Effective energy commodities’ risk management: Econometric modeling of price volatility

The current study emphasizes on the importance of the development of an effective price risk management strategy regarding energy products, as a result of the high volatility of that particular market. The study provides a thorough investigation of the energy price volatility, through the use of GARCH type model variations and the Markov-Switching GARCH methodology, as they are presented in the most representative academic researches. A large number of GARCH type models are exhibited together with the methodology and all the econometric procedures and tests that are necessary for developing a robust and precise forecasting model regarding energy price volatility. Nevertheless, the present research moves another step forward, in an attempt to cover also the probability of potential shifts in the unconditional variance of the models due to the effect of economic crises and several unexpected geopolitical events into the energy market prices

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

Forecasting volatility of Bitcoin

Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts.publishedVersio

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Identification des régimes et regroupement des séquences pour la prévision des marchés financiers

Abstract : Regime switching analysis is extensively advocated to capture complex behaviors underlying financial time series for market prediction. Two main disadvantages in current approaches of regime identification are raised in the literature: 1) the lack of a mechanism for identifying regimes dynamically, restricting them to switching among a fixed set of regimes with a static transition probability matrix; 2) failure to utilize cross-sectional regime dependencies among time series, since not all the time series are synchronized to the same regime. As the numerical time series can be symbolized into categorical sequences, a third issue raises: 3) the lack of a meaningful and effective measure of the similarity between chronological dependent categorical values, in order to identify sequence clusters that could serve as regimes for market forecasting. In this thesis, we propose a dynamic regime identification model that can identify regimes dynamically with a time-varying transition probability, to address the first issue. For the second issue, we propose a cluster-based regime identification model to account for the cross-sectional regime dependencies underlying financial time series for market forecasting. For the last issue, we develop a dynamic order Markov model, making use of information underlying frequent consecutive patterns and sparse patterns, to identify the clusters that could serve as regimes identified on categorized financial time series. Experiments on synthetic and real-world datasets show that our two regime models show good performance on both regime identification and forecasting, while our dynamic order Markov clustering model also demonstrates good performance on identifying clusters from categorical sequences.L'analyse de changement de régime est largement préconisée pour capturer les comportements complexes sous-jacents aux séries chronologiques financières pour la prédiction du marché. Deux principaux problèmes des approches actuelles d'identifica-tion de régime sont soulevés dans la littérature. Il s’agit de: 1) l'absence d'un mécanisme d'identification dynamique des régimes. Ceci limite la commutation entre un ensemble fixe de régimes avec une matrice de probabilité de transition statique; 2) l’incapacité à utiliser les dépendances transversales des régimes entre les séries chronologiques, car toutes les séries chronologiques ne sont pas synchronisées sur le même régime. Étant donné que les séries temporelles numériques peuvent être symbolisées en séquences catégorielles, un troisième problème se pose: 3) l'absence d'une mesure significative et efficace de la similarité entre les séries chronologiques dépendant des valeurs catégorielles pour identifier les clusters de séquences qui pourraient servir de régimes de prévision du marché. Dans cette thèse, nous proposons un modèle d'identification de régime dynamique qui identifie dynamiquement des régimes avec une probabilité de transition variable dans le temps afin de répondre au premier problème. Ensuite, pour adresser le deuxième problème, nous proposons un modèle d'identification de régime basé sur les clusters. Notre modèle considère les dépendances transversales des régimes sous-jacents aux séries chronologiques financières avant d’effectuer la prévision du marché. Pour terminer, nous abordons le troisième problème en développant un modèle de Markov d'ordre dynamique, en utilisant les informations sous-jacentes aux motifs consécutifs fréquents et aux motifs clairsemés, pour identifier les clusters qui peuvent servir de régimes identifiés sur des séries chronologiques financières catégorisées. Nous avons mené des expériences sur des ensembles de données synthétiques et du monde réel. Nous démontrons que nos deux modèles de régime présentent de bonnes performances à la fois en termes d'identification et de prévision de régime, et notre modèle de clustering de Markov d'ordre dynamique produit également de bonnes performances dans l'identification de clusters à partir de séquences catégorielles

Commodity price volatility, stock market performance and economic growth: evidence from BRICS countries

Abstracts in English, Afrikaans and ZuluThe study investigated the nexus between commodity price volatility, stock market performance, and economic growth in the emerging economies of Brazil, Russia, India, China, and South Africa (the BRICS) predicated on two hypotheses. First, the study hypothesised that in modern integrated financial systems, commodity price volatility predisposes stock market performance to be non-linearly related to economic growth. The second hypothesis was that financial crises are an inescapable feature of modern financial systems. The study used daily data on stock indices and selected commodity prices as well as monthly data on national output proxies and stock indices. The study analysed data for non-linearities, fractality, and entropy behaviour using the spectral causality approach, univariate GARCH, EGARCH, FIGARCH, DCC-GARCH, and Markov Regime Switching (MRS) – GARCH. The four main findings were: first, spectral causality tests signalled dynamic non-linearities in the relationship between the three commodity futures prices and the BRICS stock indices. Second, the predominantly non-linear relationship between commodity prices and stock prices was reflected in the nexus between the national output proxies and the indices of the five main commodity classes. Third, spectral causality analysis revealed that the causal structures between commodity prices and national output proxies were non-linear and dynamic. Fourth, the Nyblom parameter stability tests revealed evidence of structural breaks in the data that was analysed. The DCC-GARCH model uncovered strong evidence of contagion, spillovers, and interdependence. The study added to the body of knowledge in three ways. First, micro and macro levels of commodity price changes were linked with corresponding stock market performance indicator changes. Second, unlike earlier studies on the commodity price – stock market performance – economic growth nexus, the study employed spectral causality analysis, single - regime GARCH analysis, Dynamic Conditional Correlation (DCC) – GARCH and a two-step Markov – Regime – Switching – GARCH as a unified analytical approach. Third, spectral causality graphs depicting relationships between stock indices and national output proxies revealed benign business cycle effects, thus, contributing to broadening the scope of business cycle theoryBusiness ManagementPhD. (Management Studies

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Essays on Pricing Behaviors of Energy Commodities

This dissertation investigates the pricing behaviors of two major energy commodities, U.S. natural gas and crude oil, using times series models. It examines the relationships between U.S. natural gas price variations and changes in market fundamentals within a two-state Markov-switching framework. It is found that the regime-switching model does a better forecasting job in general than the linear fundamental model without regime-switching framework, especially in the case of 1-step-ahead forecast. Studies are conducted of the dynamics between crude oil price and U.S. dollar exchange rates. Empirical tests are applied to both full sample (1986—2010) and subsample (2002—2010) data. It is found that causality runs in both directions between the oil and the dollar. Meanwhile, a theoretical 5-country partial dynamic portfolio model is constructed to explain the dynamics between oil and dollar with special attention to the roles of China and Russia. It is shown that emergence of China‘s economy enhances the linkage between oil and dollar due to China's foreign exchange policy. Further research is dedicated to the role of speculation in crude oil and natural gas markets. First a literature review on theory of speculation is conducted. Empirical studies on speculation in commodity markets are surveyed, with special focus on energy commodity market. To test the theory that speculation may affect commodity prices by exaggerating the signals sent by market fundamentals, this essay utilizes the forecast errors from the first essay to investigate the forecasting ability of speculators' net long positions in the market. Limited evidence is provided to support the bubble theory in U.S. natural gas market. In conclusion, this dissertation explores both fundamentals and speculators' roles in the U.S. natural gas and global crude oil markets. It is found that market fundamentals are the major driving forces for the two energy commodities price booms seen during the past several years