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The purpose of this paper is to analyze the dependence between the Chinese and Market Integrated Latin America (MILA) stock markets.

The authors adjust the multivariate probability distribution Variance Gamma (VG) on data yields from the Hang Seng Index (HSI) and MILA and they use the estimated parameters under VG to find a robust estimator of the correlation matrix yields.

The degree of dependence between stock indices from China, Peru, Mexico, Colombia and Chile. In addition, the impact of the change in the HSI affects mostly the movements of the selective stock price index (IPSA) and equally affects the index of the Mexican stock exchange (IPC) and Lima Stock Exchange (S&P/BVL). The effect on index of the Colombia Stock Exchange (COLCAP) is not significant.

Over time there are different structural changes so the time has been restricted to the years 2000-2015, but could extend the analysis to other time periods and sectors of listed companies in the indices.

The results can guide policy makers to assess the effect of a random crash on stock markets and measure the level of risk from other markets.

The results can generate a greater understanding of the relationship between the stock markets of China and the emerging countries of Latin America.

The value of this paper is to focus on alternative methodology to calculate the correlation matrix yields and measure the dependence between the Chinese and MILA stock markets.

This paper aims to construct and compare various total‐return world stock indices based on daily data.

Because of diversification, these indices are noticeably similar. A diversification theorem identifies any diversified portfolio as a proxy for the growth optimal portfolio.

The paper constructs a diversified world stock index that outperforms a number of other indices and argues that it is a good proxy for the growth optimal portfolio.

The diversified world stock index has applications to derivative pricing and investment management.

The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Lévy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices.

The authors apply tail-based and Lévy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Lévy models. As the Lévy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs.

Both EV and Lévy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance.

To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Lévy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

The payoffs of exotic options (e.g., up‐and‐out call options) are dependent on the time‐path of asset prices rather than the price of the asset at a fixed point in time. The authors of this article compare various models for calibrating volatility surfaces in order to price up‐and‐out call options.

Asset allocation has primarily focused its attention on attaining mean variance efficiency by employing diversification strategies following the portfolio selection methodologies of Markowitz[1952]. These are important principles that have given rise to a large variety of diversified investment choices in mutual funds that now outnumber the available choices for investment in stocks. Paralleling this development has been a growing interest in the second odd moment describing returns, the level of skewness. The empirical stability of return skewness has been noted in Beedles and Simkowitz[1980]. Earlier, the importance of skewness for portfolio selection was studied by Arditi and Levy[1975] and Kraus and Litzenberger[1976]. More recently, motivated by the persistence skews observed in option markets (Bates[1991]), Bakshi Kapadia, and Madan [2000] take up the issue of studying the links between the statistical and risk neutral skews, while Harvey and Siddique[1999] address the asset pricing implications of investor preferences for skewness. Evidence is also presented by Carr, Geman, Madan, and Yor [2000] that the primary model for diversified returns is that of a pure jump return process reflecting both, excess kurtosis and skewness.

It is widely accepted that equity return volatility increases more following negative shocks rather than positive shocks. However, much of value‐at‐risk (VaR) analysis relies on the assumption that returns are normally distributed (a symmetric distribution). This article considers the effect of asymmetries on the evaluation and accuracy of VaR by comparing estimates based on various models.

This paper studies the pricing of collateralized debt obligation (CDO) using Monte Carlo and analytic methods. Both methods are developed within the framework of the reduced form model. One-factor Gaussian Copula is used for treating default correlations amongst the collateral portfolio. Based on the two methods, the portfolio loss, the expected loss in each CDO tranche, tranche spread, and the default delta sensitivity are analyzed with respect to different parameters such as maturity, default correlation, default intensity or hazard rate, and recovery rate. We provide a careful study of the effects of different parametric impact. Our results show that Monte Carlo method is slow and not robust in the calculation of default delta sensitivity. The analytic approach has comparative advantages for pricing CDO. We also employ empirical data to investigate the implied default correlation and base correlation of the CDO. The implication of extending the analytical approach to incorporating Levy processes is also discussed.

This paper aims to test three parametric models in pricing and hedging higher-order moment swaps. Using vanilla option prices from the volatility surface of the Euro Stoxx 50 Index, the paper shows that the pricing accuracy of these models is very satisfactory under four different pricing error functions. The result is that taking a position in a third moment swap considerably improves the performance of the standard hedge of a variance swap based on a static position in the log-contract and a dynamic trading strategy. The position in the third moment swap is taken by running a Monte Carlo simulation.

This paper undertook empirical tests of three parametric models. The aim of the paper is twofold: assess the pricing accuracy of these models and show how the classical hedge of the variance swap in terms of a position in a log-contract and a dynamic trading strategy can be significantly enhanced by using third-order moment swaps. The pricing accuracy was measured under four different pricing error functions. A Monte Carlo simulation was run to take a position in the third moment swap.

The results of the paper are twofold: the pricing accuracy of the Heston (1993) model and that of two Levy models with stochastic time and stochastic volatility are satisfactory; taking a position in third-order moment swaps can significantly improve the performance of the standard hedge of a variance swap.

The limitation is that these empirical tests are conducted on existing three parametric models. Maybe more critical insights could have been revealed had these tests been conducted in a brand new derivatives pricing model.

This work is 100 per cent original, and it undertook empirical tests of the pricing and hedging accuracy of existing three parametric models.