Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

In this paper, we investigate the relation between time-varying risk aversion and renminbi exchange rate volatility using the conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model. The CARR-MIDAS model is a range-based volatility model, which exploits intraday information regarding the intraday trajectory of the price. Moreover, the model features a MIDAS structure allowing for time-varying risk aversion to drive the long-run volatility dynamics. Our empirical results show that time-varying risk aversion has a significantly negative effect on the long-run volatility of renminbi exchange rate. Moreover, we observe that both intraday ranges and time-varying risk aversion contain important information for forecasting renminbi exchange rate volatility. The range-based CARR-MIDAS model incorporating time-varying risk aversion provides more accurate out-of-sample forecasts of renminbi exchange rate volatility compared to a variety of competing models, including the return-based GARCH, GARCH-MIDAS and GARCH-MIDAS incorporating time-varying risk aversion as well as range-based CARR, CARR-MIDAS and heterogeneous autoregressive (HAR), for forecast horizons of 1 day up to 3 months. This result is robust to alternative risk aversion measure, alternative MIDAS lags as well as alternative out-of-sample periods. Overall, our findings highlight the value of incorporating intraday information and time-varying risk aversion for forecasting the renminbi exchange rate volatility.     

Forecasting with approximate dynamic factor models: The role of non-pervasive shocks

This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.     

Time-varying price shock transmission and volatility spillover in foreign exchange,bond, equity,and commodity markets: Evidence from the United States

We study the cross-market financial shocks transmission mechanism on the foreign exchange, equity, bond, and commodity markets in the United States using a time-varying structural vector autoregression model with stochastic volatility (TV-SVAR-SV). The price shocks are absorbed immediately in two or three days, suggesting that all markets are quite efficient. A slight mean reversion and an overshooting behavior are observed. Considering the volatility spillover effect, we highlight two properties of volatility shocks. First, the effects of the volatility shocks are released gradually. Reaching peak volatility spillover levels would require five to ten days. Second, the dynamics of volatility spillovers vary tremendously over time. Different types of markets respond to certain, but not all, extreme events. Our findings suggest the need to conduct investor monitoring of current events instead of using technical analysis based on historical data. Investors should also diversify their portfolios using assets that can respond to different and extreme shocks.     

Environmental regulation,total factor productivity,and enterprise duration: Evidence from China

Environmental regulation has been confirmed to have an important impact on enterprise productivity, which is regarded as a crucial factor of enterprise duration. However, existing studies have paid little attention to how environmental regulation affects enterprise duration. Using firm‐level data from the Chinese Industrial Enterprises Database (2003–2007) and the stochastic frontier analysis method, we estimate enterprise total factor productivity (TFP) and its four decomposed components (scale efficiency change, technological change, factor allocation efficiency change, and technical efficiency change). Meanwhile, we adopt a comprehensive index to measure the environmental regulation intensity. Furthermore, we use the linear probability model and the proportional hazards model to investigate the effect of environmental regulation on enterprise duration through the mediating role of enterprise TFP. The results show that although environmental regulation per se negatively impacts on enterprise duration, environmental regulation can present a synthetic positive effect on enterprise duration due to its positive effect on enterprise TFP. Specifically, environmental regulation significantly mitigates the scale efficiency and technical efficiency of regulated enterprises. It also stimulates regulated enterprises' technological innovation and improves their factor allocation efficiency. In addition, state‐owned and large‐scale enterprises are more malleable when facing environmental regulations. We propose that the government should encourage enterprises to innovate and improve the allocation efficiency of production factors, so as to achieve the purpose of controlling environmental pollution in stages while extending enterprise duration.     

Impact of Islamic banking development and major macroeconomic variables on economic growth for Islamic countries: Evidence from panel smooth transition models

This study examines the nonlinear relationship between Islamic banking development, major macroeconomic variables and economic growth in Islamic countries. Using the panel smooth transition model, the results show a positive nonlinear relationship between Islamic banking development and economic growth. Moreover, the relationship between the macroeconomic variables and economic growth is asymmetric and regime-dependent. Further, by using the dynamic panel quantile model, we show that for many cases the Islamic banking variables lead economic growth across the quantiles. More specifically, foreign direct investment, oil production and inflation have a positive impact on economic growth during the normal financial development state, while government consumption, one-lag economic growth, terms of trade and financial development have a negative impact on economic growth for this state. The human capital index, education and the rule of law have an insignificant impact, regardless of the prevailing regime. The results for the separated oil-importing and oil-exporting economies are generally consistent with the combined sample regarding the Islamic banking development variables. As for the macro variables, they have a positive and significant (insignificant) effect on EG for the oil-importing (oil-exporting) economies for almost all models.     

The international transmission channels of US supply and demand shocks: Evidence from a non-stationary dynamic factor model for the G7 countries

We employ a multi-country non-stationary dynamic factor model to assess spillover effects and transmission channels of US supply and demand shocks on a variety of macroeconomic variables in individual non-US G7 countries. We find that trade, financial and confidence channels all play a significant role in the international transmission of US shocks. However, the results point to substantial heterogeneities of shock transmission across the individual G7 economies. In particular, we find negative transmission effects for Italy and Japan as the only two G7 countries not well integrated into global value chains. Moreover, the exchange rate responses of Germany, France and Italy turn out to be far less pronounced in comparison to the other G7 economies which we relate to their membership of the euro area and their coordinated monetary policies prior to the establishment of the euro. Whereas we document a close comovement of stock market dynamics across the G7 countries, we find credit and real estate markets to be less synchronized. We do not find the effects and transmission channels to be fundamentally affected by the post-2008 economic environment.     

Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis

Considering the frequency domain and nonlinear characteristics of financial risks, we measure the multiscale financial risk contagion by constructing EMD-Copula-CoVaR models. Using a sample composed of nine international stock markets from January 4, 1999, to May 13, 2021, the empirical study reveals that: (1) EMD-Copula-CoVaR models can effectively measure the multiscale financial risk contagion, and the financial risk contagion is significant at all time scales; (2) The high-frequency component is the major contributor of financial risk contagion; meanwhile, the low-frequency component is the smallest among all time scale components; (3) The risk export of the US financial market to other markets, except the UK under the original and medium-frequency component, is higher than that it receives; and (4) Even though the magnitude of overall financial risk contagion is similar for the COVID-19 pandemic, Subprime Crises, 9/11 terrorist attack and other crises, the relative importance of different frequency components is heterogeneous. Therefore, the countermeasures of risk contagion should be designed according to its multiscale characteristics.     

Managing risk in the formative years: Evidence from young enterprises in Europe

This paper empirically investigates aspects of risk management in young small enterprises? effort to survive and grow. We use a new dataset on several thousands small businesses in their “formative age” (2–8 years old) in 10 European countries and 18 sectors. Firms across all types of sectors use internal risk mitigation strategies to manage technology risk and operational risk. Financial risk is managed by tapping formal and informal networks. Market risk appears less amenable to internal management action. Formal network participation (strategic alliances) is a strategy cutting across all kinds of risk with the exception of operational risk. Firms in knowledge-intensive sectors (high-tech manufacturing and KIBS) engage in risk management activities more extensively. Firms led by more educated entrepreneurs and/or operating in demanding volatile markets tend to network more and to use internal risk mitigation strategies more extensively.     

FDI技术溢出效应的门限特征研究——来自中国省际面板数据的证据

已有的关于FDI(对外直接投资)技术溢出效应研究文献中,大多数学者认为FDI技术溢出会显著地促进东道主国家的经济增长,然而,利用中国1997年-2009年的省际面板数据,对FDI技术溢出效应进行研究却发现,FDI技术溢出效应在中国不同的地区存在显著差异;运用门限回归模型,从地区经济发展水平、地区开放程度、地区人力资本存量、地区金融发展程度等四个方面检验了FDI技术溢出效应的门限特征,并测算出了引发积极FDI技术外溢效应的门限水平。     

30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial

The seed of this special section was the workshop celebrated at FUNCAS in Madrid in February 2019 “30 Years of Cointegration and Dynamic Factor Models Forecasting and its Future with Big Data”. In this editorial, we describe the main contributions of the 13 papers published within the special section towards forecasting in the context of non- stationary Big Data using cointegration or Dynamic Factor Models.     

Risk aversion in the supply chain: Evidence from replenishment decisions

This study investigates the impact of individual risk aversion on replenishment decisions in a multi-echelon supply chain, and explores whether this impact is affected by experiential learning. The methodology applied is that of observational studies, while the multi-echelon supply chain is modeled through the classical Beer Game. Participants in the study are purchasing and supply chain professionals. Results suggest that risk aversion leads to higher orders, Risk aversion persists even after experience of the game has been gained.     

Risk management in life insurance companies: Evidence from Taiwan

The solvency issue of life insurance companies has become more important in recent years as business risks turn increasingly greater. This study examines the relationship among investing risk, underwriting risk, and the capital ratio during the post risk-based capital regulation period of 2004–2009 in Taiwan. In addition to the two-stage least square regression (2SLS), we also adopt the two-stage quantile regression (2SQR) to capture the effects of low capital (or risk) levels and high capital (or risk) levels. 2SLS do not fully explain the capital-risk relation. Contrary to previous evidence reported in the U.S., our findings in 2SQR model indicate that the relationship between capital and underwriting risk is positive, while the relationship between investing risk and capital shows a reverse pattern. Overall, the 2SQR provides stronger evidence than the 2SLS.     

Herding in Open-end Funds: Evidence from China

This paper aims to detect the herding of Chinese open-end fund managers and examine the role that structural features of their industry play in their herding. The herding behaviour is investigated by employing the cross-sectional absolute deviation (CSAD) model, drawing on the Chinese open-end fund market for the 2007–2020 period. Our findings suggest that the presence of herding has been confirmed in all other types of funds except for income funds, with their herding being mainly driven by non-fundamental factors. Most types of funds show more pronounced herding when the market is rising and the fund flow is positive. Up markets foster positive fund flows, which promote herding to a certain extent. The structural characteristics of fund networks of common asset holdings produce an impact on the fund herding. Herding is observed in the network with larger centrality index and the disassortative network. Moreover, we also find that herding in most types of funds is more pronounced under conditions of high volatility and high economic policy uncertainty, and the fund herding tends to grow inversely with fund-size.     

Organizational characteristics explaining participation in sustainable business models in the sharing economy: Evidence from the fashion industry using conjoint analysis

Environmental and social challenges require new sustainable business models, like sharing platforms. However, sharing platforms differ widely in their contribution to a more sustainable society. Whereas idealistic sharing platforms have dominant social goals, other sharing platforms are, or became, commercial. We explore the attractiveness of the typical organizational characteristics of idealistic sharing platforms in the fashion industry context, an industry with negative environmental and social impacts. Based on a literature review and exploratory focus groups, we conduct an online survey using conjoint analysis, completed by 1,512 respondents. Our results reveal that potential users prefer clothes sharing platforms to be small, to not partner with large clothing retailers, to have the possibility to participate in decision‐making, but to not require shareholding.     

Risk spillover between the US and the remaining G7 stock markets using time-varying copulas with Markov switching: Evidence from over a century of data

This paper analyses the risk spillover effect between the US stock market and the remaining G7 stock markets by measuring the conditional Value-at-Risk (CoVaR) using time-varying copula models with Markov switching and data that covers more than 100 years. The main results suggest that the dependence structure varies with time and has distinct high and low dependence regimes. Our findings verify the existence of risk spillover between the US stock market and the remaining G7 stock markets. Furthermore, the results imply the following: 1) abnormal spikes of dynamic CoVaR were induced by well-known historical economic shocks; 2) The value of upside risk spillover is significantly larger than the downside risk spillover and 3) The magnitudes of risk spillover from the remaining G7 countries to the US are significantly larger than that from the US to these countries.     

Compensation for illiquidity in China: Evidence from an alternative measure

This paper shows that liquidity is an important source of priced risk in China. Using A-share stocks in Shanghai and Shenzhen Exchange over the period 2007–2017, we examine the influence of liquidity on stock returns. A new liquidity measure that captures multiple dimensions of liquidity is proposed. Fama-Macbeth cross-sectional regression shows that the expected return is negatively correlated with liquidity. Based on Fama and French (1993), we propose a five-factor pricing model by incorporating reversal factor and liquidity factor. Time-series regressions show that the liquidity factor makes significantly marginal contributions to explaining excess stock returns. The liquidity factor based on the proposed measure works better than alternative liquidity measures such as turnover, Amihud illiquidity measure and the measure in Liu (2006).     

Differentiating two types of learning in contract design: Evidence from the construction industry

This research investigates the learning of inter-organizational contract design in greater depth. Two types of learning, i.e. learning from all past partnerships and learning from one specific partner, are distinguished in terms of their influence on the complexity of three different functions of the contract, namely control, coordination, and adaptation. Contract design capability and interorganizational routines are employed as mediators to explain the two types of learning respectively. Empirical tests using data from the Chinese construction industry reveal that there are significant indirect effects between partner-specific experience and contractual coordination, and between general partnership experience and all the three functions of the contract. This research contributes to the literature by providing more nuanced conclusions regarding the contract learning issue.     

A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP

We derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators. The use of the Kalman smoother allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information for the GDP forecasts beyond the monthly real activity measures. However, this is discovered only if their more timely publication is taken into account properly. Differences in publication lags play a very important role and should be considered in forecast evaluation.     

Monetary policy transparency in a forward-looking market: Evidence from the United States

Because unsatisfactory measures of the monetary policy transparency were used, the existing literature found mixed empirical results for the relationship between the monetary policy transparency and risk/volatility. This paper extends the literature by using a recently developed monetary transparency index [Kia’s (2011) index] which is dynamic and continuous. Furthermore, the existing literature ignores the fact that market participants can be forward looking and, therefore, not policy invariant. This study also finds that the agents in the market are not policy invariant and the more transparent the monetary policy is the less risky and volatile the money market will be.