Oil price shocks and yield curve dynamics in emerging markets

In a local projections framework, we study the impact of oil price shocks, based on a refined approach to disentangle oil price movements, on the dynamics of the entire yield curve in nineteen emerging economies with different positions on the oil market. Responses of the term structure factors to oil market shocks are shown to differ conditional on not only the underlying sources that drive oil price, but also based on the oil-dependence of these economies. In particular, we find that oil price risk shocks put upward pressure on the level, slope, and curvature of interest rates across the board. Supply-driven shocks in oil markets cause a rise in the level of interest rates in oil-importing economies more significantly, yet the downward impact on yield curve slope is more pronounced in oil-exporting countries. Demand-driven shocks have a significant and persistent upward impact on level factors in oil-importing countries. Furthermore, the effect of precautionary demand shocks on the curvature factor is more pronounced in oil-importing countries vis-à-vis oil-exporters. Significance, direction, and duration of our results may guide monetary policymakers in emerging countries as well as international investors in portfolio and hedging decisions.http://www.elsevier.com/locate/irefhj2022Economic

Endogeneity of Money Supply: Evidence from Turkey

There is a long discussion among academics and central bankers about the theories of money supply. According to the exogenous view, central banks have the full control over money supply via policy actions including the adjustments of interest rates and reserve ratios, both of which alter commercial banks’ lending decisions. However, the theory of endogenous money supply emphasizes the role of demand for bank loans in money creation. More specifically, banks create money by meeting the demand of economic agents. In this study, we investigate which of the money supply theories holds in Turkish economy for the period 2006-2015 by employing cointegration and causality tests. Our findings show that the causality runs from bank loans to money supply both in the short and long terms, which supports the endogenous view in a sense that central bank and the banks fully meet the total demand for money in Turkish economy

Forecasting mid-price movement of Bitcoin futures using machine learning

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil

Sentiment regimes and reaction of stock markets to conventional and unconventional monetary policies : evidence from OECD countries

In this paper, we investigate how conventional and unconventional monetary policy shocks affect the stock market of eight advanced economies, namely, Canada, France, Germany, Japan, Italy, Spain, the U.K., and the U.S., conditional on the state of sentiment. In this regard, we use a panel vector auto-regression (VAR) with monthly data (on output, prices, equity prices, metrics of monetary policies, and consumer and business sentiments) over the period of January 2007 till July 2020, with the monetary policy shock identified through the use of both zero and sign restrictions. We find robust evidence that, compared to the low investor sentiment regime, the reaction of stock prices to expansionary monetary policy shocks is stronger in the state associated with relatively higher optimism, both for the overall panel and the individual countries (with some degree of heterogeneity). Our findings have important implications for academicians, investors, and policymakers.https://www.tandfonline.com/loi/hbhf20hj2022Economic

Does climate change affect bank lending behavior?

We examine how banks adjust credit supply in areas with higher exposure to climate risks by utilizing the province-level air pollution and loan growth data of a large emerging market, Turkey, following the Paris Agreement in 2015. Our results show that banks limit their credit extension to more polluted provinces in the post-agreement interval, implying that banks consider climate change-related risks and adjust their credit provisioning accordingly. Our baseline findings are intact against a myriad of robustness checks. We also find that the shift in the climate risk-credit provisioning nexus is asymmetric depending on the levels of air pollution.Publisher PDFPeer reviewe

The role of investor sentiment in forecasting housing returns in China : a machine learning approach

This paper analyzes the predictive ability of aggregate and disaggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. We find that our new aligned investor sentiment index has greater predictive power for housing returns than the principal component analysis (PCA)-based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the disaggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the disaggregate proxies of investor sentiment. Furthermore, when we let the machine learning models to choose from all key control variables and the aligned sentiment index, the forecasting accuracy is improved at all forecasting horizons, rather than just the short-run as witnessed under standard predictive regressions. This result suggests that machine learning methods are flexible enough to capture both structural change and time-varying information in a set of predictors simultaneously to forecast housing returns of China in a precise manner. Given the role of the real estate market in China's economic growth, our result of accurate forecasting of housing returns has important implications for both investors and policymakers.http://wileyonlinelibrary.com/journal/forEconomic

Threshold effects of inequality on economic growth in the US states : the role of human capital to physical capital ratio

Theory suggests that the effect of inequality on growth varies with the level of economic development, as captured by the ratio of human capital to physical capital. In particular, the effect is shown to be positive at lower levels of this ratio, and turns negative beyond a threshold in such models. Using a comprehensive panel of annual data for the 48 contiguous US states over the period 1948 to 2014, we find overwhelming evidence in support of this theory, unlike prior work on this topic. Hence, our paper highlights the importance of accurately measuring the process of economic development using data on human capital and physical capital, instead of using proxies that are not theoretically consistent. Understandably, if not done so, policymakers would end up undertaking incorrect decisions.http://www.tandfonline.com/loi/rael202021-05-24hj2020Economic

Connectedness of energy markets around the world during the COVID-19 pandemic

his paper studies the connectedness among energy equity indices of oil-exporting and oil-importing countries around the world. For each country, we construct time-varying measures of how much shocks this country transmits to other countries and how much shocks this country receives from other countries. We analyze the network of countries and find that, on average, oil-exporting countries are mainly transmitting shocks, and oil-importing countries are mainly receiving shocks. Furthermore, we use panel data regressions to evaluate whether the connectedness among countries is influenced by economic sentiment, uncertainty, and the global COVID-19 pandemic. We find that the connectedness among countries increases significantly in periods of uncertainty, low economic sentiment, and COVID-19 problems. This implies that diversification benefits across countries are severely reduced exactly during crises, that is, during the times when diversification benefits are most important

Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data-rich environment

This paper investigates the relation between yield curve and macroeconomic factors for ten emerging sovereign bond markets using the sample from January 2006 to April 2019. To this end, the diffusion indices obtained under four categories (global variables, inflation, domestic financial variables, and economic activity) are incorporated by estimating dynamic panel data regressions together with the yield curve factors. Besides, in order to capture dynamic interaction between yield curve and macroeconomic/financial factors, a panel VAR analysis based on the system GMM approach is utilized. Empirical results suggest that the level factor responds to shocks originated from inflation, domestic financial variables and global variables. Furthermore, the slope factor is affected by shocks in global variables, and the curvature factor appears to be influenced by domestic financial variables. We also show that macroeconomic/financial factors captures significant predictive information over yield curve factors by running individual country factor-augmented predictive regressions and variable selection algorithms such ridge regression, LASSO and Elastic Net. Our findings have important implications for policymakers and fund managers by explaining the underlying forces of movements in the yield curve and forecasting accurately dynamics of yield curve factors.PostprintPeer reviewe

Persistence of state-level uncertainty of the United States : the role of climate risks

Recent theoretical developments tend to suggest that rare disaster risks enhance the persistence of uncertainty. Given this, we analyse the impact of climate risks (temperature growth or its volatility), as proxies for such unusual events, on the persistence of economic and policy-related uncertainty of the 50 US states in a panel data set-up, over the monthly period of 1984:03 to 2019:12. Using impulse response functions (IRFs) from a regime-based local projections (LPs) model, we show that the impact of an uncertainty shock on uncertainty itself is not only bigger in magnitude when the economy is in the upper-regime of temperature growth or its volatility, but is also, in line with theory, is more persistent. Our results have important policy implications.http://www.elsevier.com/locate/ecolethj2022Economic