The perils of parody : joking with stereotypes in a postcolonial context in Cien años de soledad

This article examines the comic treatment of the characters in Cien años de soledad. It challenges the previous studies of the novel’s comedy, which have tended to conclude that its comic formulas are necessarily subversive and carnivalesque. Employing Susan Purdie’s theory of comedy and Edward Said’s, Homi Bhabha’s and Walter Mignolo’s postcolonial theorisation of othering, the article analyses the ways in which Gabriel García Márquez’s comic depictions function as parodic exaggerations of certain stereotypical representations of the Latin American “other.” However, it is also argued that the diffuse nature of parody and of the technique of exaggeration open up these jokes to multiple readings. Moreover, according to Stuart Hall’s and Richard Dyer’s work on stereotypes, references to the “other” in jokes or in other types of narratives inevitably enter into an intertextual dialogue with established discourses, over which the author or text has little control. In this respect, the article aims to map the possible meanings of these jokes within the context of the novel’s global popularity. It investigates the possibility that certain comic representations of the “other” in Cien años de soledad run the risk of confirming certain discourses just as much as they have the potential to subvert them

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.risk management;value-at-risk;violations;dail;market risk;y capital charges;excessive risk taking

"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

Forecasting Realized Volatility with Linear and Nonlinear Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.neural networks;nonlinear models;financial econometrics;realized volatility;bagging;volatility forecasting

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.