Explore the vast range of titles available.

PurposeWithout precedent, green bonds confront, for the first time since their emergence, a twofold crisis context, namely the Covid-19-Russian–Ukrainian crisis period. In this context, this paper aims to investigate the connectedness between the two pioneering bond market classes that are conventional and treasury, with the green bonds market.Design/methodology/approachIn their forecasting target, authors use a Support Vector Regression model on daily S&P 500 Green, Conventional and Treasury Bond Indexes for a year from 2012 to 2022.FindingsAuthors argue that conventional bonds could better explain and predict green bonds than treasury bonds for the three studied sub-periods (pre-crisis period, Covid-19 crisis and Covid-19-Russian–Ukrainian crisis period). Furthermore, conventional and treasury bonds lose their forecasting power in crisis framework due to enhancements in market connectedness relationships. This effect makes spillovers in bond markets more sensitive to crisis and less predictable. Furthermore, this research paper indicates that even if the indicators of the COVID-19 crisis have stagnated and the markets have adapted to this rather harsh economic framework, the forecast errors persist higher than in the pre-crisis phase due to the Russian–Ukrainian crisis effect not yet addressed by the literature.Originality/valueThis study has several implications for the field of green bond forecasting. It not only illuminates the market participants to the best market forecasters, but it also contributes to the literature by proposing an unadvanced investigation of green bonds forecasting in Crisis periods that could help market participants and market policymakers to anticipate market evolutions and adapt their strategies to period specificities.

Traditional theories of term structure of interest rate consist of four major classical theories, including Pure Expectation Theory, Liquidity Preference Theory, Preferred Habitat Theory and Market Segmentation Theory. However, they cannot be well interpreted by the traditional static term structure of interest rate methods such as polynomial spline and exponential spline. To address problems on low precision and weak stability of traditional methods in constructing static interest rate term structure curve, in this paper, we introduce the tension interpolation spline based on a fourth-order differential equation with local tension parameters calculated by Generalized Reduced Gradient (GRG) algorithm. Our primary focus is to illustrate its better prediction effect and stability with an empirical study conducted using datum of treasury bonds. Then, we divided the datum into intra-sample datum for estimating tension parameters and out-of-sample datum for evaluating their robustness of predicting stochastics collected from Shanghai Stock Exchange on$ {2^{{\\rm{nd}}} $February, 2019. According to the principle of total least squares and total least absolute deviations, the result shows that the tension interpolation spline model has better precision and stronger stability in prediction of out-of-sample treasury bonds prices compared with the model established by polynomial spline and exponential spline. In addition, it can better explain the Liquidity Preference Theory, which confirms that it is suitable for constructing the static term structure of interest rates in the securities exchange market.

PurposeHaving defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.Design/methodology/approachConceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.FindingsThe predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.Originality/valueBetter understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

We show that the price of a Treasury bond and an inflation-swapped Treasury Inflation-Protected Securities (TIPS) issue exactly replicating the cash flows of the Treasury bond can differ by more than $20 per $100 notional. Treasury bonds are almost always overvalued relative to TIPS. Total TIPS-Treasury mispricing has exceeded $56 billion, representing nearly 8% of the total amount of TIPS outstanding. We find direct evidence that the mispricing narrows as additional capital flows into the markets. This provides strong support for the slow-moving-capital explanation of arbitrage persistence.

The West African Economic and Monetary Union (WAEMU) regional securities market saw increasing activity in the last decade, but still fell short of supplying sufficient long-term financing for growth-enhancing public and private investment projects. In addition to providing an institutional background, this paper studies recent developments and the determinants of interest rates on the market-using yield curve and principal component analyses. It also identifies challenges and prospective reforms that could help the region reap the full benefits of a more dynamic securities market and assesses the potential systemic risk the market may pose for the region's banking system.

Investors value the liquidity and safety of US Treasuries. We document this by showing that changes in Treasury supply have large effects on a variety of yield spreads. As a result, Treasury yields are reduced by 73 basis points, on average, from 1926 to 2008. Both the liquidity and safety attributes of Treasuries are driving this phenomenon. We document this by analyzing the spread between assets with different liquidity (but similar safety) and those with different safety (but similar liquidity). The low yield on Treasuries due to their extreme safety and liquidity suggests that Treasuries in important respects are similar to money.

This study reconsiders the Fisher effect for the UK from a different methodological perspective. To this aim, the nonlinear ARDL model recently developed by Shin et al. (2014), is applied over the periods of 1995M1-2008M9 and 2008M10-2018M1. This model decomposes the changes in original inflation series as two new series: increases and decreases in inflation rates. Hence, it enables us to examine the Fisher effect in terms of increases and decreases in inflation separately. The empirical findings support asymmetrically partial Fisher effects for the UK in the long-run only for the first period. Additionally, this study attempts to describe and introduce a different version of the partial effect concept for the first time for the UK.

We evaluate the effect of the Federal Reserve's purchase of long-term Treasuries and other long-term bonds (QE1 in 2008-09 and QE2 in 2010-11) on interest rates. Using an event-study methodology, we reach two main conclusions. First, it is inappropriate to focus only on Treasury rates as a policy target, because quantitative easing works through several channels that affect particular assets differently. We find evidence for a signaling channel, a unique demand for long-term safe assets, and an inflation channel for both QEl and QE2, and a mortgage-backed securities (MBS) prepayment channel and a corporate bond default risk channel for QE1 only. Second, effects on particular assets depend critically on which assets are purchased. The event study suggests that MBS purchases in QE1 were crucial for lowering MBS yields as well as corporate credit risk and thus corporate yields for QE1, and Treasuriesonly purchases in QE2 had a disproportionate effect on Treasuries and agency bonds relative to MBSs and corporate bonds, with yields on the latter falling primarily through the market's anticipation of lower future federal funds rates.

We propose a market-wide liquidity measure by exploiting the connection between the amount of arbitrage capital in the market and observed \"noise\" in U.S. Treasury bonds—the shortage of arbitrage capital allows yields to deviate more freely from the curve, resulting in more noise in prices. Our noise measure captures episodes of liquidity crises of different origins across the financial market, providing information beyond existing liquidity proxies. Moreover, as a priced risk factor, it helps to explain cross-sectional returns on hedge funds and currency carry trades, both known to be sensitive to the general liquidity conditions of the market.