J. Risk Financial Manag., Volume 13, Issue 11 (November 2020) – 44 articles

This paper proposes a new approach to examine the relationship between CO${}_2$ emissions and economic developing. In particular, we propose to test the Environmental Kuznets Curve (EKC) hypothesis for a panel of 24 OECD countries and 32 non-OECD countries by developing a more flexible estimation technique which enables to account for functional form misspecification, cross-sectional dependence, and heterogeneous relationships among variables, simultaneously. We propose a new nonparametric estimator that extends the well-known Common Correlated Effect (CCE) approach from a fully parametric framework to a semiparametric panel data model. Our results corroborates that the nature and validity of the income–pollution relationship based on the EKC hypothesis depends on the model assumptions about the functional form specification. For all the countries analyzed, the proposed semiparametric estimator leads to non-monotonically increasing or decreasing relationships for CO${}_2$ emissions, depending on the level of economic development of the country. Full article

Foreign direct investment (FDI) as a driver of growth is important in today’s globalized economy. It is extremely difficult for economies to grow sustainably without economic interactions outside their borders. However, there has been a debate on the impact of FDI inflow on economic expansion. Hence, this study investigated the influence of FDI on economic growth for a selection of 200 economies around the world for the period 1990–2018. We subdivided the sample into World Bank income group clusters to aid comparison across income blocs. The study employed panel estimation techniques including pooled ordinary least squares (POLS), dynamic panel estimation with fixed-effects and random-effects and generalized method of moments (GMM). The study found that FDI, debt stock and official development assistance are promoters of growth in the selected countries—although debt stock weakly impacts economic growth. In contrast, trade openness and exchange rates had a mixed (negative and positive) influence on economic growth. The study suggests that the creation of a conducive business environment and economic policies will attract FDI inflows. Additionally, borrowing from external sources could be minimized despite its perceived positive influence on growth to achieve financial independence. Full article

Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection concerning observed variables. This is not straightforward when one or more of the covariates are measured with error. Here, we present a new semi-parametric estimator that addresses this issue. In particular, we focus on inverse propensity score weighting estimators when the propensity score is of an unknown functional form and some covariates are subject to classical measurement error. Our proposed solution involves deconvolution kernel estimators of the propensity score and the regression function weighted by a deconvolution kernel density estimator. Simulations and replication of a study examining the impact of two financial literacy interventions on the business practices of entrepreneurs show our estimator to be valuable to empirical researchers. Full article

We present an empirical study of the Aumann-Serrano performance index for multi-period gambles when the underlying stochastic process is assumed to be a normal mixture process with time-varying volatility. We compare the Aumann-Serrano performance index for multi-period gambles with that for one-period gambles as well as the Sharpe ratio. Our empirical study is obtained using a selection of U.S. stock data and shows evaluation of a selection of stocks becomes more distinct in multi-period gambles than in one-period gambles in the sense that a favorable evaluation score becomes even better in multi-period gambles than in one-period gambles while an unfavorable evaluation score becomes even worse in multi-period gambles than in one-period gambles. Full article

This paper develops a test that helps assess whether the term structure of option implied volatility is constant across different levels of moneyness. The test is based on the Hausman principle of comparing two estimators, one that is efficient but not robust to the deviation being tested, and one that is robust but not as efficient. Distribution of the proposed test statistic is investigated in a general semiparametric setting via the multivariate Delta method. Using recent S&P 500 index traded options data from September 2009 to December 2018, we find that a partially linear model permitting a flexible “volatility smile” and an additive quadratic time effect is a statistically adequate depiction of the implied volatility data for most years. The constancy of implied volatility term structure, in turn, implies that option traders shall feel confident and execute volatility-based strategies using at-the-money options for its high liquidity. Full article

The aim of this paper is to assess the efficiency of a set of 62 precious metal mutual funds (PMMFs) and to explain performance differences between funds using weighted additive data envelopment analysis (DEA) and Tobit regression, respectively. The contribution of this paper is twofold: to provide for the first-time metrics of the relative performance of PMMFs using a particular weighted additive model, namely the range-adjusted measure (RAM), and to explain the performance of the funds by the use of a Tobit model. Results do not suggest positive linkages between RAM-based and standard fund performance metrics (Sharpe ratio and Jensen’s alpha). Moreover, for the sample inefficient funds the mean–variance performance hypothesis does not hold. In addition, fund performance based on RAM can be explained by the persistence of the fund and the beta coefficient. Full article

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR. Full article

This paper aims to analyze the determinants of profitability and bank efficiency in the Iberian Peninsula. To achieve the proposed objective, a sample of 66 Portuguese and Spanish banks was analyzed. To test the hypotheses formulated according to the proposed literature review, the panel data methodology was used; specifically, the Generalized Method of Moments (GMM) system model proposed by and the Tobit model. The results point out that the banking performance, measured in terms of profitability and efficiency, in the Iberian Peninsula, is influenced by internal management variables, but also by the macroeconomic environment. More interestingly, and new in the Iberian banking sector literature, the results prove a positive and negative non-linear relationship between bank size and their levels of profitability and efficiency, respectively. Full article

The scope and ambitions of biomedical institutions worldwide currently working toward the integration of personalized medicine (PM) require recognizing the potential profound impact on regulatory standards and on the economic functioning and financing of healthcare. Against this background, researchers and policymakers must manage the arising challenges for the healthcare systems. In this paper we study the literature related to the consequences of PM on health insurance and care systems. Using the PRISMA research protocol, we search the existing body of literature and analyze publications dealing with insurance (419 papers) in the field of PM. After a detailed reading of the 52 studies included in our analysis, we synthesize challenges in three fields that must be addressed to avoid hindering the implantation of PM. The key issues that we highlight concern (1) a lack of clear and consistent data on the economic relevance of PM, (2) a value-oriented and cost-efficient definition of reimbursement thresholds, (3) the implementation of PM in the prevailing healthcare system. In the meantime, we provide several solutions to these concerns; we present (a) risk-sharing contracts that can deal with the emerging coverage challenges, (b) criteria that could constitute future reimbursement thresholds and (c) examples of successful implementations of PM into healthcare systems. Our findings are relevant for policymakers and health insurance companies for redefining the guidelines for the healthcare schemes of the future. Full article

Real estate investment trusts (REITs) provide portfolio diversification and tax benefits, a stable stream of income, and inflation hedging to investors. This study employs a quantile autoregression model to investigate the dependence structures of REITs’ returns across quantiles and return frequencies. This approach permits investigation of the marginal and aggregate effects of the sign and size of returns, business cycles, volatility, and REIT eras on the dependence structure of daily, weekly, and monthly REIT returns. The study documents asymmetric and misaligned dependence patterns. A bad market state is characterized by either positive or weakly negative dependence, while a good market state is generally marked by negative dependence on past returns. The results are consistent with under-reaction to good news in a bad state and overreaction to bad news in a good state. Past negative returns increase and decrease the predictability of REIT returns at lower and upper quantiles, respectively. Extreme positive returns in the lower (upper) quantiles dampen (amplify) autocorrelation of daily, weekly, and monthly REIT returns. The previous day’s REIT returns dampen autoregression more during recession periods than during non-recession periods. The marginal impact of the high volatility of daily returns supports a positive feedback trading strategy. The marginal impact of the Vintage REIT era on monthly return autocorrelation is higher than the New REIT era, suggesting that increased participation of retail and institutional investors improves market efficiency by reducing REITs’ returns predictability. Overall, the evidence supports the time-varying efficiency of the REITs markets and adaptive market hypothesis. The predictability of REIT returns is driven by the state of the market, sign, size, volatility, and frequency of returns. The results have implications for trading strategies, policies for the real estate securitization process, and investment decisions. Full article

The Enterprise Risk Management (ERM) process has heterogeneously developed across the world, although it represents a leading paradigm, supporting organizations to identify, evaluate, and manage risks at the enterprise level. Academics have studied the process, but there is no complete picture of the determinants and implications of such an integrated risk management process. Therefore, we present a systematic empirical literature review on ERM, based on a research protocol. The review highlights that the ERM literature can be divided into four general lines of research: the ERM adoption, the determinants of the ERM implementation, the effects of ERM adoption, and other aspects. In contrast to the richness of studies devoted to ERM engagement in small and medium-sized enterprises (SMEs), studies exploring ERM adoption in banks or insurance are relatively few. The literature review has revealed that the most frequently investigated effect of ERM is on firm performance. Little effort has been dedicated to the analysis of the effectiveness of ERM by its components and to institutional, individual, and organizational factors that affect ERM adoption. The study can serve as a starting point for scholars to explore research gaps related to ERM, while the practitioners can rely on the presented findings to identify the effects of the ERM implementation. Full article

The goal programming (GP) is a well-known approach applied to multi-criteria decision making (M-DM). It has been used in many domains and the literature offers diverse extensions of this procedure. On the other hand, so far, some evident analogies between M-DM under certainty and scenario-based one-criterion decision making under uncertainty (1-DMU) have not been revealed in the literature. These similarities give the possibility to adjust the goal programming to an entirely new domain. The purpose of the paper is to create a novel method for uncertain problems on the basis of the GP ideas. In order to achieve this aim we carefully examine the analogies occurring between the structures of both issues (M-DM and 1-DMU). We also analyze some differences resulting from a different interpretation of the data. By analogy to the goal programming, four hybrids for 1-DMU are formulated. They differ from each other in terms of the type of the decision maker considered (pessimist, optimist, moderate). The new decision rule may be helpful when solving uncertain problems since it is especially designed for neutral criteria, which are not taken into account in existing procedures developed for 1-DMU. Full article

In this paper, we establish the significance and effects of initial public offer (IPO) offer price ranges on subscription, initial trading, and post-IPO ownership structures. The primary market in India provides a unique setting for estimating the effect of various initial public offer (IPO) price ranges and IPO issue factors on the initial demand for an IPO among investors, measured by full IPO subscription/oversubscription, initial turnover (liquidity), and the post-IPO listing ownership structure among investors (ownership). For the IPO pre-listing stage, this study uses firth logistic regression to estimate the effect of various IPO offer price ranges (low to high) and various IPO issue factors on the full subscription/oversubscription of an IPO in each investor category. For the post-IPO listing stage, the study uses OLS regression to estimate the effect of various IPO offer price ranges (low to high) and various IPO issue factors on the initial trading ratio (IPO listing day trading) and the ownership percentage between institutional and individual investors. We find that all investor categories show a lesser likelihood for full subscription or oversubscription of an IPO issue at the lowest range of IPO offer prices. At the post-listing stage, the results indicate a diverse IPO offer price range in which individuals and institutions maximize their respective ownership holdings after the IPO listing. The results further show that lower promoter holdings diffuse higher ownership among individual shareholders by targeting lower IPO offer prices, thus increasing control. Full article

This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL(p,q) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample. Full article

This study examines the effect of the elderly population on SARS-CoV-2 Disease (COVID-19) mortality for a sample of 146 countries. It shows that the elderly population is robustly associated with higher COVID-19 mortality. This effect, however, decreases significantly in countries with higher health care absorptive capacity. The results are robust to control for a set of economic, institutional and regional variables. Full article

Due to the lockdown regulations worldwide during the COVID-19 pandemic, the global aviation industry has been severely hit. This study focuses on the volatility estimation of stock indexes in the Chinese Airport Shipping Set (ASS) at industry-enterprise levels and identifies possible business behavior that may cause fluctuating differences. Depending on the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, text mining method and Word Cloud Views, results show that (1) the holistic volatility of Airport Shipping Set Index (ASSI) increases relative to the pre-COVID period; (2) volatility of airport stocks has crucial differences, while the volatility of shipping stocks is similar; (3) there are different responses to the pandemic between Shenzhen Airport and Shanghai Airport shown in their semiannual financial reports. Compared to the latter, the former had a more positive attitude and took various measures to mitigate risks, providing evidence of the volatility differences between firms. Full article

This paper analyzes the connectedness among bitcoin, gold, and crude oil between 3 January 2017 and 31 December 2019. The paper’s motivation is based upon the idea that bitcoin can be similar to gold in terms of its hedging properties and can be used for hedging for different assets. Moreover, although it is more metaphorical, bitcoin is also accepted because it is mined like crude oil, namely, a commodity. These similarities can be investigated by analyzing the connectedness among these financial assets. The connectedness results derived from both total connectedness and frequency connectedness methods indicate that volatility connectedness is higher than the return connectedness among these assets. Furthermore, connectedness in volatilities is mostly driven by medium frequency, although connectedness in returns mostly exists in high frequency. Therefore, these results suggest that investors should consider these financial assets for their diversification decisions. The results suggest that although diversification among these three assets is more difficult in the short- and medium-term, investors may benefit from diversification in the long-run. Full article

This paper is the first to study a comparatively new Environmental Kuznets Curve which traces empirically the relationship between environmental abatement and real GDP. Our model is a partial linear semi parametric model that allows for two way fixed effects to eliminate the bias arising from two sources. We use data for recycling and real GDP, for fifty states of the United States for the years between 1988 and 2017. We find evidence that this relationship is characterized by an increasing curve which confirms the existence of a J curve, a finding that agrees with the predictions from recent theoretical models. Full article

This paper addresses the application of financial innovations from the corporate finance perspective. The objective is to identify and prioritize the main types of barriers to the implementation of financial innovations by nonfinancial firms. The motivation behind the study lies in the importance of financial innovations for the firms’ ability to create value. As proven by the extensive literature review, comprehensive studies on financial innovation applications by nonfinancial firms are relatively rare. To cover this cognitive gap, the theoretical argumentation followed by the discussion of results of the empirical research are presented in this paper. The paper provides the results of two-stage survey research, aiming to find opinions of financial managers (end-users) and experts (creators of innovation) on the main barriers to financial innovations in Poland. According to managers, the most important are exogenous barriers, including: (1) Unclear tax and accounting regulations, (2) complex construction of financial innovations, and (3) transaction costs related to their application. On the other side, the experts from financial institutions recognized the greater importance of endogenous factors such as: (1) Lack of sufficient knowledge about financial innovations and (2) the reluctance to change observable in many firms. This study contributes to the ongoing debate on financial innovations by adding the perspective of corporate financial strategy. It also offers insights into the potential actions (at the institutional and individual level) aiming to reduce the barriers and support the implementation of financial innovations by nonfinancial firms. Full article

This paper fits into the stream of current research on the concept of short-termism and its importance for economic sustainability, especially sustainable finance. Short-termism focuses on short time horizons by both corporate managers and the financial markets, and prioritizes short-time shareholder return over the long-term growth of the company’s value. This study engages the short-termism discussion by examining the effect of quarterly reporting on the long-term market value of listed companies. The aim of the article is to determine whether European companies experience the negative effects of short-termism, precisely, whether public companies that prepare quarterly reports, and which focus mainly on achieving the short-term goals of stock exchange investors, are seeing a decline in their market value in the long-term. We have not proven the existence of such a dependence, the increase in reporting frequency of public companies does not contribute to a decline in their long-term market value. In the case of the EU-15 the results of regression model estimation indicate a positive and statistically significant impact of the time of regular quarterly reporting on the buy-and-hold rates of return, in the “new” EU member states this relationship is not observed. Full article

Community currencies are only sometimes economically advantageous. We focus on seasonal changes in money supply and assume that community currencies stabilize the money supply in a local community. This leads to additional transactions during seasons of insufficient supply of national currency. We hypothesize community currencies are therefore economically advantageous in a surrounding of seasonally insufficient money supply. We test the hypothesis qualitatively with two case studies, the German Chiemgauer and the Kenyan Sarafu Credit. We find community currencies are only economically advantageous in an environment of insufficient liquidity. Full article

Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web. Full article

The debate regarding the suitability of market orientation or culture of sustainability for corporate social responsibility (CSR) implementation and economic sustainability deserve much more scholarly attention as globalization and competition in emerging markets increases. Using qualitative content analysis of interviews with 28 senior managers of large agribusiness firms in India, this empirical article explores how market orientation or culture of sustainability affects CSR implementation, or vice versa? The findings of the study identify factors such as the nature of a firm’s business, sensitivity, commitment towards sustainable development, and pressure on profitability that prompt firms to adopt sustainability dominant, market dominant, and sustainability–market mixed corporate culture. Culture of sustainability dominant firms are likely to implement CSR more smoothly and effectively compared to firms that are driven by market orientation. Moreover, firms committed to substantial and consistent CSR are likely to induce culture of sustainability in firms. Finally, the study offers a framework that provides insights into how CSR program implementation and a culture of sustainability are complementary and could strengthen the economic sustainability of firms in emerging markets. Full article

This paper studies the determinants of German direct investment in the Central and Eastern European countries during the period 1996–2016 using the augmented Knowledge Capital model to identify the main reasons for foreign direct investment (FDI). The empirical results show increasing multinational enterprise (MNE) activity with growth in country-size and with growing similarities of countries, which supports the horizontal reason for FDI; while the difference in the share of skilled labor force associated with the vertical reason has no effect. Furthermore, the estimation results show unimportance of trade costs to the foreign market and the significance of the distance between source and host countries. Full article

The present study investigated the factors that influence the feasibility and competitive advantage of a digital freight forwarder through a binary logistic regression model. The research is a concrete application of sustainable entrepreneurship in the transport and supply chain sector. The novelty of this topic presents a research gap that needs to be covered with dedicated studies. After the literature review and concept clarification, the article presents quantitative research involving an online questionnaire administered among a sample of transporters in Romania. Through analysis of the data collected from 405 respondents, it was found that the most important factors when selecting a digital freight forwarder are the existence of both sales and dispatch departments. Furthermore, apart from greening the industry, a digital freight forwarder has several other advantages for all stakeholders and society. The study concludes that the concept has the potential to disrupt the entire industry through a unique combination of efficiency, transparency, and sustainability. Full article

This paper explores a decision analysis on product integration of family security services and residential fire insurance in the London and Taiwan markets by using the proposed mathematical models for counting sustainable value. This paper shows the five main different results between London and Taiwan markets with ten different parameters of the family security market, to find out the optimal number of family security integrated services for each security company in London. The improvement of the risk aversion effect based on risk and financial management will enhance the market share of the private security industries in the London and Taiwan markets. The results of this research can serve as a reference for the decision-making of private security industries on product integration under sustainable value consideration. The research findings highlight the potential benefits for both the private security industry and the insurance industry in their design and negotiation for product integration to improve both of business operation and achieve corporate social responsibility goals to match the sustainability in the future. Full article

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context. Full article

Sustainable corporate finance is an attractive field of study in sustainability literature; however, the literature lacks systematic bibliometric analysis that provides a comprehensive review to clarify state-of-the-art sustainable corporate finance and that discusses new opportunities and potential instructions for further studies. To address this gap, this study adopts a literature review, bibliometric analysis, network analysis and co-wording technique to systematically investigate the Scopus database. In total, 30 keywords listed at least three times are used and are divided into six clusters considering six fields of research, namely, corporate finance in corporate sustainability, sustainable competitive advantages, sustainable stakeholder engagement, circular economy, sustainable corporate finance innovation and risk management and sustainable supply chain ethics. This study contributes to examining the sustainable corporate finance bibliometric status to provide directions for future studies and practical accomplishment. The sustainable corporate finance knowledge gaps are (1) corporate finance in sustainability; (2) sustainable competitive advantages; (3) sustainable stakeholder engagement; (4) circular economy; (5) sustainable corporate finance innovation and risk management; and (6) sustainable supply chain ethics. The knowledge gaps and future directions are also discussed. Full article

This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant. Full article