Periods Of High Uncertainty Research Articles

Mean–variance relationship and uncertainty

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

Uncertainty and Public Investment Multipliers: The Role of Economic Confidence

This paper investigates how macroeconomic uncertainty affects the fiscal multiplier of public investment. In theory, uncertainty can reduce the multiplier if the private sector becomes more cautious and does not respond to the fiscal stimulus. Conversely, it can increase the fiscal multiplier if public investment shocks improve private agents’ expectations about future economic outlook, and lead to larger private spending. Using the disagreement about GDP forecasts as a proxy for uncertainty, we find that unexpected increases in public investment have larger and longer-lasting effects on output, investment, and employment during periods of high uncertainty, with multipliers above 2, and the larger multipliers are not driven by economic slack. Public investment shocks are also found to boost private sector confidence during heightened uncertainty, driving-up expectations about future economic development which in turn magnify private sector response to the initial stimulus.

TP8.2.27 Unlocking growth options in surgical education and training during the pandemic

IntroductionThe COVID-19 pandemic brought widespread disruption to structured surgical education and training. The knee-jerk reaction is often pessimism about surgical training’s future, particularly in the Improved Surgical Training (IST) pilot’s context. However, Einstein famously once said, “In the midst of every crises lies great opportunity”. Unlocking growth during periods of high uncertainty is a premise of real options theory; one utilised by supply chain managers and decision scientists, but novel to medical education. This study explores the growth options that have resulted from new operational models during the pandemic.MethodsUsing qualitative case study approach, data were obtained from interviews with core surgical trainees across Scotland. Data coding and inductive thematic analysis were undertaken.ResultsForty-six trainees participated. Analysis from trainees’ perspective revealed: unexpected fulfilment from redeployment to non-surgical specialties, benefits to personal development from the unintended broad-based training across surgical specialties, improved collaborative teamworking between specialties and allied healthcare professionals, and enhanced supervised learning opportunities. Institutional growth options reported by trainees included: rapid uptake of telemedicine and digital technology, implementation of single hospital episode encounters for minor conditions, streamlined processes in theatre and acute admissions, and changes in working culture towards rationalising and teamworking.ConclusionGrowth options have been deliberately and unintentionally unlocked due to individual and institutional adaptions and innovations in response to exogenous disruption. While some changes may be temporary, hopefully structured reflection on these changes and responders to them will drive surgical education and training into a new sustainable and resilient post-pandemic era.

1253 Unlocking Growth Options in Surgical Education and Training During the Pandemic

IntroductionThe COVID-19 pandemic brought widespread disruption to structured surgical education and training. The knee-jerk reaction is often pessimism about surgical training’s future, particularly in the Improved Surgical Training (IST) pilot’s context. However, Einstein famously once said, “In the midst of every crises lies great opportunity”. Unlocking growth during periods of high uncertainty is a premise of real options theory; one utilised by supply chain managers and decision scientists, but novel to medical education. This study explores the growth options that have resulted from new operational models during the pandemic.MethodUsing a qualitative case study approach, data were obtained from interviews with core surgical trainees across Scotland. Data coding and inductive thematic analysis were undertaken.ResultsForty-six trainees participated. Analysis from trainees’ perspective revealed: unexpected fulfilment from redeployment to non-surgical specialties, benefits to personal development from the unintended broad-based training across surgical specialties, improved collaborative teamworking between specialties and allied healthcare professionals, and enhanced supervised learning opportunities. Institutional growth options reported by trainees included: rapid uptake of telemedicine and digital technology, implementation of single hospital episode encounters for minor conditions, streamlined processes in theatre and acute admissions, and changes in working culture towards rationalising and teamworking.ConclusionsGrowth options have been deliberately and unintentionally unlocked due to individual and institutional adaptions and innovations in response to the exogenous disruption. While some changes may be temporary, hopefully structured reflection on these changes and responders to them will drive surgical education and training into a new sustainable and resilient post-pandemic era.

Commodity market risks and green investments: Evidence from India

Green investing has recently received considerable attention in India in light of the local government plans to significantly reduce the level of CO2 emissions through conducting and funding various types of eco-friendly infrastructures and green projects on a national scale. Given that investment in environmentally friendly projects is still relatively new in India, the stock prices of Indian green companies could be highly volatile and very prone to risk transmission from other assets, which requires a precise estimation of the time-varying volatility of these green stocks to understand their underlying risk. In this paper, we examine if the stock volatility of Indian green companies can be predicted based on the information contents of commodity market implied volatility indexes (VIX) by employing a GARCH-based quantile regression model on daily data. The results show that risk significantly transmits from crude oil, gold, and silver markets to various Indian green stock indexes. The impact of the transmission from commodity market VIXs is stronger during the bearish stock market periods compared to bullish stock market periods, suggesting that green stock indexes are more likely to receive volatility from energy and precious metal markets during the periods of high uncertainty. Our findings are useful to socially responsible investors who focus not only on the environmental performance of a firm but also consider its financial performance.

Doing good in periods of high uncertainty: Economic policy uncertainty, corporate social responsibility, and analyst forecast error

We investigate the behavior of analyst earnings forecast error in response to policy uncertainty. We find that the accuracy of analyst forecasts is compromised at times of increased economic policy uncertainty, when market volatility and information opacity are high. This negative association between policy uncertainty and earnings forecast accuracy is alleviated in firms where good CSR practices are in place. The disclosure of CSR-related nonfinancial information reportedly improves forecast accuracy; in the context of policy uncertainty, we document that CSR plays a stabilizing role by moderating analyst forecast error. Additionally, we report that the moderating effect of CSR is more pronounced in domestic rather than multinational firms, where, for the former, analysts are able to better assess the quality of information. Finally, we observe that analysts attach more value to the external legitimacy aspect of CSR rather than internal sustainability in guaranteeing earnings resilience in the face of high policy uncertainty. Our results remain valid in various robustness settings.

The correlation between the stock market and Bitcoin during COVID-19 and other uncertainty periods

This research examined the impact of the stock market on Bitcoin during COVID-19 and other uncertainty periods. Based on the quantile regression results, during periods of high uncertainty, such as COVID-19, the S&P 500 returns significantly affected Bitcoin returns. Moreover, this research applied the VAR (1)–GARCH (1, 1) model to investigate the spillover effect from the stock market to Bitcoin. According to the findings, the shocks from the stock market also influenced Bitcoin's volatility during COVID-19 and other periods of turmoil.

Effectives of monetary policy under the high and low economic uncertainty states: Evidence from the major Asian economies

This study examines the monetary policy effectiveness of five major Asian countries (China, Hong Kong, India, Japan, and South Korea) using a quantile vector autoregression (QVAR) model-based spillover estimation approach of Balcilar et al. (2020b) at different quantile paths. To do this, we first obtain the spillover index from interest rate to industrial production and consumer price index under the high and low levels of uncertainty. The full sample results from our analysis provide partial supporting evidence for the economic theory, which asserts that monetary policy efficiency must fall during periods of high economic uncertainty. Furthermore, this approach also allows us to uncover asymmetric effects of economic policy uncertainty and lending rate on macroeconomic indicators. The impacts of interest rate and domestic and foreign (both the EPU of US and EU) uncertainty shocks on major Asian markets present significant asymmetric characteristics. Moreover, our time-varying results suggest that monetary policy shocks are more effective and potent on Asian economies during the low and high uncertainty periods than normal economic periods.

How do stock, oil, and economic policy uncertainty influence the green bond market?

This study investigates the effects of stock volatility, oil volatility, and economic policy uncertainty (EPU) on green bonk returns, which would shed some light on the diversification capability of green bond markets. The empirical analysis includes four major green bond indices and three uncertainty indices, namely the VIX, OVX, and EPU indices, from October 2014 to November 2020. The results indicate that the connection between green bond and uncertainty is time-varying and state-dependent. During periods of low uncertainty, green bond and uncertainty are weakly connected, thus, green bond can be used to hedge against uncertainty during these periods. These diversification benefits are lower during periods of high uncertainty.

Exploring the Effect of the COVID-19 Pandemic on the Dental Team: Preparedness, Psychological Impacts and Emotional Reactions.

Background: The COVID-19 pandemic has placed increased demands on clinical staff in primary dental care due to a variety of uncertainties. Current reports on staff responses have tended to be brief enquiries without some theoretical explanation supported by developed measurement systems.Aim: To investigate features of health and well-being as an outcome of the uncertainties surrounding COVID-19 for dentists and dental health professionals in primary dental care and for those in training. In addition, the study examined the well-being indices with reference to normative values. Finally a theoretical model was explored to explain depressive symptoms and investigate its generalisability across dentists and dental health professionals in primary dental care and those in postgraduate training.Methods: A cross-sectional survey of dental trainees and primary dental care staff in Scotland was conducted in June to October 2020. Assessment was through “Portal,” an online tool used for course bookings/management administered by NHS Education for Scotland. A non-probability convenience sample was employed to recruit participants. The questionnaire consisted of four multi-item scales including: preparedness (14 items of the DPPPS), burnout (the 9 item emotional exhaustion subscale and 5 items of the depersonalisation subscale of the MBI), the 22 item Impact of Event Scale-Revised, and depressive symptomatology using the Patient Health Questionnaire-2. Analysis was performed to compare the levels of these assessments between trainees and primary dental care staff and a theoretically based path model to explain depressive symptomology, utilising structural equation modelling.Results: Approximately, 27% of all 329 respondents reported significant depressive symptomology and 55% of primary care staff rated themselves as emotionally exhausted. Primary care staff (n = 218) felt less prepared for managing their health, coping with uncertainty and financial insecurity compared with their trainee (n = 111) counterparts (all p's < 0.05). Depressive symptomology was rated higher than reported community samples (p < 0.05) The overall fit of the raw data applied to the theoretical model confirmed that preparedness (negative association) and trauma associated with COVID-19 (positive association) were significant factors predicting lowered mood (chi-square = 46.7, df = 21, p = 0.001; CFI = 0.98, RMSEA = 0.06, SRMR = 0.03). Burnout was indirectly implicated and a major path from trauma to burnout was found to be significant in primary care staff but absent in trainees (p < 0.002).Conclusion: These initial findings demonstrate the possible benefit of resourcing staff support and interventions to assist dental staff to prepare during periods of high uncertainty resulting from the recent COVID-19 pandemic.

Vision-Based Uncertainty-Aware Lane Keeping Strategy Using Deep Reinforcement Learning

Abstract Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.

The effect of uncertainty on the information content of term spread and its components

Purpose This paper aims to investigate the impact of uncertainty on the predictive power of term spread and its components for future stock market returns and economic activity in Korea and the USA. This paper finds that the stock market’s expected excess return and growth of economic activity are positively related to the risk-neutral expectation, one of the term spread’s components, particularly during high uncertainty periods. These findings are consistent with the importance of the monetary policy by the central bank in a high uncertainty environment created by unexpected shocks. The results are robust to alternate definitions of high uncertainty periods.

Crude oil volatility and the biodiesel feedstock market in Malaysia during the 2014 oil price decline and the COVID-19 outbreak

Although the global crude oil market plays a significant role in pricing edible oils, the association between energy price uncertainty and the Malaysian palm oil industry remains understudied. Given that palm oil is widely used as a cheap feedstock for biodiesel, it is important to investigate whether risk transmits from the oil market to the Malaysian palm oil industry. Employing GARCH-jump models, this study extends the scant literature. The results reveal that the crude oil volatility index (OVX) significantly influences palm oil prices suggesting that an upturn in oil market volatility negatively impacts palm oil prices. Subsample analyses show that the negative impact of OVX intensified during the 2014 oil price decline and the COVID-19 outbreak. The effect of OVX is asymmetric, implying that changes (upward and downward shifts) in oil price variance exert a heterogeneous impact on the price levels of this edible oil. It is also observed that palm oil prices experience time-dependent jumps. We show that volatility significantly transfers from the crude oil to the palm oil market during periods of high uncertainty. Hence, investors and policymakers could use the information content of OVX for forecasting future trends in palm oil prices.

Moeda Seeds: E-Commerce and Crypto Solutions for Development

ABSTRACT Blockchain technology has been explored as a tool to generate social impact. An example of it is Moeda Seeds, a Brazilian ecosystem operating to support financial inclusion and gender equality, two UN sustainable development goals, by facilitating access to microfinance for female entrepreneurs with social impact projects. Traditionally facing difficulties to access credit in the traditional banks, the selected Seed Projects are also granted with management support from Moeda’s ecosystem to optimize its entire chain efficiency and helping them to promote long-term sustainable practices for the local communities to thrive. This whole ecosystem is enabled by a blockchain-based payment and lending platform and Moeda’s proprietary digital tokens, which people around the world can promptly and transparently invest while participating in sustainable growth. This teaching case brings the COVID-19 crisis as background panorama and the founder Taynaah Reis in the center of an urgent strategic decision-making process. It covers the difficulties, variables, and alternatives presented to structure a contingency plan during that period of high uncertainty. It is extended by the teaching notes, covering: social cryptocurrencies, blockchain, digital platform, online sales, network externalities, and e-commerce ecosystem.

Endogenous Production Networks under Uncertainty

Supply chain disturbances can lead to important increases in production costs. When looking for new suppliers, firms therefore worry about the risks associated with potential trading partners. We investigate the impact of beliefs and uncertainty on the structure of the production network and, through that channel, on the level and volatility of macroeconomic aggregates. To do so, we construct a model of endogenous network formation in which uncertainty about future outcomes affects the structure of the production network. In the model, when uncertainty rises producers prefer to purchase from more stable suppliers, even though they might sell at higher prices. The resulting reorganization of the network leads to less macroeconomic volatility, but at the cost of a decline in aggregate output. We calibrate the model on U.S. data and find that the mechanism can account for a sizable decline in expected GDP during periods of high uncertainty like the Great Recession.

Особливості прогнозування показників грошово-кредитного та фондового ринків за умови макроекономічних збурень у країнах, що розвиваються

The purpose of the article is to improve approaches to forecasting and analyzing the sensitivity of money-and-credit and stock markets to macroeconomic shocks in developing countries, which are characterized by a high level of socio-economic uncertainty and political risks. The article substantiates the advantages of expanding the standard forecasting methodology based on one-sided forecasts by analyzing the sensitivity of the indicators studied to macroeconomic perturbations, especially in the period of high uncertainty observed in recent years. An empirical analysis of the reaction of the main indicators of monetary policy and stock index in countries with a transformational economy in response to macroeconomic shocks during the short term was carried out, using impulse response functions. It was identified that in the markets of most countries there was a stabilization of the stock index and the short-term interest rates during the first 2-6 months after the start of macroeconomic shocks, which was accompanied by their moderate periodic fluctuations. In only three of the seven developing countries studied, stock indexes and/or interest rates could not reach equilibrium levels on their own during the short term and required active central bank stabilization measures. It can be assumed that the degree of reaction of the stock and money-and-credit markets is primarily associated with the individual characteristics of the country’s economic system and its ability to absorb macroeconomic shocks. The carried out analysis of sensitivity allowed to determine what shocks have a critical impact on the functioning of the financial and money-and-credit markets in each of the economies under research.

Cash Is Not Always King: Economic Policy Uncertainty and the Marginal Value of Corporate Cash Holdings

This paper studies the relation between economic policy uncertainty (EPU) and the marginal value of corporate cash holdings. We find that the markets place a lower value on firms’ cash holdings under high level of EPU. Our quasi-experiment tests relating to the 9/11 terrorist attacks and the gubernatorial elections support the causal relationship between EPU and the value of cash holdings. Further, we show that this relation is more pronounced for firms with less redeployable capital assets, more investment opportunities, poor corporate governance, or positive excess cash. Overall, our evidence suggests that cash is not always king especially in periods of high uncertainty of policies.

THE ROLES OF ECONOMIC POLICY UNCERTAINTY AND THE COVID-19 PANDEMIC IN THE CORRELATION BETWEEN CRYPTOCURRENCY AND STOCK MARKETS

We are the first to explore the effect of economic policy uncertainty (EPU) and the COVID-19 pandemic on the correlation between the cryptocurrency index CRIX and the world stock market portfolio, as well as the hedging properties of CRIX. To this end, we mainly apply the dynamic conditional correlation model with mixed data sampling regressions, a threshold vector autoregressive model and the generalized impulse response function. We demonstrate that the correlation is influenced by the uncertainty stance of the economy and behaves differently in low-, medium- and high-uncertainty periods. Most of the abnormal market relations exist in high levels of EPU or during the COVID-19 period, and the impact of global EPU is greater than that of EPU originating in the United States, Europe, Russia and China. Moreover, the CRIX can serve as a hedge asset against the world stock market. The high (low) level of EPU has a significantly positive (negative) effect on the optimal hedge ratio of CRIX, which increases significantly during the COVID-19 period. Our findings have implications for risk management, portfolio allocations and hedging strategies.

Tail Risk and Expectations

We study changes in expectations by incorporating tail risk in a Bayesian learning framework with information frictions. Using a signal extraction problem, we find that economic agents behave differently in the face of tail risk. First, under tail risk, uncertainty shocks lead to a decrease in expectations, which implies more pessimistic forecasts. In comparison, in the absence of tail risks, uncertainty shocks do not influence expectations. Second, we show that individuals overreact under tail risk, that is, individuals are excessively optimistic and pessimistic as compared to a Bayesian learning framework without tail risk. Third, the magnitude of overreaction under tail risk depends on the level of uncertainty in the economy. To validate these theoretical findings, we use quantile regressions to estimate the GDP growth distribution and its measure of tail risk. By comparing the state of the economy in tail risk and non-tail risk episodes, we find that uncertainty shocks lead to a larger fall in sentiments when the economy exhibits tail risk. In addition, using an event study approach, we show that there is more overreaction under tail risk episodes. Also, consistent with the theoretical findings, overreaction is larger in periods of higher uncertainty. Our findings seek to shed light on factors driving overreaction in expectations, and to highlight the importance of uncertainty shocks in propagating macroeconomic stability.

Policy uncertainty in Australian financial markets

Economic policy touches most facets of corporate decision-making and variations in policy can elicit significant changes in financial performance and asset prices. We utilize the economic policy uncertainty (EPU) measure of Baker et al. to investigate the extent to which policy uncertainty influences Australian financial market returns. Our empirical results demonstrate that both domestic and global uncertainty have a significant negative impact on excess stock returns, changes in bond yields and Australian dollar (AUD) returns. The relationship is concentrated in the left tail of the return distribution and largely driven by increases in policy uncertainty. Although the identified relationship is negative throughout the sample period, the magnitude of the relationship appears to be state dependent and is influenced by periods of high uncertainty, recession and the lead-up to federal elections. The most plausible explanation for our results is that uncertainty about economic policy is channelled to financial markets via the discount rate effect, resulting in a higher risk premium. Our results are important for investors, corporate managers and policy makers wishing to navigate periods of policy uncertainty. JEL Classification: G10, G12, G14, G15