We introduce a no-arbitrage dynamic term structure model integrated with a shadow rate and drifting trends to estimate the real interest rate trend in the United States, the United Kingdom, and Germany from 1972 to 2022. Our findings reveal declining interest rate trends across all three countries since the 1990s, underpinned by a significant co-movement among them. We evaluate the long-run correlations between the interest rate trends and various macro-economic fundamentals to shed light on potential driving forces of the declining interest rate trend.

Real estate price drop during the financial crisis period tends to weaken firms' borrowing capacity. To buffer the adverse impact of financial shock on R&D &D activities, innovative firms turn to their internal cash reserves to substitute external credit. By examining U.S. listed innovative firms during 2008-2012, we find that a $1 decrease in real estate value leads $0.70 decrease in cash holdings of innovative firms. Additionally, we observe that the R&D &D buffering effect is more pronounced among financially constrained firms and firms with more pre- crash cash reserves. In contrast, the buffering effect is insignificant among non-innovative firms where sustaining R&D &D expenditure is not an urgent need.

This study examines the time-varying effects of uncertainty shocks identified using the external instrument method on the broad-based movement of commodity returns since the early 1990s. We employ a vector autoregression augmented dynamic factor model with time-varying parameters and stochastic volatility to extract a common factor from 43 commodity returns. We find that uncertainty shocks reduce commodity returns across the board through this common factor and that their effects vary significantly over time, with a tendency to grow much stronger during recessions. Furthermore, uncertainty shocks often lead to dollar appreciation, so they can potentially account for the seemingly negative correlation between commodity returns and strength of the US dollar.

This paper investigates how financial conditions and macroeconomic uncertainty jointly affect macroeconomic tail risks. We first document that tight financial conditions decrease all conditional quantiles of future output growth in the near term, while high macroeconomic uncertainty only stretches the interquartile range. Because financial conditions and uncertainty comove substantially, the conditional means and variances shift simultaneously in the opposite direction. Consequently, the downside risk varies much more than the upside risk. A quantile impulse response analysis indicates that both financial and uncertainty shocks are responsible for the asymmetric behaviors in the downside and upside risks.

We propose a novel decomposition approach to study the degree of co-movement of international housing markets while distinguishing among different economic drivers. We find that the housing market variability for an average country was mainly driven by the common housing risk premium components during the years leading up to the 2007-08 subprime financial crisis. A decrease in the common housing risk premium was followed by a housing boom and economic expansion in the United States prior to the crisis. Our findings add to the understanding of the role of common risk factors across international housing markets before the crisis.

In this paper, we first utilize a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of weekly state-level economic conditions indexes of the United States (US) over the period of April 1987 to August 2021. In the second step, we forecast the state-level factors in a panel data set-up based on the information content of corresponding state-level climate risks, as proxied by changes in temperature and its SV. The forecasting experiment depicts statistically significant evidence of out-of-sample predictability over a one-month- to one-year-ahead horizon, with stronger forecasting gains derived for states that do not believe that climate change is happening and are Republican. We also find evidence of national climate risks in accurately forecasting the national factor of economic conditions. Our analyses have important policy implications from a regional perspective.

We use a time-varying parameter dynamic factor model with stochastic volatility estimated using Bayesian methods to disentangle the relative importance of the common component in Federal Housing Finance Agency house price movements from state-specific shocks, over the quarterly period of 1975Q2 to 2017Q4. We find that the contribution of the national factor in explaining fluctuations in house prices is critical. We then use a Bayesian change-point vector autoregressive model that allows for different regimes throughout the sample period, to study the impact of aggregate supply, aggregate demand, (conventional) monetary policy, and term-spread shocks, identified based on sign restrictions on the national component of house price movements. While monetary policy and other shocks are found to be quite dominant early on, we find evidence that the national factor has been detached from the identified macroeconomic shocks since 2014, thus suggesting that a “national bubble” might be brewing again in the US housing market.

Summary We propose a novel econometric approach to estimating time‐varying policy effects using external instruments in the presence of time‐varying instrument relevance in a factor‐augmented VAR model with data on the United States, Canada, Germany, Japan, and the United Kingdom. We find that US monetary policy shocks are an important driver of the exchange rate movements, with no delayed overshooting. We show that estimates of spillover effects of US monetary policy shocks on the inflation and real economic activity would be distorted without considering time variation in instrument relevance, and time variation in policy effects reflects primarily varying shock size, not their transmission.

Sanitary landfills and uncontrolled dumpsites are plastic wastes (PWs) reservoirs containing ∼60% of all the plastics ever made, amounting to 5,000 × 106 tons as of 2017. The distribution, long-term behavior, and release of macro- and microplastics (MPs) from disposal sites are critical to global plastics pollution, but are poorly understood and lack systematic assessments. We review comprehensively the available knowledge in the three aspects herein. The spatial and temporal distribution of PW in 616 municipal solid waste (MSW) samples retrieved from 275 disposal sites in 56 countries are summarized. The weight percentages of PW (%PW) generally decrease with increasing year of disposal and disposal depth. Other influential factors are disposal duration and country income level. The %PW values in different disposal sites show high regionality and spatial variability and heterogeneity. Disposal sites mostly have harsh temperature and stress, reactive liquids, and microbial activities, which are conducive to long-term processes of PW and MPs. The major processes are chemical degradation, dissolution, leaching and adsorption, biological degradation, mechanical wearing, pneumatic and hydrological transport and deposition, and conglomeration. PW leaves disposal sites via recycling, scavenging, mining, wind and surface runoff, coastal erosion and flooding, and slope failure. The release and removal pathways of PW from disposal sites have been recognized only qualitatively. In addition, the sources, presences, and secondary generation of MPs in disposal sites have been studied occasionally, whereas the transport and fate of MPs within and from disposal sites remain largely unstudied.

We propose a new nonlinear Markov-STAR model to capture both the Markov switching and smooth transition dynamics for real exchange rates. We derive stationarity conditions for the model and apply it to the real exchange rates of 17 countries. We relate switching equilibrium rates and volatilities to a set of relevant macroeconomic variables and find, consistent with economic intuitions, that an economy deteriorating relative to the US economy tends to see a significantly increased likelihood of real exchange rate depreciation. Moreover, we document significant connections between rising economic uncertainties and real exchange rate changes as well as exchange rate volatility.

We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of fundamental variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. While controlling for stochastic volatility, we find that a variable which measures non-fundamental noise in the Treasury yield curve helps to predict 1-month-ahead excess stock returns, but only during sample periods that include the Great Recession. For these sample periods, higher noise predicts lower excess stock returns, implying that a shortage of arbitrage capital in financial markets allowed excess returns to drop below the levels justified by fundamentals. The statistical significance of the predictor variables that control for stochastic volatility are also typically sensitive to the sample period. Measures of implied and realized stock return variance cease to be signficant when the COVID-influenced data from early 2020 onward is included.

We develop a dynamic model of a BHC that encompasses both a trading desk and a loan desk, and explore the role of risk attitude and overleveraging by the trading desk. We trace the impact of monetary policy and market innovations on bank behavior in the presence of Basel III type regulations. We show that the value of the BHC is enhanced by operating both desks. We explore alternative regulatory remedies to ongoing efforts to ring-fence the proprietary trading business, and show that regulations that target bank governance can mitigate possible rogue trading and the overleveraging problem.

This study examines the time-varying effects of uncertainty shocks on the broadbased movement of commodity returns since the early 1990s. We employ a vector autoregression (VAR) augmented dynamic factor model with time-varying parameters and stochastic volatility (TVP-VAR-DFM-SV) to extract a common factor from 43 commodity returns. We then create interactions with observed variables, including the U.S. short-term interest rate, excess bond premium, global demand indicator, and the U.S. dollar exchange rate. We identify exogenous uncertainty shocks using an external instrumental variable method. We document an increasing contribution of the common factor to commodity returns since the mid-2000s and find that uncertainty shocks decrease commodity returns across the board. While the negative impact is significant during most periods, its magnitude varies substantially over time, tending to grow much stronger during recessions. Interestingly, uncertainty shocks often lead to dollar appreciation, and thus can potentially account for the seemingly negative correlation between commodity returns and the strength of the U.S. dollar.

We propose a shadow rate no-arbitrage DTSM with drifting trends to estimate the natural rate of interest. With the shadow rate reflecting overall financial market condition (Wu and Zhang (2019)), its long run forecast (in real term), defined as our natural rate, provides a useful measure against which monetary policy stance may be assessed. We apply our model to treasury yields data in the United States, the United Kingdom, and Germany for the sample from November 1972 to December 2019. We find that all three natural rates have been declining since as early as the 1990s and have all turn negative in the most recent few years. Furthermore, there is a strong co-movement among the three natural rates indicating that global factors contribute significantly to the natural rate declining dynamics, corroborating findings in Holston et al. (2017). The term premium estimates from our model are quite stationary and are significantly and positively correlated with several inflation uncertainty measures, consistent with Wright (2011).

We generalize the arbitrage-free Nelson–Siegel (AFNS) model to allow λt to vary over time. We find that the time-varying λt, which determines the relative factor loadings, typically reaches its local peak before starting to decline right before a recession. Through conducting extensive in-sample and out-of-sample forecast exercises, we show that the information in the time-varying λt factor has strong predictive power for business cycles and real economic activity. In particular, λt contains additional useful information beyond those in conventional yield curve predictors, such as the yield spread. We argue and also document empirical evidence that the information in λt is related to the market perception of the economic risk and uncertainty.

China’s 13th Five-Year Plan highlighted the need for transition to a market-based monetary policy framework. The transition will involve changing the intermediate target of monetary policy from measures of money supply (such as M2) to a policy interest rate. All central banks in developed economies and in many emerging market economies have completed this transition in the past few decades. In most countries, the transition from monetary targeting to interest rate targeting was driven largely by three factors: • The correlation between the quantitative target—such as money supply— and economic growth and inflation has weakened, making it more difficult to achieve prescribed economic goals and price stability through controlling the money supply. • Money demand has become less stable and predictable in light of financial innovation. Targeting the money supply alone may generate unintended volatility in market-driven interest rates. • The monetary policy transmission mechanism from the policy rate to market interest rates (such as bond yields and deposit and lending rates) and the real economy has become more effective. As China moves toward an interest-rate-based monetary policy framework, gaps remain. The country has largely met the first two conditions (as mentioned above) for the transition to targeting interest rates, but the influence of policy rates on market rates remains questionable. It is unclear to what extent a change in the policy rate would affect bond yields and deposit and lending rates (which eventually affect the real economy).

This paper examines the relationship between 43 commodity returns using a dynamic factor model with time varying stochastic volatility. The dynamic factor model decomposes each commodity return into a common (market), sector‐specific and commodity‐specific component. It enables the variance attributed to each component to be estimated at each point in time. We find the return variation explained by the common factor has increased substantially for the recent period and is statistically significant for the vast majority of commodities since 2004 (at each point in time) This phenomenon is the strongest for non‐perishable products. We link the amount of variation explained by the common factor to economic variables.

This paper develops a new measure of comovement in the banking sector that takes into account the dynamic nature of interlinkages in the return on assets (ROA) and net chargeoffs (NCO) among different bank holding corporations by using a dynamic factor model with time-varying parameters and stochastic volatility. We find that the degree of comovement in ROA and NCO peaked during the 2008-2009 financial crisis, suggesting a significant increase in sector-wide stress. Using the least absolute shrinkage and selection operator (LASSO) methodology, we show that comovement and risk measures derived from our approach perform well when compared to other widely used measures of systemic risk in explaining real economic activity. (c) 2020 Elsevier B.V. All rights reserved.