Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Public attention to “Islamic terrorism” and stock market returns

Does public attention to Islamic terrorism affect the performance of Islamic and conventional indices? We answer this question by empirically examining the effects of US public attention to Islamic terrorism on returns of US Islamic and conventional indices between 2004 and 2017. US public attention to Islamic terrorism is measured using Google Search Volume, which reflects active public attentiveness, and media coverage, which measures passive attentiveness. We test its effect on the stock returns of Islamic and conventional indices by using difference-in-difference analysis. The results indicate that US public attention to Islamic terrorism negatively affects US Islamic indices, suggesting that investors may make amalgams between terrorism and Islamic finance. These clichés may lead them to sell Sharia-compliant assets when US public attention to Islamic terrorism is high. Taken together, our findings provide new evidence and financial implications for investors and providers of Islamic financial products.     

Measuring the immeasurable — A survey of sustainability indices

Sustainability indices for countries provide a one-dimensional metric to valuate country-specific information on the three dimensions of sustainable development: economic, environmental, and social conditions. At the policy level, they suggest an unambiguous yardstick against which a country's development can be measured and even a cross-country comparison can be performed. This article reviews the explanatory power of various sustainability indices applied in policy practice. We show that these indices fail to fulfill fundamental scientific requirements making them rather useless if not misleading with respect to policy advice.     

A Framework for the Simultaneous Measurement of Spatial Variation and Temporal Movement in Prices in a Heterogeneous Country: The Dynamic Household Regional Product Dummy Model

This paper contributes to the growing literature on spatial prices in large heterogeneous countries. While the literatures on spatial variation and temporal movement in prices have grown in parallel, this study marks a departure by providing a unified treatment and proposing a comprehensive framework that allows both approaches. The proposed model is based on twin extensions of the household version of the “country product dummy model” by allowing for a dynamic stochastic specification and interdependence of spatial prices of geographically adjacent regions. Tests of temporal stability and regional independence of the estimated spatial prices are proposed and applied in this paper. The paper shows that the introduction of an autoregressive error process of order one, AR(1), improves the efficiency of the estimates of parameters, urban‐rural and temporal price indices under certain conditions. The Indian application points to a rich potential for using the proposed framework in cross country comparisons such as the International Comparison Program (ICP) exercises.     

Closeness matters. Spatial autocorrelation and relationship between socioeconomic indices and distance to departmental Colombian capitals

Socioeconomic indices that developing economies use to combat poverty may show a limited picture of all the variables related to the problem. This study analyzes spatial autocorrelation and clusters of three socioeconomic indices—living conditions, multidimensional poverty, and unsatisfied basic needs—in Colombia to explore the relation of the identified clusters with their physical distance from departmental capitals. Using a local index of spatial autocorrelation, it evaluates spatial patterns and the clustering of socioeconomic indices. Correlation analysis tests the relation between clusters and their distance from departmental capitals in three departments. The spatial patterns of indices in Colombia correspond to the model of economic development in the country and reveal the regions where socioeconomic characteristics form clusters of desirable/undesirable conditions and departments where the distance from main cities may be seen as a condition for a higher quality of life.     

Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component.     

我国切花出口国际竞争力的实证分析

花卉产业是世界农业中惟一不受农产品配额限制的产业。切花产品已经成为国际上的大宗商品，对于切花的需求逐年增长。我国地大物博，气候条件多样，适于种植花卉。作为世贸组织成员国，近年来切花出口增长的趋势明显。本文运用贸易竞争指数、市场占有率、市场集中度和显示性比较优势指数，从我国切花产品与国际市场相对应的角度，定量比较分析我国切花产品国际竞争力，并分析影响切花国际竞争力的主要因素，从而对优化我国切花产品的出口环境提出相应建议。     

Maximum likelihood estimation of the double exponential jump-diffusion process

The double exponential jump-diffusion (DEJD) model, recently proposed by Kou (Manage Sci 48(8), 1086–1101, 2002) and Ramezani and Zeng (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=606361, 1998), generates a highly skewed and leptokurtic distribution and is capable of matching key features of stock and index returns. Moreover, DEJD leads to tractable pricing formulas for exotic and path dependent options (Kou and Wang Manage Sci 50(9), 1178–1192, 2004). Accordingly, the double exponential representation has gained wide acceptance. However, estimation and empirical assessment of this model has received little attention to date. The primary objective of this paper is to fill this gap. We use daily returns for the S&;P-500 and the NASDAQ indexes and individual stocks, in conjunction with maximum likelihood estimation (MLE) to fit the DEJD model. We utilize the BIC criterion to assess the performance of DEJD relative to log-normally distributed jump-diffusion (LJD) and the geometric brownian motion (GBM). We find that DEJD performs better than these alternatives for both indexes and individual stocks.     

Is there a Difference? The Performance Characteristics of SRI Equity Indices

Abstract:  This study analyses whether stock indices that represent socially responsible investments (SRI) exhibit a different performance compared to conventional benchmark indices. In contrast to other studies, the analysis concentrates on SRI indices and not on investment funds. This has several advantages, since transaction costs of funds, the timing activities and the skill of the fund management do not have to be considered. A direct measure of the performance effects of SRI screens is therefore examined. The 29 SRI stock indices are analysed by single-equation models as well as by multi-equation systems that exploit the information in the cross-section. SRI stock indices do not exhibit a different level of risk-adjusted return than conventional benchmarks. But many SRI indices have a higher risk relative to the benchmarks. The findings are robust to the use of different benchmark indices and apply to all common types of SRI screening.