Time-varying impact of monetary policy shocks on US stock returns: The role of investor sentiment

This paper investigates how monetary policy shock affects the stock market of the United States (US) conditional on states of investor sentiment. In this regard, we use a recently developed estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks, which in turn is achieved by integrating the current short-term rate surprises, which are least affected by an information effect, into a vector autoregressive (VAR) model as an exogenous variable. When allowing for time-varying model parameters, we find that, compared to the low investor sentiment regime, the negative reaction of stock returns to contractionary monetary policy shocks is stronger in the state associated with relatively higher investor sentiment. Our results are robust to alternative sample period (which excludes the zero lower bound) and model specification and also have important implications for academicians, investors, and policymakers.     

Monetary policy and stock prices in small open economies: Empirical evidence for the new EU member states

This study presents evidence on the effect of domestic and Euro Area monetary policy on stock prices in four new EU member states of Central Europe and the main determinants of stock price volatility, estimating structural vector autoregressive models identified with short-run restrictions. We find that stock prices in the considered new EU member states are more sensitive to changes in the Euro Area interest rate than to the domestic one. Moreover, the bulk of stock price volatility in these countries is due to shocks related to exchange rate and Euro Area monetary policy. Overall, we find that local stock markets are more sensitive to external shocks than to domestic ones.     

The macroeconomic impact of economic uncertainty and financial shocks under low and high financial stress

External financial frictions might increase the severity of economic uncertainty shocks. We analyze the impact of aggregate uncertainty and financial condition shocks using a threshold vector autoregressive (TVAR) model with stochastic volatility during distinct US financial stress regimes. We further examine the international spillover of the US financial shock. Our results show that the peak contraction in euro area industrial production due to uncertainty shocks during a financial crisis is nearly-four times larger than the peak contraction during normal times. The US financial shocks have an influential asymmetric spillover effect on the euro area. Furthermore, the estimates reveal that the European Central Bank (ECB) is more cautious in implementing a monetary policy against uncertainty shocks while adopting hawkish monetary policies against financial shocks. In contrast, the Fed adopts a more hawkish monetary policy during heightened uncertainty, whereas it acts more steadily when financial stress rises in the economy.     

Economic policy uncertainty and stock market sector time-varying spillover effect: Evidence from China

This paper investigates the volatility spillover effect among the Chinese economic policy uncertainty index, stock markets, gold and oil by employing the time-varying parameter vector autoregressive (TVP-VAR) model. Three main results are obtained. Firstly, the optional consumption, industry, public utility and financial sectors are systemically important during the sample period. Secondly, among the four policy uncertainties, the uncertainty of fiscal policy and trade policy contributes more to the spillover effect, while the uncertainty of monetary policy and exchange rate policy contributes less to the spillover effect. Thirdly, during COVID-19, oil spillovers from other sources dropped rapidly to a very low point, it also had a significant impact on the net volatility spillover of the stock market. This paper can provide policy implication for decision-makers and reasonable risk aversion methods for investors.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

Do U.S. and Japanese uncertainty shocks play important roles in affecting transition mechanisms of Japanese stock market?

This research investigates the impacts of U.S. and Japanese uncertainty shocks on the transition mechanisms of the Japan stock market dynamics by utilizing the Markov-switching GARCH-jump model with a time-varying transition probability matrix and analyzes the economic policy uncertainty shock of Japan, the economic policy uncertainty shock of the U.S., and the uncertainty shock about the U.S. equity market volatility. The empirical results demonstrate that the Japan stock market responds to most shocks with the exception of the U.S. economic policy uncertainty shock. The equity market volatility shock of the U.S. plays a more crucial role than the economic policy uncertainty shock of Japan. Furthermore, an increase in the U.S. equity market volatility shock reveals totally different signals in different volatile states. It signals an adverse belief about the Japan stock market in a high-volatile state, but signals an optimism viewpoint about the Japan stock market in a low-volatile state. Finally, the impact of the uncertainty shock about the U.S. stock market volatility is stronger in a high-volatile market than in a low-volatile market.     

The effects of foreign exchange and monetary policies in Russia

This paper examines the effects of Russian foreign exchange and monetary policies under conditions of abundant natural resources during the period 1999–2011 using structural VAR models. The results suggest that monetary policy shocks, which are identified as money supply disturbances, have a persistent effect on real output, and more than half of the volatility in real output can be explained by changes in the money supply. Furthermore, the analysis reveals that stock prices are a more significant transmission channel of monetary policy than bank loans.     

News shocks and asset price volatility in general equilibrium

We study equity price volatility in general equilibrium with news shocks about future productivity and monetary policy. As West (1988) shows, in a partial equilibrium present discounted value model, news about the future cash flow reduces asset price volatility. We show that introducing news shocks in a canonical dynamic stochastic general equilibrium model may not reduce asset price volatility under plausible parameter assumptions. This is because, in general equilibrium, the asset cash flow itself may be affected by the introduction of news shocks. In addition, we show that neglecting to account for policy news shocks (e.g., policy announcements) can potentially bias empirical estimates of the impact of monetary policy shocks on asset prices.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.     

Impact of uncertainty on high frequency response of the U.S. stock markets to the Fed's policy surprises

This paper examines the impact of uncertainty on estimated response of stock returns to U.S. monetary policy surprise. This is motivated by the Lucas island model which suggests an inverse relationship between the effectiveness of a policy and the level of uncertainty in the economy. Using high frequency daily data from the Federal funds futures market, we first estimate the response of S&P 500 stock returns to monetary policy surprises within the time varying parameter (TVP) model. We then analyze the relationship of these time varying estimates with the benchmark VIX index and alternative measures of uncertainty. Evidence suggests a significant negative relationship between the level of uncertainty and the time varying response of S&P 500 stock returns to unanticipated changes in the interest rate. Thus, at higher levels of uncertainty the impact of monetary policy shocks on stock markets is lower. The results are robust to different measures of uncertainty.     

Volatility forecasting in European government bond markets

In this paper we examine the predictive power of the heterogeneous autoregressive (HAR) model for the return volatility of major European government bond markets. The results from HAR-type volatility forecasting models show that past short- and medium-term volatility are significant predictors of the term structure of the intraday volatility of European bonds with maturities ranging from 1 year up to 30 years. When we decompose bond market volatility into its continuous and discontinuous (jump) component, we find that the jump component is a significant predictor. Moreover, we show that feedback from past short-term volatility to forecasts of future volatility is stronger in the days that precede monetary policy announcements.     

Efficient predictability of stock return volatility: The role of stock market implied volatility

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our in-sample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.     

Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Monetary Policy and Asset Prices: A Markov‐Switching DSGE Approach

This paper estimates a Markov‐switching dynamic stochastic general equilibrium model by incorporating stock prices in monetary policy rules in order to identify the Federal Reserve's stance toward them. Based on the data from 1984:Q1 to 2009:Q2, I find that historical evidence of the policy reaction toward stock prices is weak except for the stock market bubble of the 1990s. A counterfactual exercise shows that the rapid growth in stock prices during that period would have been significantly higher if monetary policy had been independent of the stock market. However, unconditional macroeconomic volatility increases with the degree of policy responsiveness toward stock prices. Copyright © 2017 John Wiley & Sons, Ltd.     

Economic uncertainty,oil prices,hedging and U.S. stock returns of the airline industry

This paper examines the impacts of economic policy uncertainty and oil price shocks on stock returns of U.S. airlines using both industry and firm-level data. Our empirical approach considers a structural vector-autoregressive model with variables recognized to be important for airline returns including jet fuel price volatility. Empirical results confirm that oil price increase, economic uncertainty and jet fuel price volatility have significantly adverse effect on real stock returns of airlines both at industry and at firm level. In addition, we also find that hedging future fuel purchase has statistically positive impact on the smaller airlines. Our results suggest policy implications for practitioners, managers of airline industry and commodity investors.     

U.S. uncertainty and Asian stock prices: Evidence from the asymmetric NARDL model

When uncertainty reduces spending among U.S. consumers, it may affect the bottom line stock performance of Asian producers that cater to their needs. Theory predicts that the impact of uncertainty will be asymmetrical: during the two phases of the business cycle, countercyclic shocks will outweigh procyclic shocks, resulting in phase-specific equilibrium price adjustments. We conjecture that relative to recessions, recoveries bring larger long-run price adjustments, a response to pent-up growth potential. This is an extension of existing theories, which predict that recoveries bring overshooting, a transient reaction to pent-up demand. We test for these asymmetric uncertainty effects on 11 Asian stock market indices over the 2000M08 – 2017M02 period. Our independent measures include the economic policy uncertainty index (EPU) of Baker, Bloom, and Davis (2016), the Chicago Board Options Exchange implied volatility index (VIX), and the financial uncertainty indicator (JLN) of Jurado, Ng, and Ludvigson (2015). To characterize asymmetry, we employ the nonlinear autoregressive distributed lag (NARDL) model of Shin, Yu, and Greenwood-Nimmo (2014), in which both short- and long-run nonlinearities are captured through positive and negative partial sum decompositions of the explanatory variable(s). Using the NARDL output, we test three hypotheses. The first, that increases in uncertainty (decreases in uncertainty) result in stock price drops (stock price rises), is broadly supported by our analysis. The second, that equilibrium adjustments following negative countercyclic uncertainty shocks exceed those following positive movements, is supported fully by the EPU analysis and partially by the VIX and JLN analyses. The third hypothesis, that recoveries are characterized by overshooting, is consistent only with the behavior of the Chinese stock responses to EPU and VIX shocks. Our results demonstrate the advantages of the NARDL model in characterizing asymmetry. They suggest that while long-run asymmetry is fairly consistent across countries, short-run asymmetry is more country-specific.     

Spillover effects in oil-related CDS markets during and after the sub-prime crisis

This paper investigates the return and volatility spillover effects across oil-related credit default swaps (CDSs), the oil market, and financial market risks for the US during and after the subprime crises. The empirical analysis is based on monthly return and realized volatility data from February 2004 to April 2020. We estimate both static and dynamic generalized dynamic spillover measures based on vector autoregressive (VAR) models. Our full sample empirical findings show that the oil market is the primary source of risk transmission for all the oil-related credit default swaps, while the bond market is the highest source of risk transmission to the stock market and vice versa. We also provide evidence that the regulated monopoly US utility sector has the least role in volatility transmission. Furthermore, the bailout program conducted by the US Treasury and Federal Reserve helped stabilize the US financial market through the purchase of toxic assets after the subprime financial crisis. We find strong evidence that the federal funds rate hike cycles lessen total risk transmission throughout the US bond market. Finally, our findings assert that oil price shocks have a significant effect on the oil-related CDSs in some sub-periods via the demand and supply transmission channels.     

Time-frequency effect of crude oil and exchange rates on stock markets in BRICS countries: Evidence from wavelet quantile regression analysis

This study examines the effects of oil prices and exchange rates on stock market returns in BRICS countries (Brazil, Russia, China, India and South Africa) from a time–frequency perspective over the period 2009–2020. We use wavelet decomposition series to develop a threshold rolling window quantile regression to detect time–frequency effects at various scales. The empirical results are as follows. First, our findings confirm that the effects of both crude oil prices and exchange rates on BRICS stock returns are asymmetric. Positive shocks of crude oil have a greater impact on a bull market, whereas negative shocks have a greater impact on a bear market. Second, there is a short-term enhancement effect of crude oil and exchange rate on BRICS stock markets. In addition, volatility in the macro financial environment also exacerbates the impacts of oil prices and exchange rates on the stock market, and these fluctuations are heterogeneous. Overall, these findings provide useful insights for international investors and policy makers.     

Forecast on silver futures linked with structural breaks and day-of-the-week effect

Silver future is crucial to global financial markets. However, the existing literature rarely considers the impacts of structural breaks and day-of-the-week effect simultaneously on the volatility of silver future price. Based on heterogeneous autoregressive (HAR) theory, we establish six new type heterogeneous autoregressive (HAR) models by incorporating structural breaks and day-of-the-week effect to forecast the volatility. The empirical results indicate that new models’ accuracy is better than the original HAR model. We find that structural breaks and the day-of-the-week effect contain much forecasting information on silver forecasting. In addition, structural breaks have a positive effect on the silver futures’ volatility. Day-of-the-week effect has a significantly negative influence on silver futures’ price volatility, especially in the mid-term and the long-term. Our works is the first to combine the structural breaks and day-of-the-week effect to identify more market information. This paper provides a better forecasting method to predict silver future volatility.     

Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.