Option pricing with random risk aversion

Review of Quantitative Finance and Accounting - Based on a standard general equilibrium economy, we develop a framework for pricing European options where the risk aversion parameter is state...     

Can sustainable investments outperform traditional benchmarks? Evidence from global stock markets

To contribute to overcoming global sustainability challenges, investors have been increasingly interested in making sustainable investments and incorporating environmental, social and governance (ESG) criteria into their portfolio selection decisions and managerial activities. However, these investors and other agents interested in sustainable investment need updated and robust information to support their decision making. We analyzed the performance of several Dow Jones Sustainability Indices (DJSIs) and compared them with their respective market benchmarks from 2013 to 2018. The indices comprise the following regions and countries: the world, the Asia‐Pacific, Europe, emerging markets and the US. The analysis was conducted based on both classic and modern portfolio metrics. The results suggest that sustainable investment performance is still heterogeneous worldwide, but there is a promising opportunity for investors to obtain superior risk‐adjusted returns in certain regions while incorporating sustainable investment practices. The findings are of utmost importance to financial market practitioners, business managers, academics and other stakeholders interested in promoting investments, corporate practices and scientific knowledge to achieve the Sustainable Development Goals (SDGs).     

Reducing Brazilian greenhouse gas emissions: scenario simulations of targets and policies

This paper aims to evaluate the economic impacts of greenhouse gas emission reduction on the Brazilian economy. To this end, we developed an integrated input–output linear programming model for 2009 using the Supply and Use Tables and emissions data of the Brazilian Ministry of Science and Technology and Innovation. We simulated emissions targets for various potential scenarios in which the adopted policy design took account of sectoral composition in terms of emissions and available production technology. The results were directly affected by the high level of livestock emissions, counterbalancing this sector’s economic importance for Brazil. In the short term, sectoral emissions targets associated with taxation policy or emission permits could be developed in order to create private incentives to mitigate emissions. In this sense, the results also show that different sectoral targets may be able to balance environmental benefits with the possible economic losses incurred by such policies.     

Improving time series forecasting: An approach combining bootstrap aggregation,clusters and exponential smoothing

Some recent papers have demonstrated that combining bagging (bootstrap aggregating) with exponential smoothing methods can produce highly accurate forecasts and improve the forecast accuracy relative to traditional methods. We therefore propose a new approach that combines the bagging, exponential smoothing and clustering methods. The existing methods use bagging to generate and aggregate groups of forecasts in order to reduce the variance. However, none of them consider the effect of covariance among the group of forecasts, even though it could have a dramatic impact on the variance of the group, and therefore on the forecast accuracy. The proposed approach, referred to here as Bagged.Cluster.ETS, aims to reduce the covariance effect by using partitioning around medoids (PAM) to produce clusters of similar forecasts, then selecting several forecasts from each cluster to create a group with a reduced variance. This approach was tested on various different time series sets from the M3 and CIF 2016 competitions. The empirical results have shown a substantial reduction in the forecast error, considering sMAPE and MASE.     

Profitability and social impacts of reduced impact logging in the Tapajós National Forest, Brazil — A case study

Brazil contains the world's largest tropical rainforests, most located in the Amazon River Basin. Over the last three decades, rapid growth of this region's deforested area has had negative impacts. To minimize these impacts and maintain biodiversity, the Brazilian Government has established several national forests in the Basin. The ITTO Project, a reduced impact logging (RIL) operation, was recently carried out at one of these forests: the Tapajós National Forest, also known as Flona Tapajós. This paper evaluates the Project's profitability and its effect on local residents. The Project, which ran between 1999 and 2003, was coordinated by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA), with funding for planning and monitoring provided by the United Kingdom's Department of International Development (DFID) working through and approved by the International Tropical Timber Organization (ITTO). Treviso Agropecuária Ltda, a private logging company, carried out timber extraction on the Project site. Our evaluation found the ITTO Project to have been highly profitable for Treviso, even after their compliance with all Brazilian labor and environmental laws. This finding was based on field interviews and the examination of documents from IBAMA and Treviso. Treviso's mean internal rate of return from the Project was calculated to have been 35.79%, considerably higher than that generated by the region's farms and ranches. The ITTO Project positively impacted Project workers, providing employment and exposing them to rainforest management techniques that maximize timber production while minimizing forest destruction. The paper closes by suggesting that more of the direct and indirect benefits of new reduced impact logging projects on Brazilian national forest land need to be channeled to the local population to increase the probability of them act as capable forest custodians.     

Further investigation of the uncertain trend in US GDP

The presence of deterministic or stochastic trend in US GDP has been a continuing debate in the literature of macroeconomics. Ben-David and Papell (1995 Ben-David, D and Papell, DH. 1995. The great wars, the great crash, and steady growth: some new evidence about an old stylized. Journal of Monetary Economics, 36: 453–75. [Crossref], [Web of Science ®] , [Google Scholar]) found evidence in favour of trend stationarity using the secular sample of Maddison (1991 Maddison, A. 1991. Dynamics Forces in Capitalist Development: A Long-Run Comparative View, Oxford: Oxford University Press.  [Google Scholar]). More recently, Murray and Nelson (2000 Murray, CJ and Nelson, CR. 2000. The uncertain trend in U.S. GDP. Journal of Monetary Economics, 46: 79–95. [Crossref], [Web of Science ®] , [Google Scholar]) correctly criticized this finding arguing that the Maddision data are plagued with additive outliers (AO), which bias inference towards stationarity. Hence, they propose to set the secular sample aside and conduct inference using a more homogeneous but shorter time-span post-WWII sample. In this article we re-visit the Maddison data by employing a test that is robust against AO's. Our results suggest the US GDP can be modelled as trend stationary process     

Critical Success Factors of Brazilian Business Incubators

The main purpose of this study is to determine the Critical Success Factors (CSF) of Brazilian business incubator management and understand how they are structured. These factors are essential to improve the performance of these Brazilian businesses and to strengthening the Brazilian entrepreneurial ecosystem. The strategy used was survey, and the data obtained were analyzed using the statistical technique of Exploratory Factor Analysis. The validated CSF were grouped into seven constructs. Thus far, the literature does not present any article within the scope proposed here. Additionally, the statistical technique used here is applied for the first time in this theme.     

The Impact of Distance (External) and Organizational Factors (Internal) on the Knowledge Chain of Multinational Corporations: South Africa as a Host Country

The geographic dispersion of multinational corporations (MNCs) implies that while it gives them access to new and different knowledge from diverse localities, it also adds to the costs and complexities of managing that knowledge and its effective dispersal across geographies. The purpose of this article is to examine how knowledge is transferred within MNCs and provide a framework for this process, particularly focusing on the role that distance (external) and organizational (internal) factors plays therein. A qualitative study is utilized, focusing on two technology companies from different cultural home countries and the technology transfer process with their South African subsidiaries. We find that the standardization of knowledge impacts the creation and diffusion of knowledge; expatriates impact on the creation, diffusion, and adoption; and, finally, relevance and localization impact on the adoption and utilization of knowledge. We present a conceptual framework around trust and rationalization as regards transferring knowledge within MNCs and find some evidence of the impact of distance, particularly cultural, on the methods employed in this transfer. The article illustrates the practical ways in which MNCs organize their internal resources and overcome various dimensions of distance in ensuring knowledge transfers. By choosing companies from such divergent home countries (one industrialized and one newly industrialized, with very different cultural settings) and examining their knowledge transfers with their South African subsidiaries, we are able to unpack various dimensions of distance and how organizational mechanisms affect this process. © 2017 Wiley Periodicals, Inc.     

Implied volatility sentiment: a tale of two tails

We propose a sentiment measure jointly derived from out-of-the-money index puts and single stock calls: implied volatility (IV-) sentiment. In contrast to implied correlations, our measure uses information from the tails of the risk-neutral densities from these two markets rather than across their entire moneyness structures. We find that IV-sentiment measure adds value over and above traditional factors in predicting the equity risk premium out-of-sample. Forecasting results are superior when constrained ensemble models are used vis-à-vis unregularized machine learning techniques. In a mean-reversion strategy, our IV-sentiment measure delivers economically significant results, with limited exposure to a set of cross-sectional equity factors, including Fama and French's five factors, the momentum factor and the low-volatility factor, and seems valuable in preventing momentum crashes. Our novel measure reflects overweight of tail events, which we interpret as a behavioral bias. However, we cannot rule out a risk-compensation rationale.