Following the Basel Committee’s advocacy of value-at-risk (VaR) disclosure in external reports of financial institutions, the U.S. Securities and Exchange Commission issued Financial Reporting Release No. 48 to permit VaR disclosure as one of the most important disclosure approaches for market-risk quantitative information in 1997. This study is the first to empirically examine both economic determinants and consequences of VaR disclosure informativeness in the banking industry. First, this study finds that more informative VaR disclosure is associated with more effective corporate governance characteristics, including better shareholder protection, a larger and more independent board, the presence of a separate risk committee under the board of directors, a more independent risk committee, higher institutional ownership and a better overall governance environment. These results suggest that corporate governance mechanisms are important determinants of the informativeness of VaR disclosure. Second, the evidence shows that the cost of equity capital is negatively associated with the informativeness of VaR disclosure, consistent with informative VaR disclosure effectively communicating private information to investors about a bank’s market risk exposure and its risk management system. Additional evidence during the recent crisis further suggests the importance of VaR disclosure informativeness to the capital market as a strong signal reflecting the efficacy of risk management practices and the quality of risk governance mechanisms. However, I still find that a large proportion of the sample banks choose not to disclose information with respect to some important disclosure items (e.g., quantitative stress-test results, and non-trading portfolio VaR). It is necessary for government regulators to re-consider the current regulation on VaR disclosure in the external reports of the banking industry.

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the  used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model.

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In recent year, the securities firms had suffered form the turmoil of the financial crisis in Taiwan. Although the Taiwan Stock Exchange Corporation and the Securities and Futures Commission have their own early warning systems (EWS), the EWS based on financial statements and the "capital adequacy ratio", respectively for the risks that the brokers and dealers assume, still have some defects: (1) EWS based on financial statements are static and time-lagged in the rapid-moving market, and (2) the calculation rules in the capital adequacy ratio are inelastic and inefficient. This research emphasizes on the stock positions of the dealers, and calculate the "Value at Risk" (VaR) for these positions. In this way, we hope to know whether the EWS based on VaR can detect the risks of the dealers in time, and improve the drawbacks of the EWS based on financial statements and capital adequacy ratio. We found that: (1) the EWS based on VaR can effectively reflect the market risk of the dealers, and (2) the "historical simulation" method might distort the real portfolio risk, thus we suggest that "delta-normal" is a better method, and (3) the EWS based on VaR can discriminate the risk level of different securities dealers. In conclusion, we have the suggestion of the EWS for securities firms in the future. For firm-wide operation, the EWS based on financial statements is suitable; for the credit risks the securities firms may assume, the capital adequacy ratio is better; as for the market risk of the positions, VaR, undoubtedly, is a good alternative.

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The aim of this thesis is to examine performance and relationship between the US and Asian REITs indices. We find two-year (2005/3/10~2007/3/12) return of T-REITs is 15.87%, which is much lower than the return of US, Japan and Singapore. However, T-REITs has the lowest risk in selected sample countries because the lowest VaRs is found. We estimate one-day horizon holding periods VaRs and find T-REITs¡¦ performance is better than other country by the Sharpe Ratio of VaRs. The Granger causality approach indicates some lead-lag relationships between these REITs. The NAREIT EQUITY index is leading the Hong-Kong and Singapore REITs indices; Singapore REITs index is leading the J-REITs index; J-REITs index is leading the NAREIT EQUITY index. However, Causality tests show no significant lead-lag relationships between Taiwan REITs market and other REITs markets.

¡@¡@This study aims to investigate the theories and empirical performance of the futures and options margin systems currently used in the domestic and international exchange houses. The current system used in Taiwan Futures Exchange (TAIFEX) is strategy-based rather than portfolio-based or and contract risk-based. It is no longer compliant with the development of the futures market. Therefore, it is suggested that TAIFEX should employ international experiences to adopt a portfolio-based and VaR-based margin system so as to meet the need of the local trading feature that portfolios contain both stock futures and stock options. ¡@¡@This study integrates scenario simulation and the diagonal model to propose a new model, called Beta-Simulation, to calculate the margins for portfolios containing stock options, index futures, and stocks. The proposed model can not only simplify the inter-commodity spread in SPAN but also theoretically improve the drawback of TIMS of using a simple credit offset multiplier. In the empirical test, back testing is performed on the margins calculated by Beta-Simulation with historic data of portfolios with stock options, and other common margin systems are also included in the test for comparison. ¡@¡@The empirical results reveal that only SPAN and Beta-Simulation can save approximately 12%~42% margin requirements for portfolios containing stock options, but under the same protection degree, Beta-Simulation requires significantly lower margins and a simpler calculation process than SPAN. Therefore, the proposed model is a better model of calculating margins and VaR for portfolios containing stock options.

Inline with the third pillar of the Basel accords, quantitative market risk measurements are investigate and evaluated comparing JP Morgan’s RiskMetrics and Bollerslev’s GARCH with the Peek over Threshold and Block Maxima approaches from the Extreme Value Theory framework. Value-at-Risk and Expected Shortfall (Conditional Value-at-Risk), with 95% and 99% confidence, is predicted for 25 years of the OMXS30. The study finds Bollerslev’s suggested t distribution to be a more appropriate distributional assumption, but no evidence to prefer the GARCH to the RiskMetrics. The more demanding Extreme Value Theory procedures trail behind as they are found wasteful of data and more difficult to backtest and therefore evaluate.

Pastaruoju metu, investavimui tampant vis populiaresniu, atsiranda poreikis skaičiuoti portfelių rizikuojamąją vertę (angl. Value at Risk, toliau tekste VaR). Pastaroji gali būti skaičiuojama portfeliams sudarytiems iš skirtingų finansinių instrumentų. Tačiau iškyla problemų, kai finansiniai instrumentai yra tarpusavyje susiję (priklausomi). Šiai situacijai išspręsti naudojame VaR, kuris skaičiuojamas jungčių (angl. Copula) pagalba. Darbo tikslas – nagrinėjamiems portfeliams parinkti jungtis, kurios geriausiai atspindėtų bendrą duomenų pasiskirstymą. Tada, turint jungtis, apskaičiuoti VaR. Gavome, kad vertinant 1 portfelį ateinančiu laiko momentu mūsų didžiausias tikėtinas nuostolis yra intervale tarp 4.34 ir 4.70 litų. 2 portfelio nuostolis yra intervale (2.88, 3.42), 3 portfelio – (3.29, 5.28 ). / Recently, investments acquire vogue and it’s necessary to compute the Value at Risk of portfolio. VaR can be computed for portfolio which is made from different finance instruments. But the problem arises when these instruments are interdependent. In order to solve this problem, we compute VaR using copulas. The aim of this work is to pick copulas for real data which is the best for the distribution of the data. At that point compute VaR using selected copulas. The results are: in future time the biggest loss for first portfolio is in the interval 4.43 ant 4.7 Litas, for second portfolio the biggest loss – (2.88, 3.42) ant for third portfolio – (3.29, 5.28).

The measurement of operational risk has surely been one of the biggest challenges for banks worldwide. Most banks worldwide have opted for a value-at-risk (VaR) approach, based on the success achieved with market risk, to measure and quantify operational risk. The problem banks have is that they do not always find it difficult to calculate this VaR figure, as there are numerous mathematical and statistical methods and models that can calculate VaR, but they struggle to understand and interpret the values that are produced by VaR models and methods. Senior management and normal staff do not always understand how these VaR values will impact their decision-making and they do not always know how to incorporate these values in their day-to-day management of the bank. This study therefore aims to explain and discuss the calculation of VaR for operational risk as well as the factors that influence this figure, and then also to discuss how this figure is managed and the impact that it has on the management of a bank. The main goal of this study is then to explain the management of VaR for operational risk in order to understand how it can be incorporated in the overall management of a bank. The methodology used includes a literature review, in-depth interviews and a case study on a South African Retail Bank to determine and evaluate some of the most renowned methods for calculating VaR for operational risk. The first objective of this study is to define operational risk and all its elements in order to distinguish it from all the other risks the banking industry faces and to better understand the management thereof. It is the view of this study that it will be impossible to manage and measure operational risk if it is not clearly defined, and it is therefore important to have a clear and understandable definition of operational risk. The second objective is to establish an operational risk management process that will ensure a structured approach to the management of operational risk, by focusing on the different phases of operational risk. The process discussed by this study is a combination of some of the most frequent used processes by international banks, and is intended to guide the reader in terms of the steps required for managing operational risk. The third objective of this study is to discuss and explain the qualitative factors that play a role in the management of operational risk, and to determine where these factors fit into the operational risk process and the role they play in calculating the VaR for operational risk. These qualitative factors include, amongst others, key risk indicators (KRIs), risk and control self-assessments and the tracking of operational losses. The fourth objective is to identify and evaluate the quantitative factors that play a role in the management of operational risk, to distinguish these factors from the qualitative factors, and also to determine where these factors fit into the operational risk management process and the role they play in calculating VaR for operational risk. Most of these quantitative factors are prescribed by the Base1 Committee by means of its New Capital Accord, whereby this new framework aims to measure operational risk in order to determine the amount of capital needed to safeguard a bank against operational risk. The fifth objective is to discuss and explain the calculation of VaR for operational risk by means of discussing all the elements of this calculation. This study mainly bases its discussion on the loss distribution approach (LDA), where the frequency and severity of operational loss events are convoluted by means of Monte Carlo simulations. This study uses real data obtained from a South African Retail Bank to illustrate this calculation on a practical level. The sixth and final objective of this study is to explain how VaR for operational risk is interpreted in order for management to deal with it and make proper management decisions based on it. The above-mentioned discussion is predominantly based on the two types of capital that are influenced by VaR for operational risk. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2007.