Financial Markets and Economic Activity
Financial Markets and Economic Activity
"International Stock Market Linkages," with B.Y. Chen, Handbook of Global Financial Market: Transformations, Dependence, and Risk Spillovers. Ed. S. Boubaker and D.K. Nguyen, World Scientific Publishing, March 2019. (Download )
Abstract: This paper investigates international stock market dynamics and their linkages. It uses factor models to extract stock market indicators from common cyclical stock components of industrialized countries, emerging markets, the BRICT, and global stock markets. We find that the stock market indicators for these groups are correlated with each other and with the global market factor. The BRICT display the highest average stock return and are the least correlated with the others. The stock return indicators as well as the global stock market factor show close relationship with economic downturns, entering in bear phases around the beginning of recessions, and in bull phases mid-way through recessions, anticipating future economic recovery. We also find that the stock return indicators are more persistent and, therefore, more predictable than the stock market of individual countries. We study international linkages across these stock market groups through impulse response analysis and find that economic development levels play an important role in shock propagation. In particular, all stock market indicators respond positively to global factor shocks, with the least reactive group being the BRICT, and the most responsive being the emerging markets. Interestingly, the BRICT respond negatively to positive shocks to industrialized countries stock markets, indicating that the BRICT may have a role in hedging risk.
Abstract: This paper investigates international stock market dynamics and their linkages. It uses factor models to extract stock market indicators from common cyclical stock components of industrialized countries, emerging markets, the BRICT, and global stock markets. We find that the stock market indicators for these groups are correlated with each other and with the global market factor. The BRICT display the highest average stock return and are the least correlated with the others. The stock return indicators as well as the global stock market factor show close relationship with economic downturns, entering in bear phases around the beginning of recessions, and in bull phases mid-way through recessions, anticipating future economic recovery. We also find that the stock return indicators are more persistent and, therefore, more predictable than the stock market of individual countries. We study international linkages across these stock market groups through impulse response analysis and find that economic development levels play an important role in shock propagation. In particular, all stock market indicators respond positively to global factor shocks, with the least reactive group being the BRICT, and the most responsive being the emerging markets. Interestingly, the BRICT respond negatively to positive shocks to industrialized countries stock markets, indicating that the BRICT may have a role in hedging risk.
"A Dynamic Factor Model of the Yield Curve Components as a Predictor of the Economy", with Z. Senyuz, International Journal of Forecasting. Vol. 32, April-June 2016, 324-343, 2016. (Working Paper or IJF )
Abstract: In this paper, we propose an econometric model of the joint dynamic relationship between the Treasury yield curve components and the economy, for predicting business cycle turning points. The nonlinear multivariate dynamic factor model takes into account not only the popular slope, but also information extracted from the level and curvature of the yield curve, and from macroeconomic variables. We investigate the interrelationship between the phases of cyclical fluctuations in yield curve components and the phases of the business cycle. The results indicate a strong interrelationship between the yield curve and the economy. The proposed model has substantial incremental predictive value relative to alternative specifications. This result holds both in-sample and out-of-sample, using revised and real time unrevised data
Abstract: In this paper, we propose an econometric model of the joint dynamic relationship between the Treasury yield curve components and the economy, for predicting business cycle turning points. The nonlinear multivariate dynamic factor model takes into account not only the popular slope, but also information extracted from the level and curvature of the yield curve, and from macroeconomic variables. We investigate the interrelationship between the phases of cyclical fluctuations in yield curve components and the phases of the business cycle. The results indicate a strong interrelationship between the yield curve and the economy. The proposed model has substantial incremental predictive value relative to alternative specifications. This result holds both in-sample and out-of-sample, using revised and real time unrevised data
"What Does Financial Volatility Tell Us About Macroeconomic Fluctuations?", with Z. Senyuz, Z. and E. Yoldas, Journal of Economic Dynamics and Control. Vol. 52, March, 340–360, 2015. (Download Working Paper or JEDC)
Abstract: We provide an extensive analysis of the predictive ability of financial volatility for economic activity. We consider monthly measures of realized and implied volatility from the stock and bond markets. In a dynamic factor framework, we extract the common long-run component of volatility that is likely to be linked to economic fundamentals. Based on powerful in-sample predictive ability tests, we find that the stock volatility measures and the common factor significantly improve macroeconomic forecasts of conventional financial indicators, especially over short horizons. A real-time out of sample assessment yields similar conclusions under the assumption of noisy revisions in macroeconomic data. In a nonlinear extension of the dynamic factor model, we identify two distinct volatility regimes, and show that the high-volatility regime provides early signals of the Great Recession, which was associated with severe financial distress and credit disintermediation.
Abstract: We provide an extensive analysis of the predictive ability of financial volatility for economic activity. We consider monthly measures of realized and implied volatility from the stock and bond markets. In a dynamic factor framework, we extract the common long-run component of volatility that is likely to be linked to economic fundamentals. Based on powerful in-sample predictive ability tests, we find that the stock volatility measures and the common factor significantly improve macroeconomic forecasts of conventional financial indicators, especially over short horizons. A real-time out of sample assessment yields similar conclusions under the assumption of noisy revisions in macroeconomic data. In a nonlinear extension of the dynamic factor model, we identify two distinct volatility regimes, and show that the high-volatility regime provides early signals of the Great Recession, which was associated with severe financial distress and credit disintermediation.
“Nonlinear Risk,” with S. Potter, Macroeconomic Dynamics, Vol. 5, No. 4, 621-646, 2001.(Download Repec or Nonlinearrisk)
Abstract: This paper proposes a flexible framework for analyzing the joint time series properties of the level and volatility of expected excess stock returns. An unobservable dynamic factor is constructed as a nonlinear proxy for the market risk premia with its first moment and conditional volatility driven by a latent Markov variable. The model allows for the possibility that the risk-return relationship may not be constant across the Markov states or over time. We find a distinct business cycle pattern in the conditional expectation and variance of the monthly value-weighted excess return. Typically, the conditional mean decreases a couple of months before or at the peak of expansions, and increases before the end of recessions. On the other hand, the conditional volatility rises considerably during economic recessions. With respect to the contemporaneous risk-return dynamics, we find an overall significantly negative relationship. However, their correlation is not stable, but instead varies according to the stage of the business cycle. In particular, around the beginning of recessions, volatility increase substantially, reflecting great uncertainty associated with these periods, while expected returns decrease, anticipating a decline in earnings. Thus, around economic peaks there is a negative relationship between conditional expectation and variance. However, toward the end of a recession, expected returns are at its highest value as an anticipation of the economic recovery, and volatility is still very high in anticipation of the end of the contraction. That is, the risk-return relation is positive around business cycle troughs. This time-varying behavior also holds for non-contemporaneous correlations of these two conditional moments.
Abstract: This paper proposes a flexible framework for analyzing the joint time series properties of the level and volatility of expected excess stock returns. An unobservable dynamic factor is constructed as a nonlinear proxy for the market risk premia with its first moment and conditional volatility driven by a latent Markov variable. The model allows for the possibility that the risk-return relationship may not be constant across the Markov states or over time. We find a distinct business cycle pattern in the conditional expectation and variance of the monthly value-weighted excess return. Typically, the conditional mean decreases a couple of months before or at the peak of expansions, and increases before the end of recessions. On the other hand, the conditional volatility rises considerably during economic recessions. With respect to the contemporaneous risk-return dynamics, we find an overall significantly negative relationship. However, their correlation is not stable, but instead varies according to the stage of the business cycle. In particular, around the beginning of recessions, volatility increase substantially, reflecting great uncertainty associated with these periods, while expected returns decrease, anticipating a decline in earnings. Thus, around economic peaks there is a negative relationship between conditional expectation and variance. However, toward the end of a recession, expected returns are at its highest value as an anticipation of the economic recovery, and volatility is still very high in anticipation of the end of the contraction. That is, the risk-return relation is positive around business cycle troughs. This time-varying behavior also holds for non-contemporaneous correlations of these two conditional moments.
“Stock Market Fluctuations and the Business Cycle,” Journal of Economic and Social Measurement, Vol. 25, No. 3, 235-258, 1999. (Download SSRN or SMBC)
Abstract: This paper examines the dynamic relationship between stock market movements and business cycles at the monthly frequency. Given the forward-looking behavior of stock market investors, it explores the possibility of using fluctuations in the stock market to forecast business cycle turning points using promptly available financial variables. The model generates predictions of business cycle turning points, using economic variables, and anticipation of these predicted turns, using financial variables. The empirical analysis is divided in two parts: first, it studies the historical track record of the interaction between the stock market indicator, the business cycle indicator, and the NBER-dated recessions. From this analysis, it is found that bear markets are closely associated with low-growth phases and economic recessions. Second, it implements an out-of-sample real time analysis of the performance of the stock market indicator in predicting economic recessions. The recursive real time forecasting performance of the stock market indicator is compared with the unrevised Composite Leading Indicator (CLI), computed by the Conference Board. The proposed indicator presents several advantages over the CLI. First, the financial indicator is less noisy than the CLI, which makes it easier to use it as a tool for anticipating turning points. Second, since financial market participants continuously update their expectations about the state of the economy as new information becomes available on a daily basis, over the course of the month movements in the financial indicator reflect revisions in these market perceptions. That is, the indicator can be computed at the end of each month reflecting updated information for that month. The unrevised CLI is also released at the end of the month, but it reflects information from the previous month. In addition, revisions are incorporated in the CLI index with a much lower frequency. Third, even though the CLI contains stock prices as one of its components, predictions of turning points using the stock market factor lead predictions using the unrevised CLI. Thus, the stock market indicator extracted from the model is a leading indicator of the state of the business cycle, and has been shown to be an effective tool to anticipate economic turning points in real time.
Abstract: This paper examines the dynamic relationship between stock market movements and business cycles at the monthly frequency. Given the forward-looking behavior of stock market investors, it explores the possibility of using fluctuations in the stock market to forecast business cycle turning points using promptly available financial variables. The model generates predictions of business cycle turning points, using economic variables, and anticipation of these predicted turns, using financial variables. The empirical analysis is divided in two parts: first, it studies the historical track record of the interaction between the stock market indicator, the business cycle indicator, and the NBER-dated recessions. From this analysis, it is found that bear markets are closely associated with low-growth phases and economic recessions. Second, it implements an out-of-sample real time analysis of the performance of the stock market indicator in predicting economic recessions. The recursive real time forecasting performance of the stock market indicator is compared with the unrevised Composite Leading Indicator (CLI), computed by the Conference Board. The proposed indicator presents several advantages over the CLI. First, the financial indicator is less noisy than the CLI, which makes it easier to use it as a tool for anticipating turning points. Second, since financial market participants continuously update their expectations about the state of the economy as new information becomes available on a daily basis, over the course of the month movements in the financial indicator reflect revisions in these market perceptions. That is, the indicator can be computed at the end of each month reflecting updated information for that month. The unrevised CLI is also released at the end of the month, but it reflects information from the previous month. In addition, revisions are incorporated in the CLI index with a much lower frequency. Third, even though the CLI contains stock prices as one of its components, predictions of turning points using the stock market factor lead predictions using the unrevised CLI. Thus, the stock market indicator extracted from the model is a leading indicator of the state of the business cycle, and has been shown to be an effective tool to anticipate economic turning points in real time.
“Coincident and Leading Indicators of the Stock Market,” with S. Potter, Journal of Empirical Finance, Vol. 7, No. 1, 87-111, 2000. (Download Repec or CoinleadSM)
Abstract: In this paper we have two goals: first, we want to represent monthly stock market fluctuations by constructing a non-linear coincident financial indicator. The indicator is constructed as an unobservable factor whose first moment and conditional volatility are driven by a two-state Markov variable. It can be interpreted as the investors’ real-time belief about the state of financial conditions. Second, we want to explore an approach in which investors may use their perceptions of the state of the economy to form forecasts of financial market conditions and possibly of excess returns. To investigate this, we build leading indicators as forecasts of the estimated coincident financial index. The leading indicators yield better within and out-of-sample performance in forecasting, not only the state of the stock market but also of excess stock returns, as compared with the performance obtained using linear methods that have been proposed in the existing literature.
Abstract: In this paper we have two goals: first, we want to represent monthly stock market fluctuations by constructing a non-linear coincident financial indicator. The indicator is constructed as an unobservable factor whose first moment and conditional volatility are driven by a two-state Markov variable. It can be interpreted as the investors’ real-time belief about the state of financial conditions. Second, we want to explore an approach in which investors may use their perceptions of the state of the economy to form forecasts of financial market conditions and possibly of excess returns. To investigate this, we build leading indicators as forecasts of the estimated coincident financial index. The leading indicators yield better within and out-of-sample performance in forecasting, not only the state of the stock market but also of excess stock returns, as compared with the performance obtained using linear methods that have been proposed in the existing literature.
"Monetary Policy Regimes and the Stock Market," with Chuanlei Sun, in Business Cycles in Economics: Types, Challenges and Impacts on Monetary Policies. Ed. Jason Hsu, Nova Science Chapter 6, 87-1116, 2014. (Download )
Abstract: This paper studies the relationship between monetary policy and the stock market across business cycle recessions and expansions. A nonlinear two-state Markov switching model is used to obtain regimes in interest rate cycles and in stock market cycles. The model identifies tight and loose monetary policy phases, and bear and bull stock market regimes. Turning points for each cycle are established and their lead-lag relationship is examined and contrasted with NBER recessions. We also examine the linear predictive relationship between interest rates and stock returns. The results indicate that there are strong linkages among interest rate cycles, stock market cycles, and business cycles. We find that, generally, the stock market enters a bear market phase at around the same time as monetary policy enters a tight phase, which is associated with the onset of economic recessions. We also find that future stock returns are substantially impacted by information on monetary policy regimes (i.e. loose or tight). On the other hand, interest rates are most influenced by future inflation and recessions rather than by changes in the stock market.
Abstract: This paper studies the relationship between monetary policy and the stock market across business cycle recessions and expansions. A nonlinear two-state Markov switching model is used to obtain regimes in interest rate cycles and in stock market cycles. The model identifies tight and loose monetary policy phases, and bear and bull stock market regimes. Turning points for each cycle are established and their lead-lag relationship is examined and contrasted with NBER recessions. We also examine the linear predictive relationship between interest rates and stock returns. The results indicate that there are strong linkages among interest rate cycles, stock market cycles, and business cycles. We find that, generally, the stock market enters a bear market phase at around the same time as monetary policy enters a tight phase, which is associated with the onset of economic recessions. We also find that future stock returns are substantially impacted by information on monetary policy regimes (i.e. loose or tight). On the other hand, interest rates are most influenced by future inflation and recessions rather than by changes in the stock market.