relevance, the density and parameter estimators are shown to be highly accurate and computationally efficient. In the second chapter, I examine continuous-time stochastic volatility models with jumps in returns and volatility in which the parameters governing the jumps are allowed to switch according to a Markov chain. I estimate the parameters and the latent processes using the S&P 500 and Nasdaq indices from 1990 to 2014. The Markov-switching parameters characterize well the periods of market stress, such as those in 1997-1998, 2001 and 2007-2010. Several statistical tests favor the model with Markov-switching jump parameters. These results provide empirical evidence about the state-dependent and time-varying nature of asset price jumps, a feature of asset prices that has recently been documented using high-frequency data. The third chapter considers applying Markov-switching affine stochastic volatility models with jumps in returns and volatility, where the jump parameters are not regime-switching. The estimation is performed via Markov Chain Monte Carlo methods, allowing to obtain the latent processes induced by the structure of the models. Furthermore, I propose some misspecification tests and develop a Markov-switching test based on the odds ratios. The parameters and the latent processes are estimated using the S&P 500 index from 1970 to 2014. I show that the S&P 500 stochastic volatility exhibits a Markov-switching behavior, and that most of the high volatility regimes coincide with the recessions identified ex-post by the National Bureau of Economic Research.

This paper empirically investigates the relationship between long-run economic growth and output volatility for the time series experience of 25 OECD countries between the years 1960 and 2013. Given the low number of observations, we reject, based on Monte Carlo simulations, the obvious choice of Garch estimation, and instead propose a pooled OLS estimator between a filtered GDP series that eliminates the cyclicality and the fluctuations around this trend. We find strong empirical evidence for a positive relationship between output variability and economic growth. This relationship seems to confirm theoretical literature which proposes such a positive relation.

In this thesis, we propose two continuous time stochastic volatility models with long memory that generalize two existing models. More importantly, we provide analytical formulae that allow us to study option prices numerically, rather than by means of simulation. We are not aware about analytical results in continuous time long memory case. In both models, we allow for the non-zero correlation between the stochastic volatility and stock price processes. We numerically study the effects of long memory on the option prices. We show that the fractional integration parameter has the opposite effect to that of volatility of volatility parameter in short memory models. We also find that long memory models have the potential to accommodate the short term options and the decay of volatility skew better than the corresponding short memory stochastic volatility models.