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Might the impact of the global economic policy uncertainty (GEPU) and the long-term bond yields on oil prices be asymmetric? This paper aims to consider the effects of the GEPU and the US long-term government bond yields on oil prices using quantile-based analysis and nonlinear vector autoregression (VAR) model. The author hypothesized whether the negative and positive changes in the GEPU and the long-term bond yields of the USA have different effects on oil prices.

To address this question, the author uses quantile cointegration model and the impulse response functions (IRFs) of the censored variable approach of Kilian and Vigfusson (2011).

The quantile cointegration test showed the existence of non-linear cointegration relationship, whereas Granger-causality analysis revealed that positive/negative variations in GEPU will have opposite effects on oil prices. This result was supported by the quantile regression model’s coefficients and nonlinear VAR model’s IRFs; more specifically, it was stressed that increasing/decreasing GEPU will deaccelerate/accelerate global economic activity and thus lead to a fall/rise in oil prices. On the other hand, the empirical models indicated that the impact of US 10-year government bond yields on oil prices is asymmetrical, while it was found that deterioration in the borrowing conditions in the USA may have an impact on oil prices by slowing down the global economic activity.

As a robustness check of the quantile-based analysis results, the slope-based Mork test is used.

The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic.

The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021.

Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run.

To the best of the authors’ knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy.

The purpose of this paper is to propose a Clarke-Groves Tax (CGT) type as a remedy to the criticism that the implementation of Eurobonds has raised regarding the risk of undermining fiscal discipline. In this model, a government minimizes its sovereign debt-to-GDP ratio in a given period and decides whether to join a common sovereign debt club. In doing so, it exposes itself to a positive or negative tax burden while benefiting from the liquidity premium involved in creating a secure asset. The authors found that the introduction of this tax may prevent free riding behaviours if Eurobonds were to be implemented. To illustrate this, the authors provide some numerical simulations for the Eurozone.

In the model presented, a government which optimizes a social utility function decides whether to join the common debt club.

The adoption of the proposed tax could prevent free-riding behaviours and, therefore, encourages participation by those countries with lower debt levels that would have not otherwise taken part in this common debt mechanism. Under certain circumstances, we can expect the utility of all members of this club to improve. The bias in the distribution of gains might be mitigated by regulating the tax rule determining the magnitude of payment/reward. The proportion of the liquidity premium, arising from the implementation of a sovereign safe asset, has a decisive impact on the degree of the governments’ utility enhancement.

The adoption of a CGT would require Eurobonds club members to reach an agreement on “the” theoretical model for determining the sovereign debt yield. One of the limitations of this model is considering the debt-to-GDP ratio as the sole determinant of public debt yields. Moreover, the authors assumed the relationship between the debt-to-GDP ratio and funding costs to be identical for all countries. Any progress in the implementation of the proposed transfer scheme would require a more realistic and in-depth analysis.

A new fiscal rule based on compensating countries with lower public debt levels could be a way to mitigate free-riding problems if a Eurobond mechanism is to be established.

This fiscal rule has not been proposed or analysed before in a context such as that considered by this paper.

Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

This paper examines the interaction between monetary policy and the macroeconomy using a macro-finance term structure model of Joslin, Priebsch, and Singleton (2012), in which macroeconomic risks are not assumed to be spanned by information about the shape of the yield curve. For model estimation, we apply the Kalman filter to a large number of macroeconomic time series data grouped into output, inflation, and market stress categories and extract three common factors. For the factors determining the shape of the yield curve, we use the call rate, the spread between 10-year government bond yield and the call rate, and a combination of the call rate, 2- and 10-year government bond yields as proxies for the level, slope, and curvature factors. We interpret the call rate as a proxy for both the short rate and the instrument of monetary policy. Empirical results show that the macroeconomic factors have a significant impact on the risk premium associated with monetary policy shocks. Furthermore, we find that monetary policy shocks increase the term premium, which in turn affects the factors determining the yield curve, and such effects on the shape of the yield curve feeds back into the macroeconomic factors. Taken together, empirical findings in this paper can be interpreted as evidence supporting the term premium channel (Ferman, 2011) of monetary policy transmission mechanism.

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea business-cycle expansion and contraction regimes characterized by the OECD reference turning points. We use logistic regression and support vector machine in performing the OECD regime classification and predicting three-month-ahead regime. We find that the OnBid auction rate conveys important information for detecting the coincident and future regimes because this information might be closely related to deleveraging regarding default on debt obligations. This finding suggests that corporate managers and investors could use the auction information to gauge the regime position in their decision-making. This research has an academic significance that reveals the relationship between the auction market and the business-cycle regimes.

The study aims to analyze and compare the influence of country-specific fundamentals and global conditions on sovereign risk of Sri Lanka within the sample period of 2006–2019 while employing Treasury bond rates as proxy for sovereign risk.

The determinant powers of the variables are assessed using the auto regressive distributed lag (ARDL) model to verify both short- and long-run effects on sovereign spreads.

The study finds that Sri Lanka's sovereign spreads are shaped by both country fundamentals and global factors, though local determinants tend to have greater influence when the directions of coefficients are ignored. While the impact of most variables was in line with the researchers' expectations, fiscal deficit was found to have an unconventional negative coefficient which may be explained by investors' optimistic take on Government's involvement in post-war economic development drive during the sample period, enabling Sri Lanka to attract low-cost funding.

The study excludes of impact of the ongoing coronavirus disease-2019 ( COVID-19) health crisis which may unduly distort the data. Further, the research does not capture the impact of change in sentiment owing to market information, debt dynamics and policy changes in Sri Lanka.

The study reveals that a sound monetary policy directed at preserving both the internal and external value of currency as well as a disciplined fiscal policy are imperative to manage Sri Lanka's sovereign risk, particularly in the face of global uncertainties.

The study adds to the literature by investigating the timely importance of a country's internal fundamentals against the global events. Furthermore, the research would complement the scarcity of research regarding that subject focused on the Sri Lankan economy, capturing the rapid variations in the fundamentals that the country has undergone since the end of the civil war while recognizing the growing influence of globalization over the recent years.

This study’s goal was to identify how several markets have developed over time and what determinants have influenced this process, based on adaptive markets hypothesis (AMH). In this regard, the authors consider that agents are driven by the seeking for abnormal returns to stay “alive” and their environment could somehow modify their decision-making processes, as well as influence the degree of efficiency of the market.

The authors collected the daily closing-of-the-market index from 50 countries, between 1990 and 2022. The sample includes emerging countries, developed countries and frontier markets. Then, the authors ran multilevel modeling using Hurst exponent as an informational efficiency metric estimated by two different moving windows: 500 and 1,250 observations (approximately 2 and 5 years).

The results indicate that the efficiency of the markets is not constant over time. The authors also have identified that markets follow a cyclical pattern of efficiency/inefficiency, and they are currently in a period of convergence to efficiency, possibly explained by the increase in computational capacity and speed of the available information to agents. In addition, this study identified that country characteristics are associated with market efficiency, considering institutional factors.

Studies of this nature contribute to the literature, considering the importance of better comprehension of market efficiency dynamics and their determinants, specially observing other theories on the relationship between information and markets (like AMH), which work with other investor assumptions than those used by efficient market hypothesis.

This study investigates the performance distribution of passive funds in the Korean market and compares it with the performance distribution of active funds. The key findings are as follows, first, the performance distribution of passive funds has a thicker tail compared to that of active funds. There are passive funds that achieve outstanding performance, and both the false discovery rate (FDR) analysis and simulation analysis suggest that their outperformance is driven by managerial skill rather than luck. Second, passive fund performance is more persistent compared to active fund performance. Third, investors are less responsive to passive fund performance compared to active fund performance. The fund flow-performance relationship is significantly positive for active funds but not for passive funds. This implies that investors may not recognize the managerial skills of passive funds.