The cointegrating relationship in Asian markets with applications to stock prices, exchange rates and interest rates

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this research is to investigate the long-run co-integrating relationships in the Asian markets. Our research focuses on 4 areas; pair trading, out-of-sample forecasting, testing the unbiased forward exchange rate hypothesis and testing the expectation hypothesis of the term structure of interest rates. The introduction is provided in chapter one. In chapter two, we develop a pairs trading strategy using individual stocks listed in the Stock Exchange of Thailand. Engle and Granger approach is used to identify the potential pairs that are cointegrated. The results show that pairs trading strategy is profitable in this market. Chapter three examines the forecasting performance of the error correction model on daily share price series from the Stock Exchange of Thailand. The disequilibrium term is classified into “correct” and “mix” sign based on Alexander (2008)’s criterion; the results indicate that the error correction component can help to improve the predictability in the long run. Chapter four tests the unbiased forward rate hypothesis of 11 Asian exchange rates using linear conventional regression, ECM and logistic smooth transition regression with the forward premium as the transition variable. Out-of-sample forecasting results also suggest that inferior forecasting performance could be obtained as a result of using linear models. In chapter five, we investigate the expectation hypothesis of the term structure of interest rate for four Asian countries. We employ linear models and nonlinear approaches that allow to capture asymmetric and symmetric adjustments. The result also indicates that the term structure can be better modeled by means of LSTR models. The forecasting exercise also confirms these findings

Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models

The objective of this research is to examine and compare the mean reversion phenomenon in developed and emerging stock markets. An important aim is to measure and compare the speed of mean reversion and half-life of volatility shocks of emerging and developed markets. For this purpose, we have selected five developed and seven emerging markets, and used daily market indices for the period of 1 January 2000 to 30 June 2016. We employed autoregressive conditional heteroskedasticity – Lagrange multiplier (A.R.C.H.-L.M.), generalised autoregressive conditional heteroskedasticity (G.A.R.C.H.) (1, 1), and half-life volatility shock techniques to carry out this research. The results of the study confirmed the mean-reverting process in developed and emerging markets. The South Korean market has the slowest mean reversion, and thus has the highest comparative volatility over a longer period of time. However, the Pakistan stock exchange exhibited the fastest mean reverting process. It is also concluded that the relative volatilities are higher in emerging markets, whereas the comparative volatilities are higher in developed markets. Therefore, it is further concluded that the mean reversion process is much faster in emerging indices except the South Korean and Chinese markets. The study recommends that if investors want higher returns in a shorter period of time then they should invest in emerging markets

Efficient computation of exposure profiles for counterparty credit risk

Three computational techniques for approximation of counterparty exposure for financial derivatives are presented. The exposure can be used to quantify so-called Credit Valuation Adjustment (CVA) and Potential Future Exposure (PFE), which are of utmost importance for modern risk management in the financial industry, especially since the recent credit crisis. The three techniques all involve a Monte Carlo path discretization and simulation of the underlying entities. Along the generated paths, the corresponding values and distributions are computed during the entire lifetime of the option. Option values are computed by either the finite difference method for the corresponding partial differential equations, or the simulation-based Stochastic Grid Bundling Method (SGBM), or by the COS method, based on Fourier-cosine expansions. In this research, numerical results are presented for early-exercise options. The underlying asset dynamics are given by either the Black–Scholes or the Heston stochastic volatility model. Keywords: Expected exposure; potential future exposure; Bermudan options; Heston; numerical computation; finite differences; stochastic grid bundling method

Dynamic Loss Probabilities and Implications for Financial Regulation

Much of financial regulation and supervision is devoted to ensuring the safety and soundness of financial institutions. Such micro- and macro-prudential policies are almost always formulated as capital requirements, leverage constraints, and other statutory restrictions designed to limit the probability of extreme financial loss to some small but acceptable threshold. However, if the risks of a financial institution\u27s assets vary over time and across circumstances, then the efficacy of financial regulations necessarily varies in lockstep unless the regulations are adaptive. We illustrate this principle with empirical examples drawn from the financial industry, and show how the interaction of certain regulations with dynamic loss probabilities can have the unintended consequence of amplifying financial losses. We propose an ambitious research agenda in which legal scholars and financial economists collaborate to develop optimally adaptive regulations that anticipate the endogeneity of risk-taking behavior

Time‐invariant portfolio strategies in structured products with guaranteed minimum equity exposure

We introduce a new exotic option to be used within structured products to address a key disadvantage of standard time-invariant portfolio protection: the well-known cash-lock risk. Our approach suggests enriching the framework by including a threshold in the allocation mechanism so that a guaranteed minimum equity exposure (GMEE) is ensured at any point in time. To be able to offer such a solution still with hard capital protection, we apply an option-based structure with a dynamic allocation logic as underlying. We provide an in-depth analysis of the prices of such new exotic options, assuming a Heston–Vasicek-type financial market model, and compare our results with other options used within structured products. Our approach represents an interesting alternative for investors aiming at downsizing protection via time-invariant portfolio protection strategies, meanwhile being also afraid to experience a cash-lock event triggered by market turmoils

Application of the Real Options in Engineering Design and Decision Making: Focus on Mine Design and Planning at Operational Level

Flexibility and adaptability are essential for long-term corporate success, and real options (RO) is the preferred tool for analysis. This research argues that uncertainty is a source of value as the opportunities that it presents can be leveraged by having a flexible system. As a contribution to knowledge, a relationship between the beta and flexibility index was derived, RO identification framework for mine operational decision-making was proposed and predictive data analytics was utilised to create managerial flexibility

Stochastic evaluation of deepwater oil prospects in Portugal using Monte Carlo Simulation

Classificação: Q40; C63; O22Portugal, como muitos outros países, é um importador de petróleo, porém, é um dos poucos países com potencial para grandes descobertas de óleo, ao longo de sua zona exclusiva econômica inexplorada. O potencial é justificado a partir da semelhança geológica, com as recentes descobertas em águas profundas, no outro lado do Oceano Atlântico (Brasil, Golfo do México e do Canadá Oriental). Este estudo executa a metodologia de avaliação estocástica, para estimar o potencial petrolífero das concessões de águas profundas, na Bacia de Peniche e Alentejo. O ciclo de vida de um projeto no sector de Oil&Gas, incorpora incertezas importantes relacionadas com rendimentos e custos. As principais incertezas prendem-se com os Volumes Recuperáveis de óleo, com as Taxas de Produção, o preço do Brent e com a estrutura de Capex&Opex. Os Volumes de óleo recuperáveis são calculados e enquadrados de acordo com o software geológico, GeoEx, que expõe uma Distribuição LogNormal estatisticamente relevante. As Taxas de Produção anuais são aleatoriamente selecionadas a partir de distribuições históricas de reservatórios semelhantes. O preço do Brent, é estimado num processo de Reversão à Média com difusão de Saltos, a partir do modelo Browniano Geométrico. Finalmente, a estrutura de Capex&Opex segue uma Distribuição Triangular, estimada por gestores de projeto experientes. O ajuste das incertezas é anterior à modelagem subjacente das principais variáveis que afectam os Cash Flows na simulação de Monte Carlo. A simulação preserva a natureza estocástica para os Cash Flows, uma vez que é uma soma de variáveis aleatórias. No final do estudo, todos os NPV’s simulados são dados numa Distribuição Densidade de Probabilidade que expressa a probabilidade do valor económico da Bacia de Peniche e Alentejo.Portugal, like many other countries, is an importer of oil resources, however is one of the few countries, with potential for large oil discoveries along its unexplored exclusive economic zone. The potential is justified from the geological similarity with the recent deepwater discoveries, on the other side of the Atlantic Ocean (Brazil, Gulf of Mexico and Eastern Canada). This study performs a Stochastic Evaluation methodology, for assessing the oil potential of the deepwater concessions located in the Peniche and Alentejo Basins. A project life cycle in the Oil&Gas sector, considers important uncertainties related to yields and costs. The main uncertainties are related to Recoverable Oil Volumes, Production rates, the Brent Price and the Capex&Opex structure. Recoverable Oil Volumes are calculated and framed according to the geological software, GeoEx, which discloses a statistically relevant LogNormal Distribution. The yearly Production rates are randomly selected from historical distributions derived from similar reservoirs. The Brent price is forecasted in a Mean Reversing process with Jumps diffusion from the Geometric Brownian model. Finally, the Capex&Opex structure follows a Triangular Distribution estimated by experienced project managers. The uncertainty adjustment is prior to the modeling of the main variables that distress Cash Flows from the Monte Carlo Simulation. The simulation preserves the stochastic nature to the Cash Flows, since it is a sum of random variables. At the end of the study, all possible NPV’s are given as Probability Density Distribution that expresses the probability of the economic value for the Peniche and Alentejo Basin