Short-term Interest Research Articles

A Quantity-Driven Theory of Term Premia and Exchange Rates

AbstractWe develop a model in which specialized bond investors must absorb shocks to the supply and demand for long-term bonds in two currencies. Since long-term bonds and foreign exchange are both exposed to unexpected movements in short-term interest rates, a shift in the supply of long-term bonds in one currency influences the foreign exchange rate between the two currencies, as well as bond term premia in both currencies. Our model matches several important empirical patterns, including the comovement between exchange rates and term premia, and the finding that central banks’ quantitative easing policies affect exchange rates. An extension of our model links spot exchange rates to the persistent deviations from covered interest rate parity that have emerged since 2008.

Fuzzy Random Option Pricing in Continuous Time: A Systematic Review and an Extension of Vasicek’s Equilibrium Model of the Term Structure

Fuzzy random option pricing in continuous time (FROPCT) has emerged as an active research field over the past two decades; thus, there is a need for a comprehensive review that provides a broad perspective on the literature and identifies research gaps. In this regard, we conducted a structure review of the literature by using the WoS and SCOPUS databases while following the PRISMA criteria. With this review, we outline the primary research streams, publication outlets, and notable authors in this domain. Furthermore, the literature review revealed a lack of advancements for the equilibrium models of the yield curve. This finding serves as a primary motivation for the second contribution of this paper, which involves an extension of Vasicek’s yield curve equilibrium model. Specifically, we introduce the existence of fuzzy uncertainty in the parameters governing interest rate movements, including the speed of reversion, equilibrium short-term interest rate, and volatility. By incorporating fuzzy uncertainty, we enhance the model’s ability to capture the complexities of real-world interest rate dynamics. Moreover, this paper presents an empirical application of the proposed extension to the term structure of fixed-income public bonds in European Union. The empirical analysis suggests the suitability of the proposed extension of Vasicek’s model for practical applications.

DCARS: Deep context-aware recommendation system based on session latent context

Recommendation systems (RSs) usually create suggestions based on users’ prior intentions. Users’ interests may evolve due to context change or user-mode change. Discovering such a change is crucial for producing personalized suggestions. Traditional approaches assume that each user has a fixed preference. On the contrary, context-aware recommendation systems (CARSs) use contextual information to detect user intention changes. However, applying contextual information is the main challenge in CARSs, because it is not always feasible to achieve all the users’ contextual information. Furthermore, adding different contexts to RSs grows its dimensionality in multiple applications. Besides, existing CARSs cannot precisely obtain the hierarchical relationships between items and contexts items that influence users’ intentions. They often use short-term interest with either static long-term preference in the recommendation process. To alleviate the mentioned challenges, we propose a novel deep context-aware recommendation system (DCARS) to capture and incorporate user preferences changes in the recommendation process. The proposed method models the latent context among selected items in each session throughout users’ historical interactions and combines users’ short-term and long-term preferences to generate recommendations. Specifically, we suggest a DCARS based on latent representations of sessions derived from users’ activities. The experiment results on benchmark context-aware data sets show that the proposed DCARS model surpasses state-of-the-art approaches.

Is the fed failing to re-anchor expectations? An analysis of jumps in inflation swaps

We investigate US inflation expectations de-anchoring by analyzing tail behavior of inflation swaps. We extract probabilities of jumps in inflation expectations along with their potential jump severity and look through an event study approach and a regression analysis on how Fed policy impacted these. Our findings suggest that recent increases in interest rates and FOMC announcements of hikes did not manage to re-anchor short term inflation expectations as both probabilities and intensity of potential inflation boom/crash anticipations remain broadly independent of announcements and short-term interest rates dynamics.

The interest rate transmission mechanism and transmission efficiency of monetary policy in China: empirical analysis based on TVP-SV-VAR model

Our theoretical model sets money market short-term interest rate as policy rate and reveals the mechanism of policy rate transmission to bond market interest rate and credit market interest rate. We apply the TVP-SV-VAR model to examine changes in transmission efficiency during these processes. We find that after central bank deregulated interest rates, the transmission efficiency of monetary policy interest rates increased significantly. The transmission of short-term interest rates to risk-free rates is the most efficient, followed by the transmission to long-term interest rates, but the transmission efficiency to loan rates is not ideal. In long sample period, the transmission of financial market benchmark interest rate SHIBOR007 to other interest rates is more desirable than IBO007 and R007. In short sample period, transmission of DR007 changes to interest rate changes in other markets is similar to SHIBOR007, indicating that using DR007 as central bank’s policy target is highly feasible.

Equity Convexity under Major Monetary Policy Shift

In this article, the authors intend to gain an understanding of the drivers of stock convexity, also known as gamma. First, using a bottom-up—firm-level—approach, they show that stock fundamentals, particularly metrics related to value (captured by the price-to-book ratio) and historical volatility, allow us to efficiently discriminate between convex and concave stocks. Building on this result, they investigate the ties between the gamma premium and traditional risk factors. Second, they adopt a top-down—macroeconomic-driven—framework to understand which economic environment is the most favorable to convexity: They highlight the importance of the short-term interest rate, the VIX, but also oil price dynamics in a univariate cointegrating vector. These variables share long-term relationships. The authors then evaluate the ability of different models to forecast future convexity premium dynamics. Finally, they seek to employ these signals in the design of a systematic long convexity strategy and show that it leads to significantly improved risk-adjusted returns compared with a capitalization-weighted benchmark, especially in turbulent markets. Convexity exposure appears particularly relevant in a context of monetary policy normalization.

LSIAN: Exploiting interval interests for session-based recommendation via sparse attention network

Session-based recommendation (SBR) recommends the next clicked item to anonymous users. Recent studies utilize graph attention networks for modeling. There exist various problems in these algorithms: First, most of these methods only consider the long-term interests of users, ignoring the short-term interest transfer of users; second, when the graph attention mechanism obtains item weights, irrelevant items are assigned weights, which reduces the weights of related items, resulting in insufficient weight discretization. Third, the last action is taken as the user's final preference, however, this assumption does not necessarily correspond to the user's actual interest. This paper proposes a long- and short-term interest-attention aware network (LSIAN) model. First, we propose a novel time-aware attention mechanism that learns users' short-term interests by considering context and time interval, and utilizes average pooling to represent users' long-term interests. Next, we introduce a context-based adaptive sparse attention mechanism to discover irrelevant items, down-weight irrelevant items through a dynamic loop, and reserve higher weights for related items. Furthermore, we introduce a gated fusion method adaptively integrate the user's long-term and short-term preferences, thereby alleviating the problem of excessive weighting of the last term. Experiments on two public datasets show that LSIAN outperforms state-of-the-art algorithms.

Multi-scale modeling temporal hierarchical attention for sequential recommendation

Multi-scale modeling of items interacting in a sequence of users' historical behaviors in a sequential recommendation task is crucial. In real scenarios, the user's choice of items depends not only on static preferences (long-term interests) but also on recent dynamic preferences (short-term interests). In recent years, micro-video sharing platforms have been very favorable, and correspondingly, more efficient recommendation method will be needed to support users in finding their interested micro-videos (items). Compared to traditional online videos (such as YouTube), micro-videos are created by grassroots users, shot by smartphones, are short (typically tens of seconds), and have fewer tags or descriptive text, which makes recommending it a challenging task. In this work, we explore how to model users' historical behavior sequences at multiple scales to predict their click-through rates on micro-videos and then determine whether to recommend them to users. We present a novel Multi-scale Modeling Temporal Hierarchical Attention (MMTHA) method for modeling users' behavior sequences inspired by recent deep network-based approaches. Specifically, firstly, we capture users' short-term dynamic interests using temporal windows; secondly, we utilize a category-level attention mechanism to describe the coarse-grained interests of users and an item-level attention mechanism to capture the fine-grained interests of users; thirdly, we employ a forward multi-headed self-attention mechanism to identify and integrate long-term correlations between previously segmented temporal windows. We conducted extensive experiments on two publicly available datasets to verify their effectiveness. The experimental results show that our proposed MMTHA model achieves state-of-the-art performance in all tests.

Monetary policy, macroeconomic uncertainty and corporate liquid asset demand: a firm-level analysis for India

PurposeThe study examines how the liquid assets holdings among non-financial Indian firms vary due to tightening monetary policy and increasing macroeconomic uncertainty.Design/methodology/approachThe authors analyze 5,640 firms for the period 2011–2021. The authors first estimate India’s monetary policy shocks by decomposing the exogenous shocks from the systematic component of monetary policy changes. The authors then examine the effects of the estimated monetary policy shocks and a range of macroeconomic and policy uncertainty indicators on companies’ cash and bank balances to asset ratios using two-step system generalized method of moments (GMM) estimators.FindingsThe authors find that monetary policy shocks cause the cross-sectional variances for the firms’ liquidity holdings to increase. In anticipation of macroeconomic volatility, companies respond to these shocks after taking into account all the firm-level information to minimize the opportunity costs of holding extra cash or too few cash balances that can hamper firms’ operations. Furthermore, compared to other shocks, the contribution of inflation-induced shocks is predicted to be the largest in the cross-sectional deviation of the firm’s cash holdings. The authors also find that low-growth, older and financially constrained firms observe lesser heterogeneity in their cash holdings as they tend to hold cash as a precautionary buffer.Originality/valueThe authors’ approach to the analysis is unique in many ways. To address potential transmission bias, the authors use nowcasts and forecasts of real gross domestic product (GDP) growth and inflation to generate a series of exogenous monetary policy shocks for identifying unanticipated changes in short-term interest rates. Subsequently, the authors estimate how these shocks affect the cross-sectional deviation of liquid assets. For estimating the effects of macroeconomic uncertainty on corporate cash demand, the authors utilize a range of proxies for uncertainty. Unlike previous attempts, the authors offer evidence for a developing and fast-emerging economy.

How to foresee crises? A new synthetic index of vulnerabilities for emerging economies

We present a novel vulnerability index to monitor crises in emerging economies. To design the index, we identify the empirical regularities that precede sovereign, currency, and banking crises. Because we want to give policy makers the ability to react at an early stage, we focus on six-quarters before the onset of a crisis. We use data for 25 emerging economies and a new quarterly dataset of crisis events. The short-term interest rate is the unique variable that predicts all three types of crisis since it captures bank difficulties and sovereign and currency strains as monetary authorities use it to defend exchange rate pegs and to avoid capital flows. As the predictors of the three types of crisis generally differ, we define a different index for each type of crisis. The index, which is easy to update, outperforms the usual individual leading predictors of crises.

Revisiting Financial Volatility in the Indonesian Islamic Stock Market: GARCH – MIDAS Approach

Introduction/main objectives: The aim of this research is to study the impact of macroeconomic variables on the Indonesian Islamic stock market’s volatility. Background issues: To predict the stock market’s volatility, daily or high-frequency data has been applied to the model’s explanatory variables with the same data frequency. However, when it comes to the macroeconomic variables as volatility drivers, the data is low-frequency, such as weekly, monthly, or quarterly. The current study uses a model which treats the data equally. Novelty: This study employs the mixed data sampling (MIDAS) model, which allows data from multiple frequencies to be included in the same model. This model can combine daily stock returns’ data with monthly or quarterly macro­economic data. Hence, this is the first paper to study the determinants of the volatility of Indonesia's Islamic stock index using GARCH-MIDAS. Research Methods: The Generalized Autoregressive Conditional Heteroscedasticity GARCH-MIDAS model captures the short-run from the long-term element of volatility; the findings show the asymmetry effect for the short-term element’s result. Finding/Results: Inflation does not influence long-term market volatility. Moreover, after the 2008 crisis, the study shows that inflation and short-term interest rates positively influenced market volatility. Conclusion: The positive effect of inflation suggests that stocks can function as inflation hedges for stock investors in the long run. Further, the positive impact of interest rates implies that Muslim investors use the conventional short-term interest rates as a benchmark for investment in Shari’ah-compliant instruments.

MONETARY POLICY AND ASSET PRICES: THE CASE OF PAKISTAN

This paper sets the objective of estimating the mediating role of asset prices in the transmission of monetary policy. Vector autoregressive model comprising short-term interest rate, output, inflation and asset prices has been estimated using data of Pakistan economy for the period 2000m01 to 2019m06. We find significant role of asset prices in transmission of monetary policy to target variables, especially when individual asset prices are taken instead of asset price index. Stock prices are found important mediator of transmission from interest rate to output while exchange rate is so for inflation rate; role of house prices remains moderate.

Empirical Study on Chinas House Prices

This study aims to empirically study house prices and their relationship with China's critical economic and financial metrics. The key metrics include interest rate, inflation rate, and stock index prices. This study shows that stock index prices do not significantly impact house prices in China. At the same time, long-term interest and inflation rates positively impact house prices in China.Additionally, short-term interest rate possesses a negative im-pact on house prices. This can shed light on policymakers in China when making economic decisions to influence house prices in China. On top of this, this study also compared house prices and real estate stock index prices and concluded that the two differ significantly.

Towards a macroprudential regulatory framework for mutual funds?

AbstractThis paper highlights the procyclical and unstable behaviour of mutual funds, characterized by a varying sensitivity on common asset pricing factors. It proposes a novel factor model that allows for regime changes associated with macroeconomic and financial state variables. Estimated on a panel covering 825 US equity mutual funds over a period of 30 years, it appears that the yield curve, the dividend yield, short term interest rates and the industrial production coincide with regimes switches in the Fama–French factors. Furthermore, the estimated regimes coincide with financial crises and economic downturns, thus confirming the procyclical behaviour of mutual funds' returns. These findings, coupled with the emerging systemic role of mutual funds, promote the consideration for a specific macroprudential regulatory framework targeted at the mutual fund industry.

Estimating the US trend short-term interest rate

We estimate the trend short-term interest rate in the United States using an unobserved-components stochastic-volatility model with interest-rate and survey data from 1998Q2 to 2022Q4. Our results indicate that the trend short-term interest rate has drifted down during most of the sample and remains low in a historical perspective, despite the recent sharp increase in the short-term interest rate.

Personal or General? A Hybrid Strategy with Multi-factors for News Recommendation

News recommender systems have become an effective manner to help users make decisions by suggesting the potential news that users may click and read, which has shown the proliferation nowadays. Many representative algorithms made great efforts to discover users’ preferences from the histories for triggering news recommendations. However, there exist some limitations due to the following two main issues. First, they mainly rely on the sufficient user data, which cannot well capture users’ temporal interests with very limited records. Second, always perceiving users’ histories for recommendation may ignore some important news (e.g., breaking news). In this article, we propose a novel Multi-factors Fusion model for news recommendation by integrating both user-dependent preference effect and user-independent timeliness effect together. First, to track the preference of a certain user, we decompose her reading history into two user-related factors, including the long-term habit and the short-term interest. Specifically, we extract her persistent habit by exploring the category effect of news that she focuses on from her whole records. Then, we characterize her temporary interests by proposing a recurrent neural network of analyzing the homogeneous relations between her latest clicked news and the candidate ones. Second, to describe the user-independent news timeliness effect, we propose a novel survival analysis model to estimate the instantaneous click probability of a certain news as the occurring probability of an event, where much sensational news tends to be picked out. Last, we fuse all effects to determine the probability of a user clicking on a certain news under the independent event assumption. We conduct extensive experiments on two real-world datasets. Experimental results demonstrate that our model can generate better news recommendations on both general scenario and cold-start scenario.

Each one for themselves: Exploring the energy security paradox of the European Union

Many European Union member states have shown reluctance to voluntarily decrease natural gas consumption during the 2022/2023 winter so as to demonstrate solidarity with members most severely hit by the current energy crisis. This situation underlines the EU's energy security paradox: even though a common approach would contribute to solving member states' energy security problems, they oppose the creation of such a policy as short-term national interests – considered to safeguard energy security – prevail. A common approach would help member states present a much stronger position than they are able to present individually, both in political and economic terms. However, the solidarity negotiations lesson does not show the ability of EU member states to work together vis-à-vis a common threat. The ability to agree on only a minimal amount of jointly purchased gas (and inability to agree on gas sanctions) underlines the fragmentation of the EU's energy security policy, which benefits Russia.

Explainable AI Model for Recognizing Financial Crisis Roots Based on Pigeon Optimization and Gradient Boosting Model

Utilizing Artificial Intelligence (AI) techniques to forecast, recognize, and classify financial crisis roots are important research challenges that have attracted the interest of researchers. Moreover, the Explainable Artificial Intelligence (XAI) concept enables AI techniques to interpret the results of processing and testing complex data patterns so that humans can find efficient ways to infer and interpret the logic behind classifying complex data patterns. This paper proposes a novel XAI model to automatically recognize financial crisis roots and interprets the features selection operation. Using a benchmark dataset, the proposed XAI model utilized the pigeon optimizer to optimize the feature selection operation, and then the Gradient Boosting classifier is utilized to recognize financial crisis roots based on the obtained reduct of the most important features. The practical results showed that the short-term interest rates feature is the most important feature by which financial crisis roots can be detected. Moreover, the classification results showed that the built-in Gradient Boosting classifier in the Pigeon Inspired Optimizer (PIO) algorithm achieved training and testing accuracy of 99% and 96.7%, respectively, in recognizing financial crisis roots, which is an efficient and better performance compared to the random forest classifier.

U.S. Dollar Swap Yields: An Analysis of the Dynamics of Monthly Changes

John Maynard Keynes asserted that the central bank sways the long-term interest rate through the influence of its policy rate on the short-term interest rate. Recent empirical research shows that Keynes’s conjecture holds for long-term Treasury yields in the United States. This article investigates whether Keynes’s claim also holds for the monthly changes in U.S.-dollar-denominated long-term swap yields by econometrically modeling its dynamics using an autoregressive distributed lag (ARDL) approach. The econometric modeling reveals that there is a statistically significant effect of the monthly changes in the Treasury bill rate on the monthly changes in swap yields of different maturity tenors after controlling for a host of macroeconomic and financial control variables. The findings from the econometric models that are estimated render a perspicacious Keynesian perspective on key policy questions and contemporary debates in macroeconomics and finance.

Differences in Unconventional Monetary Policy of Both the Federal Reserve Board and the Bank of Japan

This article consists of two dimensions. First, to draw the historically penetrating nature of money-supply, monetary policy, and central banks, to clarify what “conventional” monetary policy is. Conventional monetary policy doesn’t mean the direct control of the base money, but the monetary policy by operating short term interest rate. Therefore, money supply is regulated endogenously. I point out here that we can find in Capital by Karl Marx some basic conceptions required to consider the definition of conventional monetary policy. Second, to define “unconventional policy.” Unconventional monetary policy is the policy which directly controls the amount of base money (simply applying quantitative monetary theory). It is the Bank of Japan (BOJ)’s unconventional monetary policy. However, the Federal Reserve Board (Fed)’s is not the case. The Fed tried to influence the market movement or business cycle by the control of monetary means exclusive of interest rate. Amounts of base money was not the target, but its result. Therefore, I could conclude that the Fed remained in the framework of conventional monetary policy, even though its policy looked like an “unconventional” and “quantitative” one. It is the reason why the Fed could adopt an “exit strategy” flexibly. However, the BOJ has fallen into “monetization” without an “exit strategy” so far.