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Interest in impact investing is growing, but investors typically allocate to impact funds and traditional assets independently from one another. We develop an optimizer that brings both types of assets together. Doing so allows investors to maximize impact for a certain level of risk-adjusted returns. For example, we find that investors can allocate 18% more to impact funds on average while keeping risk-adjusted returns constant. Intuitively, analyzing how impact funds and traditional assets interact should allow for better portfolio diversification. As interest in impact investing broadens, tools integrating impact funds into mainstream finance should become more widespread. TOPICS:ESG investing, portfolio construction, performance measurement, wealth management Key Findings ▪ We design a portfolio optimizer that brings together impact investments and traditional assets. Our optimizer has three dimensions: risk, return, and impact. Intuitively, returns from impact funds correlate with traditional asset returns. Taking such correlations into account allows for better portfolio diversification. That is, investors can achieve higher impact without hurting risk-adjusted returns. ▪ Diversification benefits can be large. Our optimizer allows an investor to increase impact by 18% without financial sacrifice. ▪ Demand for impact investing is rising. Tools that help to integrate impact funds into traditional portfolios are therefore useful and likely will become more widespread.

According to modern finance theory and increasing need for efficient investments, we evaluate the portfolio performance based on the data envelopment analysis method. By the fact that stock market’s return distributions usually exhibit skewness, kurtosis and heavy-tails, we consider some appropriate underlying distributions that affect the input and output of the model. In this regard, the multivariate skewed t and the multivariate generalized hyperbolic as the heavy-tailed distributions of Normal mean-variance mixture are applied. The models are inspired by the Range Directional Measure (RDM) model to deal with negative values. The value-at-risk (VaR) and conditional VaR (CVaR) as risk measures are used in these optimization problems. We estimate the parameters of such distributions by Expectation Maximization algorithm. Then we present an empirical investigation to measure the relative efficiency of two sets of seven groups of companies from different industries of Iran stock exchange market. By comparing the results of introduced models with previous RDM approach, we show that how well the distribution of assets affect the performance evaluation.

In this paper, the comparison of using garch (1, 1) and intergrated garch, igarch (1, 1) models on petroleum prices will be examined. This time-varying variation of asset returns as the horizon widens about kurtosis and volatility persistence are calculated and the results shows that petroleum prices dynamics submits more to igarch (1, 1) than garch (1, 1) model.

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.

The work aims at investigating and establishing if Aggregational Gaussianity, (AG) is in the dynamics of petroleum prices. This AG aspect is the phenomenon in which the empirical distribution of log-returns tends to normality (or as the time scale over which the returns are calculated increases). In order to achieve this, the petroleum price series was tested for arch effects. In addition, tests for Aggregational Gaussianity, (AG) were carried out using qualitative (graphical) approach and inferential approach, (involving statistical inference). The study shows that the presence of arch effects does not guarantee existence of AG. It is also observed that qualitative (graphical) approach may suggest normality and hence, presence of AG, on the other hand, inferential approach (involving statistical tests) gives a better picture of the actual conclusion, of the presence (or otherwise) of AG in the data set, with a 99.97% rejection from normality by the three tests-Kolmogorov-simonorv,Shapiro-Wilks, and Anderson-darling. In the circumstance, there is no evidence to confirm a discernible presence of AG in the dynamics of petroleum prices. The non-existence of AG in the study shows the instability in the dynamics of petroleum prices, since one cannot invoke normality as an invariant property this, among other factors, make the economy unstable as it is oil- driven. However, since the highest percentage of the budget for the country is based on the petroleum sales, which as this study reveals is unstable, hence, diversification of the economy is proposed. The softwares used in the work are Eviews 10, Minitab 18, Spss 17, Easy-fit 5.6 professional, and R 3.2.2.

This paper provides details on a classroom experiment that focuses on returns to foreign assets given uncertain future exchange rates. Students are assigned the role of foreign analysts and decide ...

One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by applying topological metrics of correlation networks to the challenge of financial forecasting. These network‐based metrics are retrieved with the help of graph theory and quantify the interconnectedness of financial assets. In this paper, we show that this network‐based information statistically significantly predicts future asset returns. Because industry professionals are more interested in the economic value‐added of competing forecasting approaches, we also devote our attention to an economic analysis. Considering economic performance metrics, network‐based predictors generate a clear value‐added, which also applies to the multi‐asset allocation case.

We propose a new method to tighten the restrictions on the admissible pricing kernel space. Our framework improves information-based pricing kernel bounds and can be used to test various asset pricing models. This method incorporates higher moments of asset returns and conditional information based on the possible risk-free rate fluctuations. The variation in the risk-free rate is attributed to two possible states of the economy, and the proposed approach identifies these states and improves the pricing kernel bound.

Purpose This paper aims to examine the impact of the Brexit referendum on the risk structure of financial asset prices. Co-movements are analysed using daily price returns of major stock and bond indices as well as commodities and exchange rates from June 2014 to June 2018. The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies. Design/methodology/approach The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. Findings It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. Research limitations/implications The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies. Originality/value To the best of the authors’ knowledge, research studying the underlying asset co-movements around Brexit does not exist.