The Ten Commandments for Optimizing Value-at-Risk and Daily Capital Charges

Credit risk is the most important type of risk in terms of monetary value. Another key risk measure is market risk, which is concerned with stocks and bonds, and related financial derivatives, as well as exchange rates and interest rates. This paper is concerned with market risk management and monitoring under the Basel II Accord, and presents Ten Commandments for optimizing Value-at-Risk (VaR) and daily capital charges, based on choosing wisely from: (1) conditional, stochastic and realized volatility; (2) symmetry, asymmetry and leverage; (3) dynamic correlations and dynamic covariances; (4) single index and portfolio models; (5) parametric, semiparametric and nonparametric models; (6) estimation, simulation and calibration of parameters; (7) assumptions, regularity conditions and statistical properties; (8) accuracy in calculating moments and forecasts; (9) optimizing threshold violations and economic benefits; and (10) optimizing private and public benefits of risk management. For practical purposes, it is found that the Basel II Accord would seem to encourage excessive risk taking at the expense of providing accurate measures and forecasts of risk and VaR.Daily capital charges, Excessive risk taking Market risk, Risk management, Value-at-risk, Violations.

"The Ten Commandments for Optimizing Value-at-Risk and Daily Capital Charges"

Credit risk is the most important type of risk in terms of monetary value. Another key risk measure is market risk, which is concerned with stocks and bonds, and related financial derivatives, as well as exchange rates and interest rates. This paper is concerned with market risk management and monitoring under the Basel II Accord, and presents Ten Commandments for optimizing Value-at-Risk (VaR) and daily capital charges, based on choosing wisely from: (1) conditional, stochastic and realized volatility; (2) symmetry, asymmetry and leverage; (3) dynamic correlations and dynamic covariances; (4) single index and portfolio models; (5) parametric, semiparametric and nonparametric models; (6) estimation, simulation and calibration of parameters; (7) assumptions, regularity conditions and statistical properties; (8) accuracy in calculating moments and forecasts; (9) optimizing threshold violations and economic benefits; and (10) optimizing private and public benefits of risk management. For practical purposes, it is found that the Basel II Accord would seem to encourage excessive risk taking at the expense of providing accurate measures and forecasts of risk and VaR.

The Ten Commandments for Optimizing Value-at-Risk and Daily Capital Charges

Credit risk is the most important type of risk in terms of monetary value. Another key risk measure is market risk, which is concerned with stocks and bonds, and related financial derivatives, as well as exchange rates and interest rates. This paper is concerned with market risk management and monitoring under the Basel II Accord, and presents Ten Commandments for optimizing Value-at-Risk (VaR) and daily capital charges, based on choosing wisely from: (1) conditional, stochastic and realized volatility; (2) symmetry, asymmetry and leverage; (3) dynamic correlations and dynamic covariances; (4) single index and portfolio models; (5) parametric, semiparametric and nonparametric models; (6) estimation, simulation and calibration of parameters; (7) assumptions, regularity conditions and statistical properties; (8) accuracy in calculating moments and forecasts; (9) optimizing threshold violations and economic benefits; and (10) optimizing private and public benefits of risk management. For practical purposes, it is found that the Basel II Accord would seem to encourage excessive risk taking at the expense of providing accurate measures and forecasts of risk and VaR.

Analysing seasonal changes in New Zealand's largest inbound market

The purpose of the paper is to analyse seasonal changes in tourism demand by New Zealand's major tourist source market, Australia, for the period 1979-2005. A time series regression model is used to test the significance of monthly seasonality. By examining sub-periods that are based on major exogenous events which have had significant impacts on international travel demand to New Zealand, seasonal distributions and intra-year seasonal variations over the 27-year period are subsequently estimated using normalized seasonal indices, coefficient of variation, seasonal ratio and the Gini coefficient. Compared with the findings of previous studies for other countries, the empirical evidence suggests that, while the tourism flow distribution or concentration is not significant for New Zealand, the seasonality in tourism demand by New Zealand's largest inbound market has changed over time

Testing Multiple Non-nested Factor Demand Systems,

Empirical factor demand analysis typically involves making a choice from among several competing non-nested functional forms. Each of the commonly used factor demand systems, such as Translog, Generalized Leontief, Quadratic, and Generalized McFadden, can provide a valid and useful empirical description of the underlying production structure of the firm. As there is no theoretical guidance on selecting the model which is best able to capture the relevant features of the data, formal testing procedures can provide additional information. Paired and joint univariate nonnested tests of a null model against both single and multiple alternatives have been discussed at length in the literature, whereas virtually no attention has been paid to either paired or joint multivariate non-nested tests. This paper shows how some multivariate non-nested tests can be derived from their univariate counterparts, and examines how to use these tests empirically to compare alternative factor demand systems. The empirical application involves the classical Berndt- Khaled annual data set for the U.S. manufacturing sector over the period 1947-1971. A statistically adequate empirical specification is determined for each competing factor demand system. The empirical results are interpreted for each system, and the models are compared on the basis of multivariate paired and joint non-nested procedures.

"Dynamic Conditional Correlations for Asymmetric Processes"

The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents an empirical example using the trivariate data of the Nikkei 225, Hang Seng and Straits Times Indices for estimating and forecasting the WDCC-EGARCH and WDCC-GJR models, and compares the performance with the asymmetric BEKK model. The empirical results show that AIC and BIC favour the WDCC-EGARCH model to the WDCC-GJR and asymmetric BEKK models. Moreover, the empirical results indicate that the WDCC-EGARCH-t model produces reasonable VaR threshold forecasts, which are very close to the nominal 1% to 3% values.

A Scientific Classification of Volatility Models.

Modeling volatility, or “predictable changes” over time and space in a variable, is crucial in the natural and social sciences. Life can be volatile, and anything that matters, and which changes over time and space, involves volatility. Without volatility, many temporal and spatial variables would simply be constants. Our purpose is to propose a scientific classification of the alternative volatility models and approaches that are available in the literature, following the Linnaean taxonomy. This scientific classification is used because the literature has evolved as a living organism, with the birth of numerous new species of models.

Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers,

This paper investigates several empirical issues regarding quasimaximum likelihood estimation of Smooth Transition Autoregressive (STAR) models with GARCH errors, specifically STAR-GARCH and STAR-STGARCH. Convergence, the choice of different algorithms for maximising the likelihood function, and the sensitivity of the estimates to outliers and extreme observations, are examined using daily data for S&P 500, Heng Seng and Nikkei 225 for the period January 1986 to April 2000.

"Input-output Structure and Growth in China"

The fast and steady economic growth in China during the 1990s has attracted much international attention. Using the three most recent Chinese input-output tables, this paper investigates industry structure and inter-industry relationships and the relationship of both to economic growth. The input-output tables contain intermediate demand and final demand for six broad industries, namely, Agriculture, Industry, Construction, Transportation, Post and Telecommunications, Services, and Other, for 1992, 1995 and 1997, which enables computing of input-output coefficients for three time periods. As direct and indirect input-output coefficients characterise industry structure during a particular time period, changes over time reflect the patterns in industry structure evolvement. Furthermore, output growth in a particular industry can be analysed from two different sources, namely the changes in input-output coefficients that reflect technological change, and the change in final demand. This paper sheds light on four different issues over the five-year period from 1992 to 1997: (1) Was growth driven by technological changes or final demand increases? (2) As a result of the interdependence of industries, how did an increase in final demand in one industry affect growth in another? (3) How has the bottleneck of an insufficient capability in the Transportation, Post and Telecommunications sector to cope with demands from other sectors been affected during this period? (4) Has the industry structure of the economy been shifting in conformity with traditional growth theory, namely with a decline in the agricultural sector and a rise in the modern industrial sector?

Dynamic Conditional Correlations in International Stock, Bond and Foreign Exchange Markets: Emerging Markets Evidence

The paper models the dynamic conditional correlations in emerging stock, bond and foreign exchange markets using the DCC model of Engle (2002) and the GARCC model of McAleer et al. (2008). The highly restrictive DCC model suggests that the conditional correlations of the overall returns are constant. In contrast, the GARCC model finds that the conditional correlations between bond-bond markets and between stock-stock markets are relatively constant across developed-emerging markets, while those between emerging-emerging markets are dynamic. The conditional correlations between stock-bond markets across developed-emerging markets are also more dynamic as compared with those between emerging-emerging markets.