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A growing number of investors in recent years has focused on environmental, social and governance (ESG) factors in carrying out investment activities and the COVID-19 pandemic has only driven such trends of ESG investing at an accelerated rate. Many studies have examined the relationship between ESG scores and corporate financial performance, along with the effectiveness of ESG portfolios. This paper discusses various approaches to incorporate ESG factors into a portfolio optimisation and critically compares and contrasts the efficacy of these approaches on the Dow Jones Industrial Average constituents. It finds that thematic investing appears to be the best performer. In addition, it is also found that there is no evidence that ESG portfolios underperform the market. [ABSTRACT FROM AUTHOR]

This article considers risk measures constructed under a discrete mixture-of-normal distribution on the innovations of a GARCH model with time-varying volatility. The authors use an approach based on a continuous [...]

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1468-0084.2009.00565.x Byline: Joseph P. Dejuan ([dagger]), John J. Seater ([double dagger]), Tony S. Wirjanto ([dagger]) Keywords: E20; E21; R10 Abstract: Abstract This paper utilizes relatively unexplored Canadian provincial-level data to investigate an old but still relevant question in macroeconomics as to whether consumption responds to income innovations in a manner consistent with the stochastic implications of the permanent income hypothesis (PIH). The empirical results obtained do not appear to be in accord with the PIH. Instead, consumption's response to income innovations is found to be much weaker than that predicted by PIH; in particular, the response displays an asymmetric pattern in the sense that it is much stronger for negative than positive income innovations. We interpret this evidence of asymmetry as indicative of the presence of liquidity constraints in provincial households. Author Affiliation: ([dagger])Department of Economics, University of Waterloo, Waterloo, ON, Canada (e-mail:jdejuan@uwaterloo.ca,twirjant@uwaterloo.ca) ([double dagger])Department of Economics, North Carolina State University, Raleigh, NC, USA (e-mail:John_seater@ncsu.edu) Article History: Final Manuscript Received: June 2009

This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market. The model fits are assessed, and the proposed models are shown to compare favorably to the one-component asymmetric stochastic volatility models with Gaussian and Student t distributed innovation terms. [ABSTRACT FROM AUTHOR]

This article proposes a general framework of a Markov Switching GARCH model with a mixture of truncated Gaussian to model asset returns with price limits in China. Theoretically, while retaining many convenient statistical properties of the Gaussian distribution, the proposed model also assumes a flexible time-varying volatility structure to accommodate the feature of the return data under price restrictions in China, such as the clusters near the bounds (due to the 'bound effect'). Empirically, we apply the model to eight representative stocks from Shanghai and Shenzhen stock markets in China. Lastly, we find that our proposed model dominates the conventional volatility models in terms of Value-at-Risk measures. [ABSTRACT FROM AUTHOR]

This paper proposes a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. It assumes that the innovations of the duration process follow a threshold distribution with a positive support. In addition, it also assumes that the latent first-order autoregressive process of the log conditional durations switches between two regimes. The regimes are determined by the levels of the observed durations and the TSCD model is specified to be self-excited. A novel Markov-Chain Monte Carlo method (MCMC) is developed for parameter estimation of the model. For model discrimination, we employ deviance information criteria, which does not depend on the number of model parameters directly. Duration forecasting is constructed by using an auxiliary particle filter based on the fitted models. Simulation studies demonstrate that the proposed TSCD model and MCMC method work well in terms of parameter estimation and duration forecasting. Lastly, the proposed model and method are applied to two classic data sets that have been studied in the literature, namely IBM and Boeing transaction data. [ABSTRACT FROM AUTHOR]

This paper discusses the management of climate change risks for equity investments and presents a scenario-based framework for building sustainable portfolios under the climate change scheme. An empirical analysis is first performed using historical price data to show the inferior risk-adjusted performance of the carbon-intensive industries in the North American stock market, which supplements evidence from existing literature in the market's gradual pricing of the climate change risk. Risk management modules are devised with subjective top-level constraints to achieve comprehensive coverage of the key aspects of climate change: risk exposures are measured by carbon intensities, while the risk impacts are quantified through equity return impact scenarios derived from climate change paths under Integrated Assessment Models. A model for quantifying stranded asset risk is also presented. Results from these modules formulate the joint posterior return distribution of the stocks that are used to construct the mean-variance optimal portfolio. [ABSTRACT FROM AUTHOR]

This paper conducts simulation-based comparison of several stochastic volatility models with leverage effects. Two new variants of asymmetric stochastic volatility models, which are subject to a logarithmic transformation on the squared asset returns, are proposed. The leverage effect is introduced into the model through correlation either between the innovations of the observation equation and the latent process, or between the logarithm of squared asset returns and the latent process. Suitable Markov Chain Monte Carlo algorithms are developed for parameter estimation and model comparison. Simulation results show that our proposed formulation of the leverage effect and the accompanying inference methods give rise to reasonable parameter estimates. Applications to two data sets uncover a negative correlation (which can be interpreted as a leverage effect) between the observed returns and volatilities, and a negative correlation between the logarithm of squared returns and volatilities. [ABSTRACT FROM AUTHOR]

This paper examines a key stochastic implication of the canonical version of the permanent income model that new information about future income should give rise to an adjustment in consumption equal in magnitude to the adjustment in permanent income. Using data from 29 Chinese provinces, the empirical results are consistent with the model. [ABSTRACT FROM AUTHOR]

This article focuses on simulation-based inference for the time-deformation models directed by a duration process. In order to better capture the heavy tail property of the time series of financial asset returns, the innovation of the observation equation is subsequently assumed to have a Student-tdistribution. Suitable Markov chain Monte Carlo (MCMC) algorithms, which are hybrids of Gibbs and slice samplers, are proposed for estimation of the parameters of these models. In the algorithms, the parameters of the models can be sampled either directly from known distributions or through an efficient slice sampler. The states are simulated one at a time by using a Metropolis-Hastings method, where the proposal distributions are sampled through a slice sampler. Simulation studies conducted in this article suggest that our extended models and accompanying MCMC algorithms work well in terms of parameter estimation and volatility forecast. [ABSTRACT FROM AUTHOR]

This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution in which the mean innovation switches between two regimes. In our model, the threshold is treated as an unknown parameter. We show that the proposed threshold SV model can not only capture the time-varying volatility of returns, but can also accommodate the asymmetric shape of conditional distribution of the returns. Parameter estimation is carried out by using Markov chain Monte Carlo methods. For model selection and volatility forecast, an auxiliary particle filter technique is employed to approximate the filter and prediction distributions of the returns. Several experiments are conducted to assess the robustness of the proposed model and estimation methods. In the empirical study, we apply our threshold SV model to three return time series. The empirical analysis results show that the threshold parameter has a non-zero value and the mean innovations belong to two separately distinct regimes. We also find that the model with an unknown threshold parameter value consistently outperforms the model with a known threshold parameter value. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Building on the tools developed for American call options in financial markets in Finance and the optimal timing of investment under uncertainty in Economics, this paper proposes a stylized equilibrium model to study the optimal time for a risk-averse unemployed individual, who receives an unemployment insurance benefit and may receive a recall from the old job, to exit from a waiting (and hence unemployment) state and start a new job. It is shown that as a result of the unemployed individual's exercising her optimal timing strategy, there is duration of so-called waiting and that this duration is affected by a number of economic primitives, prominent among which are the attitude of an unemployed individual toward risk and the uncertainty faced by this individual. [ABSTRACT FROM AUTHOR]

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes and innovations of observed duration process and latent log duration process. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allow comparison of our models and methods to those existing in the literature. [ABSTRACT FROM PUBLISHER]

ABSTRACT This paper proposes Markov chain Monte Carlo methods to estimate the parameters and log durations of the correlated or asymmetric stochastic conditional duration models. Following the literature, instead of fitting the models directly, the observation equation of the models is first subjected to a logarithmic transformation. A correlation is then introduced between the transformed innovation and the latent process in an attempt to improve the statistical fits of the models. In order to perform one-step-ahead in-sample and out-of-sample duration forecasts, an auxiliary particle filter is used to approximate the filter distributions of the latent states. Simulation studies and application to the IBM transaction dataset illustrate that our proposed estimation methods work well in terms of parameter and log duration estimation. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper extends the stochastic conditional duration model first proposed by Bauwens and Veredas (2004) by imposing mixtures of bivariate normal distributions on the innovations of the observation and latent equations of the duration process. This extension allows the model not only to capture various density shapes of the durations but also to easily accommodate a richer dependence structure between the two innovations. In addition, it applies an estimation methodology based on the empirical characteristic function. Empirical applications based on the IBM and Boeing transaction data are provided to assess and illustrate the performance of the proposed model and the estimation method. One interesting empirical finding in this paper is that there is a significantly positive correlation under both the contemporaneous and lagged intertemporal dependence structures for the IBM and Boeing duration data. [ABSTRACT FROM AUTHOR]

This article considers risk measures constructed under a discrete mixture-of-normal distribution on the innovations of a GARCH model with time-varying volatility. The authors use an approach based on a continuous empirical characteristic function to estimate the parameters of the model using several daily foreign exchange rates' return data. This approach, compared to the likelihood-based approach, has several important advantages as a method for estimating this type of models; in particular, the characteristic function, unlike its likelihood function counterpart, is always uniformly bounded over the model-parameter space due to the Fourier transformation. To evaluate VaR and expected shortfall measures obtained from alternative specifications, the authors construct several evaluation criteria, such as the number of violations and the sum square of violations. Based on these criteria, the authors find that both the VaR and expected shortfall measures obtained from the proposed model outperform those obtained from other competing models. [ABSTRACT FROM AUTHOR]

This paper presents a stylized exchange rate pass-through model of crude oil price formation for the purpose of understanding the price reactions of OPEC Member Countries to changes in the exchange rate of the US dollar against major currencies and the prices of other Members. Our empirical results suggest that, in response to changes in the exchange rate, exporting countries tend to adjust their prices to secure a stable international purchasing power of oil revenues and to avoid suppressing market demand and losing market share. [ABSTRACT FROM AUTHOR]