REGIME CHANGES IN STOCK RETURNS

This paper studies three sources of instability in parameter estimates of stock return models (1) time-varying expected mean returns, (2) time-varying return volatility and (3) changing institutional factors. We model United States stock returns as a function of a constant expected return and financing costs resulting from an institutional feature, delayed delivery. We examine two eight-year periods and find that both contain a regime shift driven by an abrupt change in volatility. The first occurs during an international monetary crisis amid important Watergate developments. The second is on the first trading day after the reappointment of Paul Volcker as the chairman of the United States Federal Reserve Board.     

The market price of risk of the variance term structure

In this paper I examine the market price of risk of the variance term structure. To this end, the S&P 500 option implied variance term structure is used as a proxy for aggregate variance risk. Principal component analysis shows that time variation in the variance term structure over the 1996–2012 period can be explained mainly by two factors which capture changes in the level and slope. The market price of risk of each factor is estimated in the cross-section of stock returns. The slope of the variance term structure is the most significant factor in the cross-section of stocks returns and carries a negative risk premium. The slope factor has also some predictive ability over long horizon equity returns.     

What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns

This paper uses a vector autoregressive model to decompose excess stock and 10-year bond returns into changes in expectations of future stock dividends, inflation, short-term real interest rates, and excess stock and bond returns. In monthly postwar U.S. data, stock and bond returns are driven largely by news about future excess stock returns and inflation, respectively. Real interest rates have little impact on returns, although they do affect the short-term nominal interest rate and the slope of the term structure. These findings help to explain the low correlation between excess stock and bond returns.     

Financial conditions,macroeconomic factors and disaggregated bond excess returns

Bond excess returns can be predicted by macro factors, however, large parts remain still unexplained. We apply a novel term structure model to decompose bond excess returns into expected excess returns (risk premia) and the innovation part. In order to explore these risk premia and innovations, we complement macro variables by financial condition variables as possible determinants of bond excess returns. We find that the expected part of bond excess returns is driven by macro factors, whereas innovations seem to be mainly influenced by financial conditions, before and after the financial crisis. Thus, financial conditions, such as financial stress, deserve attention when analyzing bond excess returns.     

On the Economic Link Between Asset Prices and Real Activity

Abstract:  This paper presents a model linking two financial markets (stocks and bonds) with real business cycle, in the framework of the Consumption Capital Asset Pricing Model with Generalized Isoelastic Preferences. Besides interest rate term spread, the model includes a new variable to forecast economic activity: stock market term spread. This is the slope of expected stock market returns. The empirical evidence documented in this paper suggests systematic relationships between business cycle's state and the shapes of two yield curves (interest rates and expected stock returns). Results are robust to changes in measures of economic growth, stock prices, interest rates and expectations generating mechanisms.     

Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data

This paper tests the relation between stock excess returns and risk factors measured by volatility. The sources of the volatility are based on the volatility of macroeconomic factors and time-series volatility. To model the macroeconomic fundamentals, we divide the risk into real and financial volatilities pertinent to Taiwan's economic environment. By examining the data of indusry excess returns and market excess returns, we find evidence to reject the hypothesis that the stock excess returns are independent of the real and financial volatilities.     

Asset market linkages: Evidence from financial, commodity and real estate assets

We use a general Markov switching model to examine the relationships between returns over three different asset classes: financial assets (US stocks and Treasury bonds), commodities (oil and gold) and real estate assets (US Case-Shiller index). We confirm the existence of two distinct regimes: a “tranquil” regime with periods of economic expansion and a “crisis” regime with periods of economic decline. The tranquil regime is characterized by lower volatility and significantly positive stock returns. During these periods, there is also evidence of a flight from quality - from gold to stocks. By contrast, the crisis regime is characterized by higher volatility and sharply negative stock returns, along with evidence of contagion between stocks, oil and real estate. Furthermore, during these periods, there is strong evidence of a flight to quality - from stocks to Treasury bonds.     

Can interest rate factors explain exchange rate fluctuations?

The level, slope, and curvature of the yield curve reflect time variation in investors’ risk premia and are known predictors of excess bond returns and economic activity. In this paper, I develop a term structure model under complete markets and no arbitrage to relate these interest rate factors to exchange rate fluctuations. The Gaussian properties of the stochastic discount factors imply non-linearities in exchange rate risk premia that are shown to account for up to half of the in-sample variation in one-year currency returns for different country pairs during the 1980s–2015 period. I find that interest rate factors help explain exchange rate fluctuations in and out of sample, particularly at longer horizons, and yield profitable currency portfolios relative to standard carry trade strategies.     

The Term Structure of Real Rates and Expected Inflation

Changes in nominal interest rates must be due to either movements in real interest rates, expected inflation, or the inflation risk premium. We develop a term structure model with regime switches, time‐varying prices of risk, and inflation to identify these components of the nominal yield curve. We find that the unconditional real rate curve in the United States is fairly flat around 1.3%. In one real rate regime, the real term structure is steeply downward sloping. An inflation risk premium that increases with maturity fully accounts for the generally upward sloping nominal term structure.     

Regime Shifts in a Dynamic Term Structure Model of U.S. Treasury Bond Yields

This article develops and empirically implements an arbitrage-free,dynamic term structure model with "priced" factor and regime-shiftrisks. The risk factors are assumed to follow a discrete-timeGaussian process, and regime shifts are governed by a discrete-timeMarkov process with state-dependent transition probabilities.This model gives closed-form solutions for zero-coupon bondprices, an analytic representation of the likelihood functionfor bond yields, and a natural decomposition of expected excessreturns to components corresponding to regime-shift and factorrisks. Using monthly data on U.S. Treasury zero-coupon bondyields, we show a critical role of priced, state-dependent regime-shiftrisks in capturing the time variations in expected excess returns,and document notable differences in the behaviors of the factorrisk component of the expected returns across high and low volatilityregimes. Additionally, the state dependence of the regime-switchingprobabilities is shown to capture an interesting asymmetry inthe cyclical behavior of interest rates. The shapes of the termstructure of volatility of bond yield changes are also verydifferent across regimes, with the well-known hump being largelya low-volatility regime phenomenon.     

Dividend Dynamics and the Term Structure of Dividend Strips

Many leading asset pricing models are specified so that the term structure of dividend volatility is either flat or upward sloping. These models predict that the term structures of expected returns and volatilities on dividend strips (i.e., claims to dividends paid over a prespecified interval) are also upward sloping. However, the empirical evidence suggests otherwise. This discrepancy can be reconciled if these models replace their proposed dividend dynamics with processes that generate stationary leverage ratios. Under such policies, shareholders are forced to divest (invest) when leverage is low (high), which shifts risk from long‐ to short‐horizon dividend strips.     

Risk profiles: how do they change when stock markets collapse?

This paper has two main objectives: the first is to unveil the relative importance of global versus local risk factors in influencing excess returns in the emerging country stock markets; the second is to analyse how the observed risk profiles change when markets undergo a major crisis. Our main country of focus is Mexico, but we also analyse six Asian countries which went through the 1997 Asian crisis. Our findings indicate that during stable periods investors are mainly concerned about global risk factors, whereas close to a crisis they also include local factors in their information sets in forming expectations about future excess returns.     

INTERTEMPORAL ASSET PRICING WITHOUT CONSUMPTION DATA: EMPIRICAL TESTS

In this paper I conduct tests of an intertemporal asset pricing model using variables that forecast stock returns as the risk factors. I document that the forecasting variables are priced so that expected excess returns are related to their conditional covariances with the forecasting variables. The variability in the covariance risk fails to explain the cross-sectional and time-series variation in expected stock returns. Evidence rejects restrictions on the prices of covariance risk imposed by the model with constant volatilities. I also find that an extended model that allows time-varying conditional volatilities is misspecified.     

Stock Returns and the Business Cycle

This article examines whether movements in economic factors dictated by the dividend discount model can explain broad movements in stock returns over the business cycle. As anticipated, stock returns decrease throughout economic expansions and become negative during the first half of recessions. Returns are largest during the second half of recessions, suggesting an important role for expected earnings. These results are consistent with the notion that expected stock returns vary inversely with economic conditions, yet suggest that realized returns are especially poor indicators of expected returns prior to turning points in the business cycle.     

Consumption-based CAPM models: International evidence

We examine the performance of several types of the consumption-based CAPM (C-CAPM) models to explore if consumption factors matter for determining excess returns across 17 MSCI country indexes. While the classic world C-CAPM does exhibit some power in explaining cross-sectional variations of expected excess returns, the model seems to require an implausibly large coefficient of risk aversion. The more sophisticated models including the heterogeneous C-CAPM, the world surplus consumption and the habit-formation models provide more reasonable estimates and add substantial explanatory power for the variation in the cross section of excess stock returns. Our results suggest that country-specific consumption risk is not fully diversified thus implying that stock returns are related to idiosyncratic consumption risk.     

Expected Inflation, Interest Rates, and Stock Returns

This paper documents a long-lived asymmetrical relationship between interest rate changes and subsequent stock returns. Drops in interest rates are followed by twelve months of excess stock returns, while increases in interest rates have little effect. The results are robust to the choices of short-term interest rate and stock index. These findings cannot be explained by Geske and Roll's [10] reversed causality argument; nor do they appear to result from periods of unusual interest rates or stock returns. Since interest rate changes are generally used as proxies for changes in expected inflation, the results provide new insights into previous research on inflation and stock returns, and there are important implications for the literature on time-varying risk premia.     

Investor sentiment and economic forces

Economic theory suggests that pervasive factors should be priced in the cross-section of stock returns. However, our evidence shows that portfolios with higher risk exposure do not earn higher returns. More importantly, our evidence shows a striking two-regime pattern for all 10 macro-related factors: high-risk portfolios earn significantly higher returns than low-risk portfolios following low-sentiment periods, whereas the exact opposite occurs following high-sentiment periods. These findings are consistent with a setting in which market-wide sentiment is combined with short-sale impediments and sentiment-driven investors undermine the traditional risk-return tradeoff, especially during high-sentiment periods.     

Predicting stock returns: A regime-switching combination approach and economic links

This paper introduces a regime-switching combination approach to predict excess stock returns. The approach explicitly incorporates model uncertainty, regime uncertainty, and parameter uncertainty. The empirical findings reveal that the regime-switching combination forecasts of excess returns deliver consistent out-of-sample forecasting gains relative to the historical average and the Rapach et al. (2010) combination forecasts. The findings also reveal that two regimes are related to the business cycle. Based on the business cycle explanation of regimes, excess returns are found to be more predictable during economic contractions than during expansions. Finally, return forecasts are related to the real economy, thus providing insights on the economic sources of return predictability.     

“好”的不确定性、“坏”的不确定性与股票市场定价——基于中国股市高频数据分析

不确定性并不是都是“坏”的,“好”的不确定性也同样存在。本文采用Barndorff-Nielsen et al.(2010)提出的已实现半方差作为股票市场“好”的不确定性和“坏”的不确定性的代理指标,并在此基础上构建了相对符号变差(RSV),分析RSV对中国股市定价的影响。基于2007-2017年中国A股5分钟高频数据的实证研究发现:(1)与理论解释相一致,RSV与股票收益之间呈现负相关关系。无论是基于单变量分组、双变量分组还是公司层面的截面回归,这种影响在经济上和统计上都显著。(2)RSV是独立于已实现偏度的一个重要定价因子,且RSV对股票的定价能力强于已实现偏度的定价能力。(3)RSV对中国股市的影响是状态依存的,相对于经济景气程度高的状态,在经济景气程度低的状态下RSV定价影响更大。(4)基于RSV构建的投资组合的表现明显优于市场超额收益率组合、SMB组合和HML组合的表现。     

Banks’ equity performance and the term structure of interest rates

Using an extensive global sample, this paper investigates the impact of the term structure of interest rates on bank equity returns. Decomposing the yield curve to its three constituents (level, slope and curvature), the paper evaluates the time‐varying sensitivity of the bank's equity returns to these constituents by using a diagonal dynamic conditional correlation multivariate GARCH framework. Evidence reveals that the empirical proxies for the three factors explain the variations in equity returns above and beyond the market‐wide effect. More specifically, shocks to the long‐term (level) and short‐term (slope) factors have a statistically significant impact on equity returns, while those on the medium‐term (curvature) factor are less clear‐cut. Bank size plays an important role in the sense that exposures are higher for SIFIs and large banks compared to medium and small banks. Moreover, banks exhibit greater sensitivities to all risk factors during the crisis and post‐crisis periods compared to the pre‐crisis period; though these sensitivities do not differ for market‐oriented and bank‐oriented financial systems.