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We use data on the extent to which residents of one country hold the bonds of issuers resident in another as a measure of financial integration or interrelatedness, asking how Asia compares with Europe and Latin America and with the base case in which the purchaser and issuer of the bonds reside in different regions. Not surprisingly, we find that Europe is more financially integrated than other regions. Asia, more interestingly, already seems to have made more progress on this front than Latin America and other parts of the world. The contrast with Latin America is largely explained by stronger creditor and investor rights, better contract enforcement, and greater transparency, all of which are conducive to foreign participation in local markets and to intraregional cross holdings of Asian bonds generally. Further results based on a limited sample suggest that one factor holding back investment in foreign bonds in East Asia may be limited geographical diversification by mutual funds, in turn reflecting a dearth of appropriate assets. Asian Bond Fund 2, by creating a passively managed portfolio of local currency bonds potentially attractive to mutual fund managers and investors, may help to relax this constraint.

This study characterizes volatility dynamics in external emerging bond markets and examines how prices and volatility respond to news about macroeconomic fundamentals. As in mature bond markets, macroeconomic surprises in external emerging bond markets are found to a¤ect both conditional returns and volatility, with the e¤ects on volatility being more pronounced and longer lasting than those on prices. Yet the process of information absorption tends to be more drawn out than in mature bond markets. International and regional macroeconomic news is at least as important as local news for both asset valuations and volatility dynamics in external emerging bond markets.

PurposeThis paper investigates the impact of a change in economic policy uncertainty (ΔEPUt) and the absolute value of a change in geopolitical risk (|ΔGPRt|) on the returns of stocks, bonds and gold in the Chinese market.Design/methodology/approachThe paper uses Engle's (2009) dynamic conditional correlation (DCC) model and Chiang's (1988) rolling correlation model to generate correlations of asset returns over time and analyzes their responses to (ΔEPUt) and  |ΔGPRt|.FindingsEvidence shows that stock-bond return correlations are negatively correlated to ΔEPUt, whereas stock-gold return correlations are positively related to the |ΔGPRt|, but negatively correlated with ΔEPUt. This study finds evidence that stock returns are adversely related to the risk/uncertainty measured by downside risk,  ΔEPUt and  |ΔGPRt|, whereas the bond return is positively related to a rise in ΔEPUt; the gold return is positively correlated with a heightened |ΔGPRt|.Research limitations/implicationsThe findings are based entirely on the data for China's asset markets; further research may expand this analysis to other emerging markets, depending on the availability of GPR indices.Practical implicationsEvidence suggests that the performance of the Chinese market differs from advanced markets. This study shows that gold is a safe haven and can be viewed as an asset to hedge against policy uncertainty and geopolitical risk in Chinese financial markets.Social implicationsThis study identify the special role for the gold prices in response to the economic policy uncertainty and the geopolitical risk. Evidence shows that stock and bond return correlation is negatively related to the ΔEPU and support the flight-to-quality hypothesis. However, the stock-gold return correlation is positively related to |ΔGPR|, resulting from the income or wealth effect.Originality/valueThe presence of a dynamic correlations between stock-bond and stock-gold relations in response to  ΔEPUt and  |ΔGPRt| has not previously been tested in the literature. Moreover, this study finds evidence that bond-gold correlations are negatively correlated to both ΔEPUt and  |ΔGPRt|.

Studies of bond return predictability find a puzzling disparity between strong statistical evidence of return predictability and the failure to convert return forecasts into economic gains. We show that resolving this puzzle requires accounting for important features of bond return models such as volatility dynamics and unspanned macro factors. A three-factor model comprising a forward spread, a weighted combination of forward rates, and a macro factor generates notable gains in out-of-sample forecast accuracy compared with a model based on the expectations hypothesis. Such gains in predictive accuracy translate into higher risk-adjusted portfolio returns after accounting for estimation error and model uncertainty. Consistent with models featuring unspanned macro factors, our forecasts of future bond excess returns are strongly negatively correlated with survey forecasts of short rates. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2829 This paper was accepted by Gustavo Manso, finance.

This study attempted to examine the volatility spillover between the sovereign bond returns of South Africa and Ghana and the emerging market bond return, USA stock market return and the world long term interest rate using weekly data in the period of 2014–2022. The research used dynamic and constant conditional correlation generalized auto-regressive conditional Heteroskedasticsticity models. The result showed that the volatility of long-term world bond interest rate and USA stock market return affected the Ghana sovereign bond return positively and negatively, respectively. Similarly, the volatility of emerging market bond return and long-term world interest rate affected the South African sovereign bond return positively and negatively, respectively. Thus, policy intervention is needed to contain the negative impact of stock market and long-term world interest rates.

The study of the financial repercussions of low-carbon policy has focused mainly on stocks, leaving bonds out of the picture. The objective of this paper is to assess the impact of low-carbon policy upon European bond returns. This is done by extending the Fama and French two factor model for bonds with an EU-ETS participation factor: GMC (Green minus Carbon). This paper makes four contributions. Firstly, it provides a statistically highly significant measure of the sensitivity of bond portfolio returns to the GMC factor. Secondly, it shows the presence of a green premium in the European bond market in between 2008 and 2018. Thirdly, evidence is found that the addition of an environmental factor improves the performance of the original model. Fourthly, the carbon stress test put forward is able to indicate the effects of a plausible but more severe average EU-ETS carbon price on bond returns.

We demonstrate that characterizing the minimal dimension of the term structure of interest rates is more challenging than currently appreciated. The highly structured polynomial patterns of the factor loadings, which are widely reported and discussed in the literature, reflect local correlations of smooth curves across maturities. We derive analytical expressions for the loadings of cross-sectionally dependent processes that tend to favor a much lower dimension than the true dimension of the underlying factor space. Numerical examples illustrate the significant economic costs of erroneously committing to a parsimoniously parameterized factor space that is informed by standard metrics of goodness-of-fit. Our results apply to other assets with a finite maturity structure.

A properly performing and efficient bond market is widely considered important for the smooth functioning of trading systems in general. An important feature of the bond market for investors is its liquidity. High-frequency trading employs sophisticated algorithms to explore numerous markets, such as fixed-income markets. In this trading, transactions are processed more quickly, and the volume of trades rises significantly, improving liquidity in the bond market. This paper presents a comparison of neural networks, fuzzy logic, and quantum methodologies for predicting bond price movements through a high-frequency strategy in advanced and emerging countries. Our results indicate that, of the selected methods, QGA, DRCNN and DLNN-GA can correctly interpret the expected bond future price direction and rate changes satisfactorily, while QFuzzy tend to perform worse in forecasting the future direction of bond prices. Our work has a large potential impact on the possible directions of the strategy of algorithmic trading for investors and stakeholders in fixed-income markets and all methodologies proposed in this study could be great options policy to explore other financial markets.

A number of studies have examined the change over time in the information content of accounting numbers to stockholders. However, the stockholders’ perspective is not necessarily identical to that of debt holders. The two groups face different risks and rewards, and thus their informational needs are not the same. We examine the change in the information content of accounting numbers over time from the debt holders’ perspective and hypothesize about the economic and reporting factors likely to affect this change. Using the association between accounting numbers and bond valuation and returns, we find that the information content to debt holders has increased over time. In contrast, but consistent with prior studies, we find that the information content to equity holders has declined. The results suggest that the increased information content to debt holders is related to changes in credit risk and to reporting factors such as the increase in reporting conservatism, the shift towards fair value accounting, and the increase in the frequency of losses. The findings contribute to the scant literature on the use of accounting information by debt holders and the extent to which financial reporting meets their unique needs.