Narrow Results

Cryptocurrencies have emerged as a new asset class. In order to provide a thorough understanding of this new asset class, we study the dependencies in tail risk events within cryptocurrencies, and provide a hedging alternative in this paper. First, we adopt the Financial Risk Meter approach for cryptocurrencies, which is able to identify individual risk characteristics and indicate systemic risk in a network topology. Next, we detect the interdependencies across digital coins and study the spillover effects. Finally, we construct tail event sensitive portfolios and test the performance versus traditional approaches from January 2019 to May 2022.

This research examines how the digital economy, energy efficiency, and demographic transition might help Viet Nam achieve more sustainable economic development. The causal association between digitalization, the demographic dividend, energy intensity, and long-term economic development has not been thoroughly investigated yet. Therefore, this paper aims to examine the asymmetric relationship between these indicators in Viet Nam, which is recognized as an emerging economy, using the relatively novel quantile-on-quantile regression and Granger causality approaches in different quantiles. The empirical findings suggest that the asymmetric interaction between digital innovation, energy intensity, demographic change, and economic growth in Viet Nam is primarily positive, even though there are slight differences across various quantiles of the selected indicators. In addition, the outcomes of Granger causality in quantile analysis uncover that, during the sample period, a bidirectional association exists between digitalization, the demographic dividend, and economic growth, while a unidirectional causality runs from energy intensity to economic growth.

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

Using a quantile regression approach with a sample of Korean firms, this paper empirically investigates whether cash holding behaviors with the precautionary motive are different between Chaebol and non-Chaebol firms and they have changed before and after the financial crisis. We obtain the following empirical results. First, for non-Chaebol firms, the precautionary motive plays an important role in determining cash holdings throughout the sample period. In contrast, there is no empirical evidence supporting that Chaebol firms hold cash with the precautionary motive in the pre-crisis period: This motive becomes important only in the post-crisis period. Second Chaebol firms’ cash holdings with the precautionary motive are different from those of non-Chaebol firms in the pre-crisis period, but not in the post-crisis period. These imply that after experiencing financial crisis, Chaebol firms’ cash holding strategy has been changed substantially in a way to become more cautious in dealing with cash flow (CF) volatility.

This study examines the impact of foreign aid on the institutional quality (IQ) of the OIC countries. Using the data of OIC countries for the three-year average period from 1991 to 2016, the system GMM finds that aid in general deteriorates the IQ for the aid recipient countries. However, quantile regression suggests that the negative impact of foreign aid on institutional quality (IQ) is relatively greater in the countries where the existing quality of institution is poor. The findings of the study suggest that improving the existing capacity is essential for reaping the optimum benefit of foreign aid on institutional development.

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly-traded financial institutions. We demonstrate the suitability of this AI-based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword  FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.

Although the rapid development of financial technology has been realized throughout the Asian countries, research studies seldom focus on the growing impact of financial technology on poverty alleviation. The research paper explores financial technology’s effect on poverty alleviation in the Asian countries. The dataset was collected from Asian countries between 2010 and 2021. A web search technology and frequent word analytics tool are employed to conceptualize variables, and after that, each selected region’s financial technology index is constructed. Given the variation in poverty across selected regions, a novel approach of vector quantile regression is used to quantify the effect of financial technology on poverty allocation. The suggested method’s parametric estimators are employed as “location” measurements of poverty. The findings of this research reveal that despite an uneven index of financial technology development across Asian economies, financial technology significantly impacts poverty alleviation in every selected Asian economy. Furthermore, financial technology strongly impacts poverty alleviation in developing economies than emerging economies of Asian countries. Considering the above findings, practitioners, governments and policy-makers need to foster advanced financial technology mechanisms, specifically in developing economies of the selected Asian economies.

This study uses the panel quantile regression model and conducts path analysis to examine the impact of inter-provincial digital economy development in China on carbon emission reduction. Results reveal several key points: First, the impact of digital economy development on carbon emission reduction varies significantly across quantiles; it can promote or inhibit emissions and shows an inverted “U” relationship. Second, the inhibition effect of the digital economy on carbon emissions is significantly stronger than the growth effect. Third, more than half of the studied regions show an inhibitory effect of the digital economy on carbon emissions, primarily through indirect impact paths. Among these paths, the three most significant ones are the improvement of urbanization level, resident affluence level, and per capita gross domestic product (GDP). Policy recommendations are discussed.

In this paper, we use quantile-based analysis and nonlinear conditional autoregressive value-at-risk (CAViaR) model to investigate how cryptocurrency price and policy uncertainty indexes affect exchange traded funds (ETFs) investing in equity securities of companies operating in various disruptive innovation sectors and related to digital assets and blockchain technology. Results of the quantile-on-quantile (QQ) estimates and quantile regression reveal that cryptocurrency price and policy uncertainty generally have a negative impact on the globally traded ETFs suggesting that the changes in cryptocurrency uncertainty lead to destabilizing effects in the ETF market. Moreover, we find that the impact of cryptocurrency uncertainty is asymmetric depending on each quantile of ETF returns even though each ETF is influenced in the same way by each uncertainty index, indicating that cryptocurrency price uncertainty and cryptocurrency policy uncertainty have common effects on the ETF market. A linear quantile causality analysis indicated that there is evidence of a significant causality running from the considered uncertainty indexes to the ETF returns, revealing that cryptocurrency price and policy uncertainty have a significant information value in explaining expected ETF returns. On the other hand, we find that linear quantile models outperform nonlinear CAViaR models in quantiles that are only moderate.

This study investigates gold as a hedge or a safe haven against inflation in four countries. We propose two standard and quantile techniques in the volatility models, with a time-varying conditional variance of regression residuals based on TGARCH specifications. Gold exhibits considerable evidence of a strong hedge in the US and China. Nevertheless, gold provides shelter at different times and not consistently across countries. With regards to be a safe haven, gold retains its status as a key investment in China. On the other hand, gold only plays a minor role in the UK and India. These findings indicate that gold can secure Chinese investment during the high inflationary periods, while gold is a profitable asset to hold over a long period of time in the US. In contrast, UK and Indian investors should hold a well-diversified portfolio for sustainable return and protection from purchasing power loss.

Quantile regression methods have been used widely in finance to alleviate estimation problems related to the impact of outliers and the fat-tailed error distribution of financial returns. However, a potential problem with the conventional quantile regression method is that the estimated conditional quantiles may cross over, leading to a failure of the analysis. It is noticed that the crossing over issues usually occur at high or low quantile levels, which are the quantile levels of great interest when analyzing financial returns. Several methods have appeared in the literature to tackle this problem. This study compares three methods, i.e. Cai & Jiang, Bondell et al. and Schnabel & Eilers, for estimating noncrossing conditional quantiles by using four financial return series. We found that all these methods provide similar quantiles at nonextreme quantile levels. However, at extreme quantile levels, the methods of Bondell et al. and Schnabel & Eilers may underestimate (overestimate) upper (lower) extreme quantiles, while that of Cai & Jiang may overestimate (underestimate) upper (lower) extreme quantiles. All methods provide similar median forecasts.

This article investigates share price responses to strategic alliances in Taiwan's high-tech industry from 1996 to 1999. We analyze the determinants of abnormal returns caused by strategic alliances using a quantile regression estimation procedure. Our empirical findings show that the wealth effect for a strategic alliance is positive, with no evidence of a wealth transfer between alliance partners. In addition, intra-industry alliances show significantly higher abnormal returns than inter-industry alliances. The horizontal and vertical alliances both have significantly positive abnormal returns, but the positive effect of vertical alliances primarily comes from downstream buyers. On the other hand, results from a quantile regression model show that the P/B ratio, director and supervisor shareholding ratios, and R&D ratio are important determinants of abnormal returns of strategic alliance announcements. Our empirical findings are consistent with the argument that the organizational flexibility offered by alliances is valuable to the high-tech industry which needs to cope with a fast-changing environment.

The main purpose of this paper is to explore the issue of enterprise's transfer pricing which has been noticed by tax administrations of many nations in recent years. The academia has also striven to develop models for interpreting transfer pricing and assessing transfer pricing profit of enterprises. A precise model is applied in this study to interpret enterprise's transfer pricing in practice. Furthermore, this model is also used to assess the transfer pricing profit of listing/OTC electronics companies in Taiwan and study the factors affecting transfer pricing profit. The empirical results show that material expense ratio significantly impacts transfer pricing profit. Corporate income tax expenses are positively and significantly correlated with net incomes of corporate transfer pricing transactions.

This study also finds that regardless of whether the net income of corporate transfer pricing transactions is high, medium, or low, the proportion of material costs is positively and significantly correlated with the net income of corporate transfer pricing transactions in all scenarios. Higher profit will be affected by exchange rate. This suggests that companies involved in wafer fabrication, IC packaging and testing, and DRAM packaging and testing (subcontractors with relatively low material costs) are less likely to be involved in transfer pricing decisions.

In this paper, we investigated (i) the possible nonlinear effects in the determinants of capital structures and (ii) the nonlinear adjustment behavior of cross-sectional debt ratios in Australian firms. Our analysis utilized the quantile regression methodology and examined the existence of nonlinear relationships for Australian financial data from 1991 through 2007. In our results, we first were able to confirm the existence of nonlinear effects between debt ratio and explanatory variables such as firm size and profitability. However, this nonlinearity was not conspicuous for the independent variables of asset tangibility and non-debt tax shield. Also, the non-linear speeds of cross-sectional debt adjustment were confirmed to exist. This research introduces a new perspective on nonlinear effects into the capital structure literature and subsequently serves as a contribution to the previous literature.

This study examines how the investment horizon of the institutional shareholders of a firm affects the stock performance of private equity placements. The results show that firms with long-term institutional investors receive significantly positive abnormal returns around the offering announcement. Post-issue stock price underperformance is especially pronounced in firms held by short-term institutional investors. These findings suggest that private placement firms with long-term institutional investors acquire certification and monitoring-related benefits and thus reduce the information asymmetry and entrenchment costs between managers and external investors.

The effect of financial leverage on corporate performance has been debated. We reexamine the effect by using a sample of 6,630 observations from nonfinancial Taiwanese publicly traded companies during the 2008–2012 period, employing the quantile regression approach and comparing its results with the ones provided by conventional models (least squares and fixed effects). Our empirical results show that the effect of financial leverage on the corporate performance is not homogeneous among various quantile levels: the financial leverage destroys (enhances) companies with low (high) return on equity quantiles. Moreover, the association between leverage and corporate performance is trivial when the mid-range performance quantiles are considered. Our findings are consistent with the results provided by Lee and Li [Lee, BS and M-YL Li (2012). Journal of Banking and Finance, 36, 2157–2173] for U.S. firms. The asymmetric relationship between financial leverage and the corporate performance identified in this study can adequately clarify the debated link between financial leverage and the corporate performance reported in previous empirical studies.

We propose a new robust and non-parametric denoising method, quantile spectrum decomposition (QSD) that can handle heavy-tailed noise. Our method is an alternative to singular spectrum analysis which is not robust to such noises. In QSD, the trajectory matrix of the signal is decomposed using iterative quantile regressions. This implementation leads to a robust matrix decomposition and robust denoising. Simulations and real data analysis show that QSD is robust to heavy-tailed noise and its performance is promising in the presence of non-heavy-tailed noise.

Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel-based methods for additive models. These learning rates compare favorably in particular in high dimensions to recent results on optimal learning rates for purely nonparametric regularized kernel-based quantile regression using the Gaussian radial basis function kernel, provided the assumption of an additive model is valid. Additionally, a concrete example is presented to show that a Gaussian function depending only on one variable lies in a reproducing kernel Hilbert space generated by an additive Gaussian kernel, but does not belong to the reproducing kernel Hilbert space generated by the multivariate Gaussian kernel of the same variance.

This paper studies some robust regression problems associated with the $q$-norm loss ($q\ge 1$) and the $ϵ$-insensitive $q$-norm loss in the reproducing kernel Hilbert space. We establish a variance-expectation bound under a priori noise condition on the conditional distribution, which is the key technique to measure the error bound. Explicit learning rates will be given under the approximation ability assumptions on the reproducing kernel Hilbert space.

The objective of this paper is to verify how investments in R&D influence the measures of performance (sales and operating profit) of companies. Based on the EU Industrial R&D Investment Scoreboard, a 2003–2013 information panel was developed for 548 companies. Regression with panel data and Quantile Regression were used. The results indicate that investments in R&D positively influence sales and operating profit of companies as a whole, varies according to the R&D intensity when companies are analyzed in groups and that the distance from the frontier influences the relationship between R&D and performance. The companies of the upper quantiles have better relation R&D and profit than the companies more distant from the frontier. Companies with higher profit can transform R&D investments more efficiently into profit.