Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track using a specific form of intercept correction. Among these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and subsequent recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment, and very persistent negative effects on trend growth. Similar findings also emerge for forecasts by institutions, for survey forecasts, and for the other G7 countries.     

Institutional Economics as Theory of Policy Change: Impact of Past Policy Failures on Present Policy

Social scientists have developed several theories for understanding or evaluating policy change over time. Since all costs or benefits are not internalized owing to positive transaction costs, policymaking is always implemented under cost underestimation conditions and, therefore, is imperfect. I call this trait policy failure in this article. Furthermore, I show that a new framework combining the social costs approach and the legal/economic approach in institutional economics is suitable and can be applied to evaluating how past policy failures affect present policy, providing as an example the Fukushima nuclear disaster in Japan.     

Tourists’ hotel event experience and satisfaction: an integrative approach

ABSTRACT

Tourists’ hotel event experiences have received little attention in tourism research. By proposing an integrated model of expectation–confirmation theory (ECT) and the experience economy concept, this research explores the relationship between tourists’ event experience and their satisfaction regarding the hotel event setting. The hypothesized model was empirically validated using a sample of 663 tourists who experienced a holiday event at a resort hotel. Results confirmed that the integration of the experience economy and ECT provided a better understanding of tourists’ post-satisfaction in a pleasure-driven setting. This affective–cognitive approach advances the knowledge of tourists’ experiences and satisfaction at hotel events.     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

A practical road map for assessing cyber risk

The increase in interconnectivity and developments in technology have caused cyber security to become a universal concern. This paper highlights the dangers of the evolution of cyber risk, the challenges of quantifying the impact of cyber-attacks and the feasibility of the traditional actuarial methodologies for quantifying cyber losses. In this paper, we present a practical roadmap for assessing cyber risk, a roadmap that emphasizes the importance of developing a company and culture-specific risk and resilience model. We develop a structure for a Bayesian network to model the financial loss as a function of the key drivers of risk and resilience. We use qualitative scorecard assessment to determine the level of cyber risk exposure and evaluate the effectiveness of resilience efforts in the organization. We highlight the importance of capitalizing on the knowledge of experts within the organization and discuss methods for aggregating multiple assessments. From an enterprise risk management perspective, impact on value should be the primary concern of managers. This paper uses a value-centric/reputational approach to risk management rather than a regulatory/capital-centric approach to risk.     

Determinants of the allocative,cost and scope efficiencies of Indian banks

ABSTRACT

This study aims to investigate the impact of competition on determinants of allocative, scope and cost efficiencies of Indian scheduled commercial banks (SCBs). Specifically, the study, analyzes the impact of the second round of licensing on the efficiency of Indian SCBs. This is the first paper to measure scope efficiency of Indian banks and analyze its determinants. A two-stage analysis is performed on a balanced panel dataset of Indian SCBs for the period 1999–2016. In the first stage, the allocative, cost and scope efficiencies for each bank are estimated following the data envelopment analysis approach. In the second stage, internal determinants of the stated efficiency measures are estimated following the system of the generalized method of moments approach. The findings suggest that competition has not resulted in enhancing the efficiency of Indian SCBs. Among factors that can influence efficiency, it is seen that size does matter. Larger banks can enhance the efficiency of SCBs. It is also seen that having more foreign banks improves the overall efficiency of SCBs. However, before embarking on further rounds of licensing, the study posits that market-driven correction to succeed, it is imperative to address sunspots in the form of investor or borrower repression.     

Developing smart service concepts: morphological analysis using a Novelty-Quality map

Among many techniques for generating new service ideas, morphological analysis has been used due to the advantage of decomposing the system into dimensions and shapes and bringing creative results in the process of recombining them. However, with the rise of smart service systems, the determination of dimensions and shapes has become a critical problem, particularly as both their structure and components become complex. This research focuses on a data-driven approach by incorporating mobile app service documents to increase objectivity and diversity in the construction of a morphology matrix. To this end, firstly, the novelty-quality map is developed to identify innovative data based on quantitative indicators. Secondly, morphological analysis is employed along with experts’ judgment in order to generate new smart service concepts. Finally, the feasibility and effectiveness of the proposed approach are shown based on a comparative analysis with conventional approaches and real services through a case study of smart home.     

Real income convergence and the patterns of financial integration in the EU

We analyse real income convergence among the EU28 countries throughout 1995–2017 and the relationship with the convergence patterns of financial systems. We apply the nonlinear latent factor model of Phillips and Sul (2007, 2009) to real incomes and the IMF financial development indices for financial markets and financial institutions (Svirydzenka, 2016), and identify convergence clubs endogenously. We have several results. First, income disparities narrowed significantly over the last twenty years; yet, the growth convergence process lost momentum triggered by the global financial crisis and countries legacies shaped up asymmetries that have crystallised. Second, countries' financial systems exhibit high fragmentation, especially for financial markets, with the new EU member states at a lower financial development, confirming the existence of a two-tier Europe. Overall, the convergence patterns for real incomes and financial development are strongly correlated. Finally, the financial structure matters and market-oriented economies feature higher long-run growth, indicating the need to implement pan-European policy actions that increase the opportunities of risk diversification, enhance capital raising and channel large-scale financing to firms.     

Public pollution abatement and wage inequality

This paper constructs three-sector general equilibrium models to investigate how public pollution abatement affects the skilled–unskilled wage inequality. In the basic model with full employment, we find that a higher degree of public pollution abatement will decrease the wage inequality if the intensity of skilled labor in the urban skilled sector is sufficiently large and expand or narrow down the wage gap if this intensity is sufficiently small. In the extended models, we consider other four cases, and obtain the results similar or dissimilar to that of the basic model.     

Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures

Using daily data from August 9, 2015, to July 7, 2020, this study examines the effects of economic policy uncertainty (EPU) on the returns of four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Ripple. To this end, two new measures of EPU (Twitter-based economic uncertainty and Twitter-based market uncertainty) are considered. A Granger causality test using the recursive evolving window approach shows a significant causality between the Twitter-based EPU measures and the BTC/USD exchange rate from October 2016 to July 2017. Moreover, a significant causality was noted from the EPU measures to the ETH/USD exchange rate from June 2019 to February 2020 and from the EPU measures to the XRP/USD exchange rate from January 2020 to February 2020. The Twitter-based EPU measures primarily positively affect the returns of the related cryptocurrencies during these periods. These results are robust to different measures of Twitter-based EPU and different econometric techniques. Potential implications, including the COVID-19 era, are also discussed.