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The purpose of this paper is to study the scope for country diversification in international portfolios of mutual funds for the “core” EMU countries. The author uses a sample of daily returns for country indices of French, German and Italian funds to investigate the quest for international diversification. The author focuses on fixed-income mutual funds during the period of the financial market turmoil since 2007.

The author compute optimal portfolio allocations from both unconstrained and constrained mean-variance frameworks that take as input the out-of-sample forecasts for the conditional mean, volatility and correlation of country-level indices for funds returns. The author also applies a portfolio allocation model based on utility maximization with learning about the time-varying conditional moments. The author compares the out-of-sample forecasting performance of 12 multivariate volatility models.

The author finds that there is a “core” EMU country also for the mutual fund industry: optimal portfolios allocate the largest portfolio weight to German funds, with Italian funds assigned a lower weight in comparison to French funds. This result is remarkably robust across competing forecasting models and optimal allocation strategies. It is also consistent with the findings from a utility-maximization model that incorporates learning about time-varying conditional moments.

This is the first study on optimal country-level diversification for a mutual fund investor focused on European countries in the fixed-income space for the turmoil period. The author uses a large array of econometric models that captures the salient features of a period characterized by large changes in volatility and correlation, and compare the performance of different optimal asset allocation models.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.

One of the key elements of survival models is that they enable the researcher to determine whether the length of time an individual (or economic entity) spends in a particular state affects the probability of exiting that state. Natural applications in economics and finance include the analysis of unemployment spells, corporate bankruptcies and mortgage pre‐payments. The distinguishing feature of most applications is the definitive event that marks the transition from the origin to the transition state. We believe that limiting the use of survival analysis to applications in which the event duration appears to be ‘naturally’ available is an unnecessary constraint. For example, the date of emergence from Chapter 11 bankruptcy protection is a subjective management decision and the true event duration, though treated as definitive, is in reality quite ambiguous. We propose that survival models can and should be extended to analyze researcher‐defined events such as the length of time a stock takes to reach a preset price target. We illustrate our point with an examination of IPO aftermarket behavior.

Safety improvement and pollutant reduction in many practical combustion systems and especially in aero-gas turbine engines require an adequate understanding of flame ignition and stabilization mechanisms. Improved software and hardware have opened up greater possibilities for translating basic knowledge and the results of experiments into better designs. The present study deals with the large eddy simulation (LES) of an ignition sequence in a conical shaped bluff-body stabilized burner involving a turbulent non-premixed flame. The purpose of this paper is to investigate the impact of spark location on ignition success. Particular attention is paid to the ease of handling of the numerical tool, the computational cost and the accuracy of the results.

The discrete particle ignition kernel (DPIK) model is used to capture the ignition kernel dynamics in its early stage of growth after the breakdown period. The ignition model is coupled with two combustion models based on the mixture fraction-progress variable formulation. An infinitely fast chemistry assumption is first done, and the turbulent fluctuations of the progress variable are captured with a bimodal probability density function (PDF) in the line of the Bray–Moss–Libby (BML) model. Thereafter, a finite rate chemistry assumption is considered through the flamelet-generated manifold (FGM) method. In these two assumptions, the classical beta-PDF is used to model the temporal fluctuations of the mixture fraction in the turbulent flow. To model subgrid scale stresses and residual scalars fluxes, the wall-adapting local eddy (WALE) and the eddy diffusivity models are, respectively, used under the low-Mach number assumption.

Numerical results of velocity and mixing fields, as well as the ignition sequences, are validated through a comparison with their experimental counterparts. It is found that by coupling the DPIK model with each of the two combustion models implemented in a LES-based solver, the ignition event is reasonably predicted with further improvements provided by the finite rate chemistry assumption. Finally, the spark locations most likely to lead to a complete ignition of the burner are found to be around the shear layer delimiting the central recirculation zone, owing to the presence of a mixture within flammability limits.

Some discrepancies are found in the radial profiles of the radial velocity and consequently in those of the mixture fraction, owing to a mismatch of the radial velocity at the inlet section of the computational domain. Also, unlike FGM methods, the BML model predicts the overall ignition earlier than suggested by the experiment; this may be related to the overestimation of the reaction rate, especially in the zones such as flame holder wakes which feature high strain rate due to fuel-air mixing.

This work is adding a contribution for ignition modeling, which is a crucial issue in various combustion systems and especially in aircraft engines. The exclusive use of a commercial computational fluid dynamics (CFD) code widely used by combustion system manufacturers allows a direct application of this simulation approach to other configurations while keeping computing costs at an affordable level.

This study provides a robust and simple way to address some ignition issues in various spark ignition-based engines, namely, the optimization of engines ignition with affordable computational costs. Based on the promising results obtained in the current work, it would be relevant to extend this simulation approach to spray combustion that is required for aircraft engines because of storage volume constraints. From this standpoint, the simulation approach formulated in the present work is useful to engineers interested in optimizing the engines ignition at the design stage.

The purpose of this paper is to perform a relative assessment of performance benchmarks based on alternative asset pricing models to evaluate performance of mutual funds and suggest the best approach in Indian context.

Sample of 237 open-ended Indian equity (growth) schemes from April 2003 to March 2013 is used. Both unconditional and conditional versions of eight performance models are employed, namely, Jensen (1968) measure, three-moment asset pricing model, four-moment asset pricing model, Fama and French (1993) three-factor model, Carhart (1997) four-factor model, Elton et al. (1999) five-index model, Fama and French (2015) five-factor model and firm quality five-factor model.

Conditional version of Carhart (1997) model is found to be the most appropriate performance benchmark in the Indian context. Success of conditional models over unconditional models highlights that fund managers dynamically manage their portfolios.

A significant α generated over and above the return estimated using Carhart’s (1997) model reflects true stock-picking skills of fund managers and it is, therefore, worth paying an active management fee. Stock exchanges and credit rating agencies in India should construct indices incorporating size, value and momentum factors to be used for purpose of benchmarking.

The study adds new evidence as to applicability of established asset pricing models as performance benchmarks in emerging market India. It examines role of higher order moments in explaining mutual fund returns which is an under researched area.

This paper empirically investigates the link between expected returns on stocks and a set of variables that describe the general state of economic activity. The model relates the first and second conditional moments on stock excess returns to the conditional variances and covariances of a set of prespecified macroeconomic factors. The estimation results suggest that industrial production growth, inflation, and short‐term interest rates help explain the behavior over time of expected excess returns on stocks.

It has been increasingly recognized that exchange rate changes affect the cash flow and the value of firms. Existing studies on exchange rate exposure do not have much success in finding significant exposure, and the failure to find this relationship empirically has been termed “exposure puzzle”. Motivated by the limited success in detecting significant exchange rate exposure in the extant literature, China's exchange rate regime reform in 2005, the increasing role of China's stock market played in the global financial market and its attractiveness in international portfolio diversification, the purpose of this paper is to resolve the so-called “exposure puzzle” and thus make a contribution to the literature by investigating whether the renminbi (RMB) exchange rate movements have any significant impact on China's stock market from the perspective of US investors who may want to diversify their portfolios with Chinese stocks.

Since previous studies which rely heavily on the standard Ordinary Least Squares (OLS) or seemingly unrelated regression (SUR) method of estimation with the assumption of constant variance of firm's or industry's returns do not have much success in detecting significant exchange rate exposure, in this study, we apply an asymmetric GARCH(1,1) with generalized error distribution (GED) model which takes conditional heteroscedasticity and leptokurtosis of asset returns into account in the estimation of first- and second-moment exchange rate exposure.

Using weekly data over the period August 10, 2005–January 1, 2020 on 40 Chinese sector stock returns, the authors find strong evidence of first-moment exchange rate exposure. In particular, 65% (26 out of 40) of sectors examined have significant first-moment exposures and 73.08% (19 out of 26) of these significant first-moment exposures are asymmetric. For the second-moment exchange rate exposures, they are less frequently detected with 20% (8 out of 40) significant cases. These results are robust to whether an unorthogonalized or orthogonalized bilateral US dollar (USD)/Chinese Yuan (CNY) exchange rate is used in the estimation.

Because this study concerns only with whether exchange rate movements affect ex post returns as opposed to expected (ex ante) returns, and given the significant exposures with respect to different risk factors found in the study, it is interesting to see if any of these risk factors commands a risk premium. In other words, a natural extension of this study is to test whether any of these risk factors is priced in China's stock market.

The findings of the study have interesting implications for US investors who would like to diversify their portfolios with Chinese stocks and are concerned about whether the unexpected movements in CNY will affect their portfolio returns in addition to its local and world market risk exposures.

The study extends previous research on the first- and second-moment exchange rate exposure of Chinese stock returns by utilizing an asymmetric GARCH(1,1) with generalized error distribution (GED) model, which has not been fully exploited in the literature.

The paper aims to examine the performance of Spanish mutual funds between 1999 and 2003.

The methodolgy uses the stochastic discount factor (SDF) framework across a variety of models developed in the recent asset pricing literature. This approach is a fairly recent innovation in the evaluation of investment performance.

The present work complements the research of Farnworth et al. and Fletcher and Forbes, adding a new issue to the SDF, the third co‐moment of asset returns. Recent asset pricing studies show the relevance of the component of an asset's skewness related to the market portfolio's skewness, the coskewness, and how it helps to explain the time‐variation of ex‐ante market risk premiums. It is found that the effects of adding coskewness to evaluate the performance is significant even when factors based on size, book‐to‐market and momentum are included.

The omission of a coskewness factor may lead to erroneous evaluations of a fund's performance, and therefore, issues such as the persistence of performance should be revised.

This paper explores, for the first time, the effects of incorporating a coskewness factor in the analysis of investment performance, both in an unconditional and a conditional framework using SDF models.

Whether stock returns are linked to exchange rate changes and whether foreign exchange risk is priced in a domestic context are less conclusive and thus still subject to a great debate. The purpose of this paper is to provide new empirical evidence on these two inter‐related issues, which are critical to investors and corporate risk management.

This paper applies two different econometric approaches: Nonlinear Seemingly Unrelated Regression (NLSUR) via Hansen's Generalized Method of Moment (GMM) and multivariate GARCH in mean (MGARCH‐M) to examine the exchange rate exposure and its pricing.

Using industry data for Japan, similar to previous studies, foreign exchange risk is not priced based on the test of an unconditional two‐factor asset pricing model. However, strong evidence of time‐varying foreign exchange risk premium and significant exchange rate betas are obtained based on the tests of conditional asset pricing models using MGARCH‐M approach where both conditional first and second moments of industry returns and risk factors are estimated simultaneously.

The strong empirical evidence found in this study implies that corporate currency hedging not only results in more stable cash flows for a firm, but also reduces its cost of capital, and hence is justifiable.

This paper conducts an in‐depth investigation regarding the exchange rate exposure and its pricing by utilizing two different econometric approaches: NLSUR via Hansen's GMM and MGARCH‐M. In doing so, a more reliable conclusion about the exchange rate exposure and its pricing can be drawn.