The Impact of the Measure Used to Calculate the Distance between Exchange Rate Time Series on the Topological Structure of the Currency Network

Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson's linear correlation coefficient, Spearman's rank correlation coefficient, Kendall's coefficient, partial correlation, dynamic time warping measure, and Kullback-Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia's invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries' geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries.

The impact of containment measures and monetary and fiscal responses on US financial markets during the COVID-19 pandemic

This paper analyses the effects of containment measures and monetary and fiscal responses on US financial markets during the Covid-19 pandemic. More specifically, it applies fractional integration methods to analyse their impact on the daily S&P500, the US Treasury Bond Index (USTB), the S&P Green Bond Index (GREEN) and the Dow Jones (DJ) Islamic World Market Index (ISLAM) over the period 1/01/2020-10/03/2021. The results suggest that all four indices are highly persistent and exhibit orders of integration close to 1. A small degree of mean reversion is observed only for the S&P500 under the assumption of white noise errors and USTB with autocorrelated errors; therefore, market efficiency appears to hold in most cases. The mortality rate, surprisingly, seems to have affected stock and bond prices positively with autocorrelated errors. As for the policy responses, both the containment and fiscal measures had a rather limited impact, whilst there were significant announcement effects which lifted markets, especially in the case of monetary announcements. There is also evidence of a significant, positive response to changes in the effective Federal funds rate, which suggests that the financial industry, mainly benefiting from interest rises, plays a dominant role.

A Matrix-Variate t Model for Networks

Networks represent a useful tool to describe relationships among financial firms and network analysis has been extensively used in recent years to study financial connectedness. An aspect, which is often neglected, is that network observations come with errors from different sources, such as estimation and measurement errors, thus a proper statistical treatment of the data is needed before network analysis can be performed. We show that node centrality measures can be heavily affected by random errors and propose a flexible model based on the matrix-variate t distribution and a Bayesian inference procedure to de-noise the data. We provide an application to a network among European financial institutions.

Hospitalization risk in pediatric patients with bipolar disorder treated with lurasidone vs. other oral atypical antipsychotics: a real-world retrospective claims database study

BACKGROUND

Real-world evidence on atypical antipsychotic (AAP) use in pediatric bipolar disorder is limited.

OBJECTIVE

To assess the risk of all-cause and psychiatric hospitalization among pediatric patients with bipolar disorder when treated with lurasidone versus other atypical antipsychotics (AAPs).

METHODS

This retrospective cohort study used commercial claims data (January 1, 2011 to June 30, 2017) to identify pediatric patients (age ≤17 years) with bipolar disorder treated with oral atypical antipsychotics (N = 16,201). The date of the first claim for an AAP defined the index date, with pre- and post-index periods of 180 days. Each month of the post-index period was categorized as monotherapy treatment with lurasidone, aripiprazole, olanzapine, quetiapine, or risperidone, no/minimal treatment, or other. The risk of all-cause and psychiatric hospitalizations (defined by a psychiatric diagnosis on the facility claim) was analyzed based on treatment in the current month, time-varying covariates (prior treatment-month classification, hospitalization in the prior month, emergency room visit in the prior month), and fixed covariates (age, gender, pervasive development disorder/mental retardation, disruptive behavior/conduct disorder, attention deficit hyperactivity disorder, depression, anxiety, adjustment disorder, obesity, diabetes, antidepressants, anxiolytics, other co-medication) using a marginal structural model.

RESULTS

Treatment with aripiprazole (OR = 1.60, 95% CI: 1.08-2.36) and olanzapine (OR = 1.68, CI: 1.03-2.71) was associated with significantly higher odds of all-cause hospitalizations compared to lurasidone, but treatment with quetiapine (OR = 1.03, CI: 0.69-1.54) or risperidone (OR = 1.02, CI: 0.68-1.53) was not. Similarly, treatment with aripiprazole (OR = 1.61, 95% CI: 1.08-2.38) and olanzapine (OR = 1.73, CI: 1.06-2.80) was associated with significantly higher odds of psychiatric hospitalizations compared to lurasidone, but treatment with quetiapine (OR = 1.02, CI: 0.68-1.54) or risperidone (OR = 1.01, CI: 0.67-1.51) was not.

CONCLUSION

In usual clinical care, pediatric patients with bipolar disorder treated with lurasidone had a significantly lower risk of all-cause and psychiatric hospitalizations when compared to aripiprazole and olanzapine, but not quetiapine or risperidone.

Cost-effectiveness of nivolumab monotherapy in the third-line treatment of small cell lung cancer

AIMS

Present cost-effectiveness analysis of nivolumab monotherapy vs. commonly prescribed third-line (3 L+) treatment in small cell lung cancer (SCLC).

MATERIALS AND METHODS

A three health states partitioned survival model (progression-free, progressed disease, and death; US payer perspective) was developed. The systematic literature review identified no randomized controlled or single-arm trials with separate outcomes for 3 L + SCLC patients. Topotecan was chosen as a comparator because it is frequently prescribed in real-world practice for 3 L SCLC. Clinical inputs for topotecan were derived from the Flatiron database with inclusion/exclusion criteria matched to patients treated with 3 L + nivolumab in CheckMate 032. Intravenous (IV) and oral topotecan clinical efficacy were assumed equivalent. Base-case analysis used a 20-year lifetime horizon. An annual discount rate of 3.0% for costs and outcomes was applied. Uncertainty was assessed using sensitivity analyses adjusted for key parameters.

RESULTS

Incremental cost per quality-adjusted life-year (QALY) gained with nivolumab was US$153,312 vs. IV topotecan and US$123,003 vs. oral topotecan, respectively. When results were disaggregated, nivolumab-related costs were mainly driven by drug acquisition costs, and topotecan-related costs were primarily due to adverse event treatment. Mean overall survival (OS) was 21.69 months with nivolumab and 5.80 months with IV or oral topotecan. More favorable outcomes were found by the landmark response analyses. Deterministic sensitivity analyses showed that changes to the discount rate for costs and outcomes and body weight had the greatest impacts on results.

LIMITATIONS

Included use of real-world data for OS outcomes associated with 3 L topotecan, use of second-line topotecan data for progression-free survival, and no indirect costs.

CONCLUSIONS

Based on the literature on willingness-to-pay for a QALY in metastatic cancer, nivolumab monotherapy might represent a cost-effective option for 3 L + treatment of SCLC compared with IV and oral topotecan. Sensitivity analysis using response-based methods yielded further favorable cost-effectiveness estimates.

Households Health Expenditure in Interannual Correlation With Public Health Expenditure in Greece

The aim of this article is to investigate the relationship between the public and the private health expenditure (macroeconomic and microeconomic approach) over time and within the recession and austerity period in Greece, in order to find out whether the strict Memorandum health policies pass, influence, or go along with the health expenditure to the final consumer, i.e., the health services user. In this context, by using econometric tools, such as multiple regression and cointegration analysis on the raw microdata of Household Budget Surveys from 1987/88 up to 2018, as well as by using data of public expenditure of Organization for Economic Co-operation and Development-Health Statistics 2019 in the Stata version 13, the study compares the variation of household and public health expenditures before and after the financial crisis in Greece and also examines the correlation between the two variables. The analysis demonstrated that the Greek household health expenditure (HHE) was rapidly increasing during the period 1988-2008, and afterward, it started decreasing. Moreover, the total private and the total public health expenditures seem to have a bidirectional long-run relationship and significant cointegration. The same was observed regarding the public expenditure and household medical services expenditure, as well as pharmaceuticals. Furthermore, the results indicate that over the years of recession, the monthly HHE decreases, due to the confiscation of middle-class income, which led to consumerism restrictions. However, as households are now spending a bigger portion of their shrunken income for health (as health is an inelastic commodity), HHE, as a proportion of total private expenditure, has eventually risen.

Testing the role of oil production in the environmental Kuznets curve of oil producing countries: New insights from Method of Moments Quantile Regression

The global economic growth has triggered the continual increase in oil demand for transportation and petrochemical sectors which offshoots environmental pollution. Though there exists literature on environmental Kuznets curve, however, very few examine the scope in the light of fossil fuel energy production. This paper investigated the dynamic effect of oil production on carbon emissions in 15 oil-producing countries by accounting for the role of electricity production, economic growth, democracy, and trade over the period 1980-2010. Using the novel Method of Moments Quantile Regression (MMQR) with fixed effects, the results found an inverted U-shape relationship between economic growth and CO₂ emissions only at median and higher emission countries, thus, validating the environmental Kuznets curve hypothesis. Oil production increases CO₂ emissions significantly from the first to sixth quantiles with greater effect at the lowest quantile and weaker effect at the highest quantile. Electricity production was found to increase CO₂ emissions while trade condenses CO₂ emissions across all the quantiles thereby confirming the pollution halo hypothesis for oil-producing countries. The effect of democracy was positive across all the quantiles but only significant in countries with average CO₂ emissions. The findings provide insight for policymakers to mitigate CO₂ emissions in oil-producing countries through diversification and clean energy technologies such as carbon capture and storage.

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively.

Lending to Parents and Insuring Children: Is There a Role for Microcredit in Complementing Health Insurance in Rural China?

This paper assesses the causal impact on child health of borrowing formal microcredit for Chinese rural households by exploiting a panel dataset (2000 and 2004) in a poor northwest province. Endogenous borrowing is controlled for in a dynamic regression-discontinuity design creating a quasi-experimental environment for causal inferences. There is causal relationship running from formal microcredit to improved child health in the short term, while past borrowing behaviour has no protracted impact on subsequent child health outcomes. Moreover, formal microcredit appears to be a complement to health insurance in improving child health through two mechanisms-it enhances affordability for out-of-pocket health care expenditure and helps buffer consumption against adverse health shocks and financial risk incurred by current health insurance arrangements. Government efforts in expanding health insurance for rural households would be more likely to achieve its optimal goals of improving child health outcomes if combined with sufficient access to formal microcredit.

The impact of government failure on tourism in the Philippines

While the Philippines aspires to be one of the top tourist destinations in Southeast Asia, self-inflicted wounds like the failure of the government to comply with international aviation safety standards may derail the country from achieving its goals. This article estimates the short- and long-term impact of the US FAA downgrade of the Philippine civil aviation system in 2008 and the EU ban of Philippine carriers in 2010 on tourist expenditures, arrivals, and length of stay using monthly time series data. The econometric model, consisting of three equations due to the endogeneity of the tourist arrivals and length of stay variables in the tourist expenditures equation, is estimated simultaneously using the generalized method of moments. The results indicate that the US FAA downgrade and the EU ban impact monthly tourist receipts negatively in the short term while the downgrade also impacts tourist expenditures in the long term. Moreover, the ban impacts length of stay negatively in the short and long term while the downgrade impacts length of stay negatively only in the long term. The substantial decline in tourism receipts from 2008 to 2010 despite an increasing trend in tourist arrivals is due to the shorter stay of tourists, indicating that high-spending tourists have not returned following the downgrade and ban.