Recent accounting scandals and current global financial crisis have brought new demands on the whole corporate world. The call for better corporate governance is strengthening in all business areas including tax. Tax non -- compliance brings substantial risks for both tax payers and tax revenue authorities. The way how companies manage their tax risks can significantly influence their overall financial performance and reputation. The paper deals with issues of tax non -- compliance as a lack of good corporate governance practice. The main goal of the paper is to put tax into the concept of corporate governance. Moreover, the paper deals with the concept of tax risk management as a way of how tax compliance in general could be enhanced and introduces the current international practice in this field.

In this dissertation the theory of solvency assessment for long-term insurers is reviewed and how it evolved. Key international bodies and standards are identified and selected jurisdictions’ solvency frameworks are reviewed. The South African framework required by legislation introduced in 1998 is compared to these standards. Solvency capital requirements, valuation methods and risk management standards are the key areas considered. The financial results of a model office according to the South African requirements are compared to the financial results modelled stochastically according to the identified international standards. It is shown that the South African framework does not meet international standards. The key problem areas are the prescribed nature of the solvency capital requirement, the onerous treatment of policy cancellations and the treatment of new business. The role of actuaries in solvency assessment is also investigated. South Africa’s statutory-actuary role is compared with similar international roles. It is concluded that although similar international roles, notably the appointed actuary of the UK, have evolved the role of the statutory actuary has remained the same.

Counterparty credit risk (CCR) is the risk that a counterparty in a deal will not be able to meet their contractual obligations in the future. While CCR is an important task for any risk desk, it has often been underestimated due to the miss-conception that some counterparties were deemed to be either too big to fail or too big to be allowed to default. This was highlighted by the 2008 nancial crisis that saw respected banks, such as Lehman Brothers, and nancial service providers, such as AIG, default on their obligations. Since then there has been renewed interest in CCR, with the focus being on actively pricing and hedging it. In this work CCR is invistigated including its intersection with other forms of risk. CCR mitigation techniques are explored, followed by the formal quanti cation of CCR in the form of credit value adjustments (CVA). The analysis of CCR is then applied to interest rate derivatives, more speci cally forward rate agreements (FRAs) and interest rate swaps (IRSs). The e ect of correlation on unilateral and bilateral CVA between counterparties, including risk factors such as the interest rate, is investigated. This is invistigated under two credit risk modelling frameworks, the structural and intensity based frameworks. It is shown that correlation has a none-negligible e ect on both unilateral and bilateral CVA for FRAs and IRSs. Correlation structures, namely the Gaussian and the Student-t copula, are used to induce dependency in order to understand their e ect on both unilateral and bilateral CVA. It is shown that the choice of copula does not have signi cant e ect on either unilateral or bilateral CVA.

University of Macau / Faculty of Science and Technology. / Department of Civil and Environmental Engineering

This thesis studies two subareas within the forecasting literature: volatility forecasting and risk-neutral density estimation and asks the question of how accurate volatility forecasts and risk-neutral density estimates can be made based on the given information. Two sources of information are employed to make those forecasts: historical information contained in time series of asset prices, and forward-looking information embedded in prices of traded options. Chapter 2 tests the comparative performance of two volatility scaling laws - the square-root-of-time (√T) and an empirical law, TH, characterized by the Hurst exponent (H) - where volatility is measured by sample standard deviation of returns, for forecasting the volatility term structure of crude oil price changes and ten foreign currency changes. We find that the empirical law is overall superior for crude oil, whereas the selection of a superior model is currency-specific and relative performance substantially differs across currencies. Our results are particularly important for regulatory risk management using Value-at-Risk and suggest the use of empirical law for volatility and quantile scaling. Chapter 3 studies the predictive ability of corridor implied volatility (CIV) measure. By adding CIV measures to the modified GARCH specifications, we show that narrow and mid-range CIVs outperform the wide CIVs, market volatility index and the BlackScholes implied volatility for horizons up to 21 days under various market conditions. Results of simulated trading reinforce our statistical findings. Chapter 4 compares six estimation methods for extracting risk-neutral densities (RND) from option prices. By using a pseudo-price based simulation, we find that the positive convolution approximation method provides the best performance, while mixture of two lognormals is the worst; In addition, we show that both price and volatility jumps are important components for option pricing. Our results have practical applications for policymakers as RNDs are important indicators to gauge market sentiment and expectations.

by Chan Pui Man. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 108-111). / ABSTRACT --- p.II / TABLE OF CONTENTS --- p.III / ACKNOWLEDGEMENT --- p.VI / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.4 / Impact due to Deregulation --- p.5 / Impact due to Globalization --- p.5 / Impact due to Securitization --- p.6 / Impact due to Institutionalisation --- p.6 / Impact due to Computerisation --- p.7 / Chapter III. --- CONCEPT: MANAGEMENT OF RISK --- p.8 / Definition of Risk --- p.9 / Risk Analysis --- p.10 / Risk Assessment --- p.10 / Risk Measurement --- p.10 / Risk Management --- p.11 / Chapter IV. --- TYPE OF RISK --- p.13 / Market/Capital Risk --- p.14 / Reinvestment Risk --- p.15 / Interest Rate Risk --- p.16 / Credit Risk --- p.17 / Liquidity or Funding Risk --- p.18 / Currency and Foreign Exchange Risk --- p.19 / Inflation Risk --- p.19 / Operations Risk --- p.20 / Legal Risk --- p.20 / Political Risk --- p.21 / Systemic Risk --- p.22 / Portfolio Risk --- p.22 / Control Risk --- p.23 / Settlement Risk --- p.23 / Country Risk --- p.24 / Underwriting Risk --- p.24 / Residual or Moral Risk --- p.24 / Strategy Risk and Environment Risk --- p.25 / Chapter V. --- MEASURING CHANGING RISK --- p.26 / Historical Estimates --- p.28 / Non-parametric Methods --- p.29 / Parametric Methods --- p.30 / Chapter VI. --- EVOLUTION OF RISK ESTIMATION --- p.35 / Chapter VII. --- APPLYING PORTFOLIO THEORY INTO RISK ANALYSIS --- p.41 / Modelling Bank Risk --- p.43 / Identification of linkages between an individual loan and bank's overall risk profile --- p.43 / Distribution of expected values --- p.44 / Portfolio expected value --- p.44 / Scenario Analysis and Formation of Loan Risk Measurement --- p.45 / Subsystem --- p.45 / Formation of an Integrated Risk Measurement --- p.45 / Active Management of Portfolio Risk --- p.49 / Chapter VIII. --- RISK ANALYSIS OF INTERNATIONAL INVESTMENT --- p.51 / Discounted-Cash-Flow Analysis --- p.51 / Net Present Value Approach --- p.51 / Internal Rate of Return Approach --- p.54 / Break-even Probability Analysis --- p.55 / Certainty-Equivalent Method --- p.56 / Chapter IX. --- CONSTRUCTING A MODEL FOR RISK ASSESSMENT --- p.58 / "Set up a Model to Estimate ""Capital at Risk""" --- p.58 / Obey the Minimum Standards --- p.60 / Audit and Verify the Model --- p.62 / Chapter X. --- METHODOLOGIES OF RISK MEASUREMENT / Measuring Market Risk : J P Morgan Risk Management Methodology - RiskMetrics´ёØ --- p.64 / Statistical Analysis of Returns and Risk --- p.66 / Market Moves and Locally Gaussian Processes --- p.72 / Stochastic Volatility --- p.72 / Risk and Optionality --- p.73 / Mapping and Term Structure of Interest Rates --- p.73 / Measuring Position Risk --- p.75 / The Simplified Portfolio Approach --- p.77 / The Comprehensive Approach --- p.81 / The Building-Block Approach --- p.83 / Chapter XI. --- ITEMS INVOLVED IN RISK MANAGEMENT --- p.85 / Management Control --- p.35 / Constructing Valuation Methodology --- p.90 / Contents of Reporting --- p.92 / Evaluation of Risk --- p.93 / Counterparty Relationships --- p.93 / Chapter XII. --- AFTERTHOUGHT --- p.95 / APPENDIX --- p.98 / BIBLIOGRAPHY --- p.108

Chan Wing-Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 43-45). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation of Volatility --- p.5 / Chapter 2.1 --- A revisit to the RiskMetrics --- p.6 / Chapter 2.2 --- Predicting Multiple-period of Volatilities --- p.7 / Chapter 2.3 --- Performance Measures --- p.11 / Chapter 2.4 --- Nonparametric Estimation of Quantiles --- p.13 / Chapter 3 --- Univariate Prediction --- p.15 / Chapter 3.1 --- Piecewise Constant Technique --- p.16 / Chapter 3.2 --- Piecewise Linear Technique --- p.22 / Chapter 4 --- Bivariate Prediction --- p.27 / Chapter 4.1 --- Model Selection --- p.28 / Chapter 4.2 --- Piecewise Linear with Discontinuity --- p.29 / Chapter 4.3 --- Piecewise Linear Technique --- p.35 / Chapter 5 --- Conclusions --- p.41 / Bibliography --- p.43

Li Chin Pang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 64-66). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Structural Models of Credit Risk --- p.7 / Chapter 2.1 --- Barrier-Independent Models --- p.8 / Chapter 2.2 --- Barrier-Dependent Models --- p.9 / Chapter 2.3 --- Empirical Literature --- p.10 / Chapter 3 --- Jump-Diffusion Models --- p.13 / Chapter 3.1 --- Analytical Option Pricing Formula --- p.14 / Chapter 3.1.1 --- The Jump-Diffusion Model of Merton --- p.14 / Chapter 3.1.2 --- The Jump-Diffusion Model of Kou --- p.15 / Chapter 3.2 --- Simulation for Options --- p.19 / Chapter 3.2.1 --- Simulation for Barrier-Independent Options --- p.19 / Chapter 3.2.2 --- Brownian Bridge Simulation for DOC Option --- p.20 / Chapter 4 --- Likelihood Function for Equity Returns --- p.24 / Chapter 4.1 --- Likelihood Function on Equity Return --- p.26 / Chapter 4.2 --- Degeneracy Problem of Likelihood Function --- p.27 / Chapter 5 --- The Proposed Framework --- p.31 / Chapter 5.1 --- Penalized Likelihood Estimation --- p.31 / Chapter 5.2 --- Expectation-Maximization Algorithm --- p.36 / Chapter 5.3 --- The MJD Structural Model --- p.41 / Chapter 5.4 --- The K<JD Structural Model --- p.43 / Chapter 5.5 --- Computation of the E-step --- p.47 / Chapter 6 --- Performance of Estimation --- p.49 / Chapter 6.1 --- Simulation Checks --- p.49 / Chapter 6.2 --- Empirical Performance --- p.55 / Chapter 6.2.1 --- Bond Selection --- p.55 / Chapter 6.2.2 --- Empirical Results --- p.57 / Chapter 7 --- Conclusion --- p.62 / Bibliography --- p.64

This thesis focuses on the relationship between the real economy and the financial sector which gives rise to various amplification mechanisms known as financial accelerators. Historically, those channels are known to be in the roots of the world's largest crises such as the 2008 Great Recession. In its aftermath, policy-makers have undertaken various reforms that introduce macroprudential policy which focuses on the stability of the financial system as a whole. This thesis studies different financial amplification channels and the ability of macroprudential policy to mitigate their impact on the real economy in three chapters. The first chapter introduces different macroprudential tools into a macroeconomic framework with financial frictions and analyses their ability to mitigate the impact of a crisis originating from the financial sector to the real economy. The main finding of the paper is that sector specific tools can be effective if applied before the occurrence of the crisis, however, broader tools are much more effective once the crisis has spread to the economy. The second chapter expands the framework of the previous one, in order to provide a realistic representation of the current regulatory setting for capital requirements - the Internal Rating Based approach. The paper then studies the ability of the regulation to lead to procyclical capital requirements and thus amplify the business cycle and reduce social welfare. In order to avoid these consequences, an alternative policy rule is proposed which is able to mitigate the amplification effects. The third chapter focuses on the founding theory behind the current regulatory framework - the portfolio loss distribution (Vasicek, 2002) and expands it by introducing macroeconomic amplification mechanisms known as financial accelerators. The resulting portfolio distribution shows large losses to be substantially more likely which increases the fragility of the financial system and the amount of capital necessary to maintain its stability.

現代風險管理需要對金融產品的相關結構做出刻畫，而在實際生活中，我們通常使用相關係數和自相關係數去刻畫這種結構。然而，越來越多的人意識到自相關函數在度量相關結構上面被高估了，特別是在風險管理中我們更關心極端事件。同樣的，偏自相關函數也有這樣的短板。在這篇論文中，我們在有限維分佈服從有正尾係數的正則變差的嚴平穩過程上定義了Partial Extremogram。 這個指標僅僅依賴於隨機過程中的極端值。我們給出了它的一個估計并且研究了這個估計的漸進性質。此外，为了刻畫时间序列的負相關結構，我們把 Inverse Tail Dependence 的想法推廣到了隨機過程上面並且引入了Inverse Extremogram 的概念。我們給出了Inverse Extremogram 在ARMA模型中的顯示表達式。理論推導和數據模擬都說明這個指標可以很好的刻畫出一個隨機過程的尾部的負相關結構。 / Modern risk management calls for deeper understanding of the dependence structure of financial products, which is usually measured by correlation or autocorrelation functions. More and more people realized that autocorrelation function is overvalued as a tool to measure dependence, especially when one has to deal with extremal events in risk management. Likewise, partial autocorrelation function also suffers similar shortcomings as autocorrelation function. In this thesis, an analog of the partial autocorrelation function for a strictly stationary sequence of random variables whose finite-dimensional distributions are jointly regularly varying with positive index, the partial extremogram, is introduced. This function only depends on the extremal events of the underlying process. A natural estimator of the partial extremogram is also proposed and its asymptotic properties are studied. Furthermore, to measure the negative dependence of a time series, the idea of inverse tail dependence is extended to a stochastic process and the notion of inverse extremogram is proposed. A closed form of the inverse extremogram for an ARMA model is deduced. The theoretical and simulation results show that the inverse extremogram is a useful tool for measuring the negative tail dependence of a process. / Detailed summary in vernacular field only. / Chen, Pengcheng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 53-56). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Tail Dependence --- p.2 / Chapter 1.2 --- Extremogram --- p.4 / Chapter 1.2.1 --- Regularly Varying Time Series --- p.4 / Chapter 1.2.2 --- Extremogram for Regularly Varying Time Series --- p.7 / Chapter 1.3 --- Motivation and Organization --- p.8 / Chapter 2 --- Partial Extremogram --- p.9 / Chapter 2.1 --- Definition of Partial Extremogram --- p.9 / Chapter 2.2 --- Applications of Partial Extremogram --- p.15 / Chapter 2.2.1 --- AR(1) Process --- p.15 / Chapter 2.2.2 --- MA(1) process --- p.17 / Chapter 2.2.3 --- Stochastic Volatility Model --- p.19 / Chapter 2.3 --- Estimation of Partial Extremogram --- p.19 / Chapter 2.4 --- Simulation Study --- p.22 / Chapter 3 --- Inverse Extremogram --- p.28 / Chapter 3.1 --- Definition of Inverse Extremogram --- p.28 / Chapter 3.2 --- Applications of Inverse Extremogram --- p.29 / Chapter 3.2.1 --- MA(q) Model --- p.29 / Chapter 3.2.2 --- MA(∞) Model --- p.35 / Chapter 3.2.3 --- ARMA Model --- p.40 / Chapter 3.2.4 --- GARCH Model and SV Model --- p.41 / Chapter 3.3 --- Simulation Study --- p.42 / Chapter 4 --- Conclusions and Further Research --- p.50 / Bibliography --- p.53