Asymmetric Multivariate Normal Mixture GARCH

An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is shown that the disaggregation of the conditional (co)variance process generated by the model provides substantial intuition. Moreover, the model exhibits a strong performance in calculating out–of–sample Value–at–Risk measures.Conditional Volatility, Finite Normal Mixtures, Multivariate GARCH, Leverage Effect

Essays on the empirical analysis of energy risk

Energy markets have become increasingly sophisticated, requiring modelling techniques of analogous calibre. This thesis deals with models of changing regime for the petroleum complex. Modelling the conditional distribution of energy prices as a regime switching process is motivated by the market-specific characteristics of oil: different market conditions, such as backwardation and contango, involve different dynamics. The first empirical part examines the very short-end of the futures curve volatility. To address in a realistic way the potential diverse response of oil volatility to fundamentals across high and low volatility regimes, augmented regime volatility models are employed. Results indicate that volatility can be decomposed to a highly persistent conditional volatility process and a relatively short-lived non-stationary process. Apart from evaluating the size of price risk, risk managers must also design a framework for mitigating their exposures. This is the focus of the second empirical part which estimates dynamic hedge ratios. Linking the concept of disequilibrium with that of uncertainty across high and low volatility regimes, a state-dependent error correction model with timevarying second moments is introduced. Finally, the third empirical part, examines the information content of the dependence structure between correlated petroleum futures curves. Term structure is decomposed into level, slope and curvature shocks. Introducing a multiregime framework, these factors are utilised to study inter-commodity and inter-market spreads. Results suggest markedly different state-dependent speeds of mean reversion and volatility/correlation dynamics across regimes. Overall, the employed models provide superior forecasting performance and indicate that state-dependent dynamics may provide significant benefits to market participants. The findings of this thesis have important implications for energy market trading and risk management, as well as energy market operations, such as refining and budget planning, by providing valuable information on the oil price volatility dynamics and the ability to predict risk

On Gegenbauer long memory stochastic volatility models: A Bayesian Markov chain Monte Carlo approach with applications

This thesis begins by developing a time series model which has generalised (Gegenbauer) long memory in the mean process with stochastic volatility errors where each process is assumed to have Gaussian errors. We subsequently develop and derive a new Bayesian posterior simulator that couples advanced posterior maximisation techniques, as well as traditional latent stochastic volatility estimation procedures. This model provides a distinct advantage by measuring the long memory attributes in the mean process, whilst also estimating the daily time varying volatility of the error process. These findings are then extended to a Gegenbauer long memory stochastic volatility model with leverage and a bivariate Student's t-error distribution to describe the innovations of the observation and latent volatility jointly, with applications to Cryptocurrency time series. The main advantage of pursuing such a model is incorporating the robustness of the Student's t-distribution, and leverage effects to address the deep rooted characteristics found in Cryptocurrencies. The models are applied to Value-at-Risk (VaR) forecasts and several measures are used to assess the forecast performance. Finally, it is found that Cryptocurrencies do indeed show highly distinct behaviours that are not present in fiat currencies, and thus require specialized analysis. Cryptocurrencies as of late have commanded global attention on a number of fronts. Most notably, their variance properties are known for being notoriously wild, unlike their fiat counterparts. The third part of this thesis highlights some stylized facts about the variance measures of Cryptocurrencies and relates these results to their respective cryptographic designs such as intended transaction speed. The overarching implication of these result is the volatility of Cryptocurrencies can be better understood and measured via the use of fast moving autocorrelation functions, as opposed to smoothly decaying functions for fiat currencies

Asymmetric Dependence in US Financial Risk Factors?

.Asymmetric Dependence; Copulas; Diversification Failure; Risk Factor; Systemic Risk; Time-Varying Downside Risk

Estimating Dependences and Risk between Gold Prices and S&P500: New Evidences from ARCH,GARCH, Copula and ES-VaR models

This thesis examines the correlations and linkages between the stock and commodity in order to quantify the risk present for investors in financial market (stock and commodity) using the Value at Risk measure. The risk assessed in this thesis is losses on investments in stock (S&P500) and commodity (gold prices). The structure of this thesis is based on three empirical chapters. We emphasise the focus by acknowledging the risk factor which is the non-stop fluctuation in the prices of commodity and stock prices. The thesis starts by measuring volatility, then dependence which is the correlation and lastly measure the expected shortfalls and Value at risk (VaR). The research focuses on mitigating the risk using VaR measures and assessing the use of the volatility measures such as ARCH and GARCH and basic VaR calculations, we also measured the correlation using the Copula method. Since, the measures of volatility methods have limitations that they can measure single security at a time, the second empirical chapter measures the interdependence of stock and commodity (S&P500 and Gold Price Index) by investigating the risk transmission involved in investing in any of them and whether the ups and downs in the prices of one effect the prices of the other using the Time Varying copula method. Lastly, the third empirical chapter which is the last chapter, investigates the expected shortfalls and Value at Risk (VaR) between the S&P500 and Gold prices Index using the ES-VaR method proposed by Patton, Ziegel and Chen (2018). Volatility is considered to be the most popular and traditional measure of risk. For which we have used ARCH and GARCH model in our first empirical chapter. However, the problem with volatility is that it does not take into account the direction of an investments’ movement: volatility of stocks is that they suddenly jump higher and investors are not distressed with gains. When we talk about investors for them the risk is about the odds of losing money, after my research and findings VaR is based on the common-sense fact. Hence, investors care about the odds of big losses, VaR answers the question, what is my worst-case scenario? Or simply how much I could lose in a really bad month? The results of the thesis demonstrated that measuring volatility (ARCH GARCH) alone was not sufficient in measuring the risk involved in an investment therefore methodologies such as correlation and VAR demonstrates better results. In terms of measuring the interdependence, the Time Varying Copula is used since the dynamic structure of the de- pendence between the data can be modelled by allowing either the copula function or the dependence parameter to be time varying. Lastly, hybrid model further demonstrates the average return on a risky asset for which Expected Shortfall (ES) along with some quantile dependence and VaR (Value at risk) is utilised. Basel III Accord which is applied in coming years till 2019 focuses more on ES unlike VaR, hence there is little existing work on modelling ES. The thesis focused on the results from the model of Patton, Ziegel and Chen (2018) which is based on the statistical decision theory. Patton, Ziegel and Chen (2018), overcame the problem of elicitability for ES by using ES and VaR jointly and propose the new dynamic model of risk measure. This research adds to the contribution of knowledge that measuring risk by using volatility is not enough for measuring risk, interdependence helps in measuring the dependency of one variable over the other and estimations and inference methods proposed by Patton, Ziegel and Chen (2018) using simulations proposed in ES-VaR model further concludes that ARCH and GARCH or other rolling window models are not enough for determining the risk forecasts. The results suggest, in first empirical chapter we see volatility between Gold prices and S&P500. The second empirical chapter results suggest conditional dependence of the two indexes is strongly time varying. The correlation between the stock is high before 2008. The results further displayed slight stronger bivariate upper tail, which signifies that the conditional dependence of the indexes is influence by positive shocks. The last empirical chapter findings proposed that measuring forecasts using ES-Var model proposed by Patton, Ziegel and Chen (2018) does outer perform forecasts based on univariate GARCH model. Investors want to 10 protect themselves from high losses and ES-VaR model discussed in last chapter would certainly help them to manage their funds properly

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Bayesian non-parametrics for time-varying volatility models

Mención Internacional en el título de doctorPrograma Oficial de Doctorado en Economía de la Empresa y Métodos CuantitativosPresidente: Michael Peter Wiper; Secretaria: María Pilar Muñoz Gracia; Vocal: Roberto Casarí