Examination of the impacts of the immediate interest rate of the United States and the VIX on the Dow Jones Islamic Market Index

This study examines the effects of the market volatility index of the Chicago Board Options Exchange (VIX) and the immediate interest rate of the United States on the Dow Jones Islamic Market Index (DJIMI) using quantile-based techniques and wavelet coherence (WTC) analysis with monthly data for the period January 2010to May 2021. A quantile cointegration model indicated that the relationship between the VIX and the DJIMI can be valid in the long term since the estimated coefficients are negative and statistically significant across the quantiles 0.05 and 0.50, while a quantile autoregressive model revealed that large negative and positive changes in the VIX and the immediate interest rate of the United States do not have a significant impact on the DJIMI in the short term. Allowing the role of regime changes, it was found by the quantile regression model that an increase in the VIX lowers the performance in the DJIMI, supported by the WTC. It was also underlined that the DJIMI may not benefit from the positive financial conditions. According to the quantile regression models, the immediate interest rate of the US has asymmetrical effects, and the stabilizing effect of the increase/decrease is valid during bearish/bullish market conditions in the DJIMI.     

Cryptocurrency and African fiat currencies: A peaceful coexistence?

This study examines the asymmetric behaviour of Bitcoin relative to six major African fiat currencies (Egyptian Pound, Cedi, ZAR, Naira, Rupee and Dinar) for the period 10 August 2015 to 31 December 2022. The time and frequency information in the time series of the currencies were captured applying the ensemble empirical mode decomposition. The quantile regression (QR) and quantile-in-quantile regression (QQR) were applied on the decomposed series to examine the connections among the currencies at different currency regimes across time. The empirical results show that both QR and QQR can adequately capture the time-varying asymmetric behaviour of the currencies across time. The results range from weak to very strong dependencies albeit both negative and positive across different quantiles. Our findings suggest that except for ZAR, Bitcoin is a viable alternative currency to African reserve currencies from the medium-term since it can hedge depreciation and forex risk of the fiat currencies. Based on the findings of this study, we recommend that forex traders and policymakers in Africa should adopt Bitcoin as an alternative currency to African currencies in the medium-term to mitigate currency crises in the continent.     

Examining comovement and causality between producer price index for P&C insurance premium and uncertainty indices: Wavelet and non-parametric quantile causality approach

This study examines the impact of the news-based climate policy uncertainty index (CPU) on PPI-P&C. To understand the impact of different types of uncertainty on P&C insurance premiums, the study also examines the relation of economic policy uncertainty (EPU), and geopolitical risk uncertainty (GPU) with PPI-P&C index. The time-frequency relation between the indices is examined using wavelet coherence analysis (WCA), whereas the casual dependency is examined using the non-parametric causality in quantiles (CIQ) approach and linear and non-linear Granger causality tests. WCA shows significant co-movement phases between CPU and PPI-P&C across time-frequency domain with CPU leading the PPI-P&C over a specific time interval. Results from CIQ give evidence of uncertainty indices having an asymmetric significant dependency relation with the PPI-P&C index. The results have implications for examining the impact of rising uncertainties on rising insurance costs for P&C insurance providers.     

Okun’s law revisited: a threshold in regression quantiles approach

This article proposes, for the first time, a threshold in regression quantiles approach to the analysis of Okun’s law. By applying to US data over the 1948Q1–2016Q4 period, we have three major findings. First, a single threshold is detected for both multiple and individual quantiles cases. However, the effect of threshold nonlinearity is only present in the middle to upper quantiles of the conditional unemployment distribution in the individual quantiles case. Second, the first-order autoregressive coefficients of unemployment are significantly larger in the lower-growth regime, indicating that shocks to unemployment appear to be more persistent during recessions. Finally, the Okun’s coefficients are all negative across the recessionary and expansionary regimes, confirming the validity of Okun’s law. Moreover, the Okun’s coefficients are smaller (more negative) in the lower-growth regime, suggesting that the effect of differenced output on differenced unemployment is asymmetric, and is more pronounced in recessions.     

Equity market information and credit risk signaling: A quantile cointegrating regression approach

Investigating linkages between credit and equity markets, we consider daily aggregate U.S. CDS spreads as well as well-chosen equity market and implied volatility indexes over ten years. We describe such robust (to spurious correlation) relationship with the quantile cointegrating regression approach. Such approach handles extreme quantiles/CDS values and their behavior with respect to the equity market's influence. Heteroskedastic patterns such as time-varying variance, but also autocorrelation, skewness and leptokurtosis are captured. Thus, the sensitivity of aggregate CDS spreads to equity market price and volatility channels is accurately measured across quantiles and spreads. Such quantile-dependent sensitivity exhibits asymmetric responses to equity market shocks. A sub-period analysis investigates potential regime shifts in estimated quantile cointegrating regressions. Quantile cointegrating coefficients vary over time and quantiles, and exhibit different magnitudes across sub-periods and spreads. Therefore, the relationship is unstable over time. We also propose a scenario analysis and risk signaling application for credit risk management prospects. Under specific risk levels, credit risky situations are described conditional on the equity market's information over time, and related expected aggregate CDS spreads are computed. Estimated conditional quantiles/CDS spreads act as credit alert triggers.     

The persistence of firms’ knowledge base: a quantile approach to Italian data

The paper investigates the patterns of persistence of innovation and of the properties of firms’ knowledge base (KB) across a sample of Italian firms in the period 1998–2006. The analysis draws upon a theoretical representation of knowledge as a collective good, stemming from the recombination of knowledge bits that are fragmented and dispersed across economic agents. On this basis, we derived properties of the KB like the coherence, the cognitive distance and the variety, and investigated their patterns of persistence over time. The empirical analysis is implemented by exploring the autocorrelation structure of such properties within a quantile regression framework. The results suggest that the properties of knowledge are featured by somewhat peculiar patterns as compared with knowledge stock, and that such evidence is also heterogeneous across firms in different quantiles.     

Phillips curve in US: New insights in time and frequency

The paper explores the co-movement between unemployment and inflation rates in US by using a battery of wavelet tools. The dataset covers the period 1945Q1–2017Q4, having quarterly frequency.The main findings reveal a not stable Phillips curve in US, depending on economic context, seasonality and time-persistence. The Phillips curve is not validated during economic turbulences, while it works over expansion economic periods. Even so, the trade-off between unemployment and inflation is unstable under seasonal growth components and time-persistence, running from short- to medium-term. No link between unemployment and inflation is found in the long-term.     

The impact of income,economic openness and interest rates on housing prices in China: evidence from dynamic panel quantile regression

The rapid growth of housing prices has attracted the attention of the whole of society in China. This article adopts the dynamic panel quantile regression to investigate the impact of income, economic openness and interest rates on housing prices in China, based on the panel data of 35 major cities from 2002 to 2012. Compared with previous studies, we can more precisely and reasonably discuss the impact of these variables on different levels of housing prices. The empirical results indicate that the impact of independent variables on housing prices is heterogeneous across quantiles. Specifically, the impact of income is positive and significant across quantiles, and the impact becomes greater at the 90th and 95th quantiles. Economic openness has a positive and significant effect at the 5th–80th quantiles, which support the Balassa–Samuelson effect, but it is insignificant at the 90th and 95th quantiles. The impact of interest rates is positive and significant at low quantiles, but the impact is negative and insignificant at high quantiles. Furthermore, we also find that the coefficients of interest rates at various quantiles are smaller. In addition, the population has a significant positive effect across quantiles. Finally, we provide important policy implications.     

Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market

This paper studies tail risk connectedness and systemic risk in the Chinese financial market in the post-crisis period of 2009–2017. We adopt the conditional value at risk (CoVaR) and complex theory to construct the tail risk connectedness network and identify the systemically important financial institutions during the Chinese financial turbulence. We precisely characterize the dynamic evolution of the tail risk connectedness at the institutional, sector and market levels. We find that, during normal times, the banking sector contributes the most tail risk to the market and that the real estate sector contributes the least. However, during the crisis period, the real estate sector played its role and became the most significant tail risk emitter. In addition, we identify the significant important financial institutions in the Chinese financial market, highlighting the fact that the four state-owned commercial banks and two largest insurance companies dominate. Our results are helpful to both regulators for developing macroprudential supervision policies and investors interested in the Chinese financial market for making risk management strategies.     

Political stability and growth: An application of dynamic GMM and quantile regression

This paper studies the effect of political stability on economic growth by taking 120 developing countries over the period of 1996–2014. We apply relatively advanced dynamic two step system-GMM and quantile regression. Political stability is found to be a key determinant of economic growth. More importantly, political instability (or risk) is found to be higher in the OIC countries and is a deterrent to economic growth. Also, for the lower and middle income OIC countries, political instability appears to affect economic growth more severely perhaps due to the absence of strong economic and political institutions. Moreover, political instability is also found to be significantly higher in the oil-dependent OIC countries. Notably, political instability is likely to affect growth through the channels of investment and human capital accumulation in the developing countries. Finally, the impact of political stability and political instability on growth is found to be equally distributed across the OIC countries with higher or lower growth level. Therefore, the development of political and economic institutions along with human capital development is recommended for all the developing countries in general and the OIC countries in particular.     

Revisiting growth empirics based on IV panel quantile regression

We analyse the well-known issue of economic growth convergence using quantile regression. Most previous studies have used a least squares (LS) method or variation, which focuses on the issue only at the mean of the growth rate. Therefore, such results cannot provide a satisfactory answer to what can happen if the growth rate is far from the conditional mean level. For example, we consider the following question: do we still have economic growth convergence or is the convergence speed changed in a low growth period such as the ‘Great Recession,’ that started in 2008? We propose using instrumental variable panel quantile regression to answer this question. Our empirical findings demonstrate that economic growth convergence occurs at all quantiles over the entire conditional distribution, but that the convergence speed does depend on quantiles; the convergence speed is much higher when the GDP growth rate is at either high or low quantiles.     

Markovian Social Security in Unequal Societies

In this paper, we develop a dynamic political‐economic theory of social security. We analytically characterize a Markov perfect equilibrium and find that the interaction between Markovian tax policy and tax distortion on private investment in human capital shapes an intertemporal policy rule, linking taxes positively over time. By allowing current taxpayers to influence their own future social security benefits, the positive intertemporal tax linkage provides political support for social security. Moreover, this positive tax linkage leads to a negative correlation between wage inequality and the size of a nation's social security system, consistent with the empirical pattern observed across OECD countries.     

Is aid for trade effective? A panel quantile regression approach

This paper investigates whether Aid for Trade (AfT) leads to greater exports in recipient countries. Using panel quantile regression and two strategies to address endogeneity (AfT lagged by two periods and dynamic OLS), our results suggest that total AfT disbursements promote the export of goods and services mainly for the lower quantiles (0.1, 0.25, 0.50) of the conditional distribution of exports. Hence, countries that export less in volume are those benefitting most from AfT. This effect is mainly driven by the impact on exports of goods rather than on that of services. We also investigate which types of AfT are effective when endogeneity is controlled for. The main results show that whereas aid to improve trade policy and regulation is associated with higher exports for all quantiles, aid used to build infrastructure is found to affect exports at only the lowest tails of the distribution (0.10–0.35) and aid to build productive capacity is generally more effective for the lower quantiles of the export distribution (0.10–0.50). In contrast, aid disbursed for general budget support (an untargeted type of aid) is not associated with greater export levels. This finding holds irrespective of the quantile.     

Public Preferences for Hydrogen Buses: Comparing Interval Data, OLS and Quantile Regression Approaches

We use a quantile regression (QR) approach to analyse contingent valuation estimates of public willingness to pay (WTP) for the air and noise pollution reductions associated with the introduction of hydrogen buses in London. QR results show that variables that were not significant in interval regression or ordinary least squares regression become significant at certain quantiles along the WTP distribution. In addition, the determinants of WTP at the lower tail of the distribution differ from those at the higher end of the distribution. Our findings illustrate the usefulness of quantile regression methods for analysing contingent valuation data, enhancing our understanding of the determinants of willingness to pay.     

The effects of demographics and maternal behavior on the distribution of birth outcomes

This paper utilizes quantile-regression techniques in order to estimate the effects of demographics and maternal behavior during pregnancy at various quantiles of the birthweight distribution. Due to the high costs and long-term effects (both medical and economic) associated with low-birthweight babies, there is a great deal of interest in quantifying these effects, particularly at the lower end of the birthweight distribution. Using large samples of 1992 and 1996 births in the United States, the quantile-regression estimates indicate that several factors (including race, education, and prenatal care) have a significantly higher impact at lower quantiles and lower impact at higher quantiles. These effects at lower quantiles are underestimated by least-squares regression estimates. The inequality in birthweights implied by these results is quite significant, and there is little indication that the inequality has changed much in recent years.     

Attainment of combined optimal lipid values and the impact of lipid medication on cardiovascular outcomes and costs in a commercially insured population in the US

Abstract

Objective: To determine factors associated with the achievement of optimal lipid values (OLVs) and subsequent impact on clinical and economic outcomes.

Methods: An observational managed care database analysis was conducted among treatment-naïve adults with elevated cardiovascular (CV) risk, ≥12 months follow-up and full lipid panel from the 1st January 2002 to the 28th February 2005. Achievement of guideline-based levels for low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides was evaluated via laboratory data. Annual CV-attributable resource utilisation was assessed via medical and pharmacy claims data. Clinical and economic outcomes associated with the achievement of OLVs were assessed using multivariate regression.

Results: A total of 52,778 patients were followed for a mean (standard deviation) of 27 (10) months with 13% achieving combined OLVs at baseline and 23% after 4 years. Of patients, 69% did not initiate lipid-modifying medication. The achievement of combined OLVs reduced the risk of CV event (odds ratio = 0.86; 95% confidence interval 0.78–0.95), resource utilisation (inpatient visits: 3.36 vs. 4.41 per 100 patient years, p<0.0001; emergency department visits: 1.1 vs. 2.4 per 100 patient years, p<0.05) and costs: $703 vs. $903 per patient year, p<0.0001.

Conclusions: Simultaneous achievement of OLVs was rare in this patient population. Physicians should be encouraged to manage multiple risk factors aggressively to improve clinical and economic outcomes associated with CV disease.     

Strength of co-movement between sector CDS indexes and relationship with major economic and financial variables over time and during investment horizons

The main purpose of this article is to analyse the co-movement in both time and frequency between financial sector CDS indexes and between these indexes and their main economic and financial control variables for the period 2004–2014. Empirically, we implement the wavelet-squared coherence methodology to analyse the co-movement through time, frequency and power. Our results unveil that the co-movement between the three financial sectors’ CDSs changes through time and investment horizons, stressing the importance of hedging portfolios in real time. Also, we uncover that the changes in co-movement to relatively higher frequencies coincide with the inception of the recent global financial crisis. This result is collaborated with the co-movement between each CDS index and other global risk factors, including crude oil prices, interest rates and equity market volatility. Finally, we compare the wavelet coherence results with those of the DCC-FIAPARCH model and find that the two different approaches provide quite similar conditional correlations over time. Our results are important for investors, debtors, creditors and other decision-makers which are interested in CDS spread co-movements at different frequencies or investment horizons. It would be useful for all market participants to resort to an appropriate frequency domain to have better understanding of the sector CDS interrelationship behaviour in this domain.     

The Idiosyncratic Risk-Return Relation: A Quantile Regression Approach Based on the Prospect Theory

Given some debate on the empirical idiosyncratic risk-return relation in the literature, we reexamine the relation using a quantile regression approach based on the prospect theory developed by Kahneman and Tversky [1979]. The quantile regression approach allows the coefficient on the independent variable (idiosyncratic risk) to vary across the distribution of the dependent variable (return). Our sample consists of stocks traded on the NYSE, AMEX, and NASDAQ during 1980–2010: 80,324 firm-year observations and 8,123 firms in total. The quantile regression results show that idiosyncratic risk is positively (negatively) related to returns at the high (low) quantiles of returns. The findings are consistent with the prospect theory that investors have a tendency to be less (more) willing to gamble with profits (losses). The results also demonstrate that the least-squares and least-sum optimization methods commonly used in prior research do not capture the relations between idiosyncratic risk and returns at the tail parts of the distribution of returns. Therefore, our empirical results provide new insights into the idiosyncratic risk-return relation in the literature.     

Non-linearity in the determinants of capital structure: evidence from UK firms

We demonstrate that, by exploiting more fully the distribution of leverage, conditional quantile regression methods yield new insights into the choice of leverage ratio. For UK listed companies we find that not only is the estimated effect of the explanatory variables different at different quantiles of the distribution, but also that the effect of a variable changes sign between low leveraged and high leveraged firms.