Macroeconomic risks and REITs returns: A comparative analysis

We study the relationship between the excess returns of REITs and volatilities of macroeconomic factors in developing markets (Bulgaria and South Africa) and a ‘benchmark’ developed market (USA). As expected, our results generally indicate that conditional volatilities of macroeconomic risks, extracted through the GARCH (1,1) process, are time-varying. GARCH coefficients are largely significant for excess returns and retained principal components implying conditional time-varying volatility. We use the GMM to examine the linkage between volatilities of macroeconomic variables and REITs returns. The general result here is that macroeconomic risk cannot explain excess returns on REITs. However, we document a positive relationship between variability in REITs returns and the real economy for the US. US REITs portfolio managers and investors should be wary of fluctuations in these variables as they may accentuate volatility in REITs returns.     

The determinants of the model-free positive and negative volatilities

In this paper we analyze the role of macroeconomic and financial determinants in explaining stock market volatilities in the U.S. market. Both implied and realized volatility are computed model-free and decomposed into positive and negative components, thereby allowing us to compute directional volatility risk premia. We capture the behaviour of each component of implied volatility and risk premium in relation to their different determinants. The negative implied volatility appears to be linked more towards financial conditions variables such as uncertainty and geopolitical risk indexes, whereas positive implied volatility is driven more by macro variables such as inflation and GDP. There is a clear shift in importance from macro towards financial determinants moving from the pre towards the post financial crisis. A mixed frequency Granger causality approach uncovers causality relationships between volatilities and risk premia and macro variables and vice versa, a finding which is not detected with a conventional low frequency VAR model.     

Intraday Seasonalities and Macroeconomic News Announcements

Using a data set consisting of more than five years of 5‐minute intraday stock index returns for major European stock indices and US macroeconomic surprises, conditional means and volatility behaviour in European markets were investigated. The findings suggest that the opening of the US stock market significantly raises the level of volatility in Europe, all markets responding in an identical fashion. Furthermore, US macroeconomic surprises exert an immediate and major impact on both the European stock markets’ intraday returns and volatilities. Thus, high frequency data appear to be critical for the identification of news impacting the markets.     

Asymmetric Correlation and Volatility Dynamics among Stock, Bond, and Securitized Real Estate Markets

We apply a multivariate asymmetric generalized dynamic conditional correlation GARCH model to daily index returns of S&P500, US corporate bonds, and their real estate counterparts (REITs and CMBS) from 1999 to 2008. We document, for the first time, evidence for asymmetric volatilities and correlations in CMBS and REITs. Due to their high levels of leverage, REIT returns exhibit stronger asymmetric volatilities. Also, both REIT and stock returns show strong evidence of asymmetries in their conditional correlation, suggesting reduced hedging potential of REITs against the stock market downturn during the sample period. There is also evidence that corporate bonds and CMBS may provide diversification benefits for stocks and REITs. Furthermore, we demonstrate that default spread and stock market volatility play a significant role in driving dynamics of these conditional correlations and that there is a significant structural break in the correlations caused by the recent financial crisis.     

The degree of financial liberalization and aggregated stock-return volatility in emerging markets

In this study, we address whether the degree of financial liberalization affects the aggregated total volatility of stock returns by considering the time-varying nature of financial liberalization. We also explore channels through which the degree of financial liberalization impacts aggregated total volatility. We document a negative relation to the degree of financial liberalization after controlling for size, liquidity, country, and crisis effects, especially for small and medium-sized markets. Moreover, the degree of financial liberalization transmits its negative impact on aggregated total volatility through aggregated idiosyncratic and local volatilities. Overall, our results provide evidence in favor of the view that the broadening of the investor base due to the increasing degree of financial liberalization causes a reduction in the total volatility of stock returns.     

Simultaneous monetary policy announcements and international stock markets response: An intraday analysis

This paper investigates the return and volatility response of major European and US equity indices to monetary policy surprises by utilizing extensive intraday data on 5-min price quotes along with a comprehensive dataset on monetary policy decisions and macroeconomic news announcements. The results indicate that the monetary policy decisions generally exert immediate and significant influence on stock index returns and volatilities in both European and the US markets. The findings also show that press conferences held by the European Central Bank (ECB) that follow monetary policy decisions on the same day have a clear impact on European index return volatilities. This implies that they convey additional important information to market participants. Overall, our analysis suggests that the use of high frequency data is critical to separate the effect of monetary policy actions from those of macroeconomic news announcements on stock index returns and volatilities.     

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.     

The intended and unintended consequences of financial-market regulations: A general-equilibrium analysis

In a production economy with trade in financial markets motivated by the desire to share labor-income risk and to speculate, we show that speculation increases volatility of asset returns and investment growth, increases the equity risk premium, and reduces welfare. Regulatory measures, such as constraints on stock positions, borrowing constraints, and the Tobin tax have similar effects on financial and macroeconomic variables. However, borrowing constraints and the Tobin tax are more successful than constraints on stock positions at improving welfare because they substantially reduce speculative trading without impairing excessively risk-sharing trades.     

Modelling the Implied Volatility of Options on Long Gilt Futures

This paper investigates the properties of implied volatility series calculated from options on Treasury bond futures, traded on LIFFE. We demonstrate that the use of near-maturity at the money options to calculate implied volatilities causes less mis-pricing and is therefore superior to, a weighted average measure encompassing all relevant options. We demonstrate that, whilst a set of macroeconomic variables has some predictive power for implied volatilities, we are not able to earn excess returns by trading on the basis of these predictions once we allow for typical investor transactions costs.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

Is volatility clustering of asset returns asymmetric?

Volatility clustering is a well-known stylized feature of financial asset returns. This paper investigates asymmetric pattern in volatility clustering by employing a univariate copula approach of Chen and Fan (2006). Using daily realized kernel volatilities constructed from high frequency data from stock and foreign exchange markets, we find evidence that volatility clustering is highly nonlinear and strongly asymmetric in that clusters of high volatility occur more often than clusters of low volatility. To the best of our knowledge, this paper is the first one to address and uncover this phenomenon. In particular, the asymmetry in volatility clustering is found to be more pronounced in the stock markets than in the foreign exchange markets. Further, the volatility clusters are shown to remain persistent for over a month and asymmetric across different time periods. Our findings have important implications for risk management. A simulation study indicates that models which accommodate asymmetric volatility clustering can significantly improve the out-of-sample forecasts of Value-at-Risk.     

China,Japan and the US Stock Markets and the Global Financial Crisis

In this paper, while focusing on the impact that the global financial crisis had on the stock markets of China, Japan, and the United States, the stock-price volatilities and linkage between these three countries are analyzed. In addition, the relationships between macroeconomic variables (real-economy variables and monetary-policy variables) and stock price volatility in each country are investigated. The estimation results of the EGARCH model revealed that although China’s stock price volatility was far greater than those of Japanese and US stock prices, China was less affected by the global financial crisis in 2007 than Japan and the United States. For China, stock price volatility was greater in the early 1990s, shortly after the stock market had been established, than in 2007 when the global financial crisis occurred. Furthermore, it has been revealed that the linkage of Chinese, Japanese, and US stock prices has increased since the global financial crisis. Moreover, Granger causality testing revealed China’s real-economy variables and monetary-policy variables do not affect China’s stock price volatility.     

Determinants of equity return correlations: a case study of the Amman Stock Exchange

This paper seeks to explain time-varying correlations among equity returns. The literature has shown that fundamental and economic factors can explain stock returns or the volatility of markets. Here, panel data analysis is employed to examine whether these factors can also explain the comovement of stock returns. Time-varying correlations among sectoral indexes are estimated using a restricted multivariate threshold GARCH model with dynamic conditional correlation controlling for the asymmetric effects of news and the influence of financial crises. The empirical results from this panel data analysis show that equity return correlations can be explained not only by macroeconomic variables but also by fundamentals within an industry.     

Noise trading and stock market volatility

We investigate the relative effects of fundamental and noise trading on the formation of conditional volatility. We find significant positive (negative) effects of investor sentiments on stock returns (volatilities) for both individual and institutional investors. There are greater positive effects of rational sentiments on stock returns than irrational sentiments. Conversely, there are significant (insignificant) negative effects of irrational (rational) sentiments on volatility. Also, we find asymmetric (symmetric) spillover effects of irrational (rational) bullish and bearish sentiments on the stock market. Evidence in favor of irrational sentiments is consistent with the view that investor error is a significant determinant of stock volatilities.     

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting; the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias; and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.     

Time-varying stock returns and labor income risks in the US and UK

I propose and estimate conditional asset pricing models where the risk premiums of the markets are related to the conditional covariance of the markets with labor income growth within and across countries and the volatility of the markets are related to the shocks and interactions of stock returns and labor income growth. I document that the risk premiums for the US and UK stock markets are more related to the conditional covariance of returns with the labor income growth within countries than across countries. I also find significant interactions of volatilities between stock returns and labor income within countries but not across countries. The results are consistent with the hypothesis that prices of domestic stocks are determined to a greater extent by stochastic discount factors of domestic investors than foreign investors and vice versa.     

Mean-reversion properties of implied volatilities

In this paper, we present a new stylized fact for options whose underlying asset is a stock index. Extracting implied volatility time series from call and put options on the Deutscher Aktien index (DAX) and financial times stock exchange index (FTSE), we show that the persistence of these volatilities depends on the moneyness of the options used for its computation. Using a functional autoregressive model, we show that this effect is statistically significant. Surprisingly, we show that the diffusion-based stochastic volatility models are not consistent with this stylized fact. Finally, we argue that adding jumps to a diffusion-based volatility model help recovering this volatility pattern. This suggests that the persistence of implied volatilities can be related to the tails of the underlying volatility process: this corroborates the intuition that the liquidity of the options across moneynesses introduces an additional risk factor to the one usually considered.     

MACROECONOMIC VARIABLES AND SEASONAL MEAN REVERSION IN STOCK RETURNS

In this paper I investigate whether seasonal mean reversion in stock portfolio returns is related to common macroeconomic risk factors. I decompose excess returns into explained and unexplained returns using a multifactor pricing model. The explained excess returns exhibit January mean reversion; the unexplained excess returns do not. The mean reversion can be attributed to the components of return related to unexpected inflation, bond default premium, and market risk. The results do not depend on the time-series properties of the portfolio betas. Bond default premia and excess market returns are mean reverting in January.     

S&P 500 Cash Stock Price Volatilities

S&P 500 stock return volatilities are compared to the volatilities of a matched set of stocks, after controlling for cross-sectional differences in firm attributes known to affect volatility. No significant difference in volatility is observed between 1975 and 1983—before the start of trade in index futures and index options. Since then, S&P 500 stocks have been relatively more volatile. The difference is statistically, but not economically, significant. The relative increase occurs primarily in daily returns and only to a lesser extent in longer interval returns. Other factors besides the start of derivative trade could be responsible for the small increase in volatility.     

Corporate Bond Returns and Volatility

Recent literature emphasizes the relation of stock volatility to corporate bond yields. We demonstrate that during 1996–2005 corporate bond excess return volatility is directly related to contemporaneous corporate bond excess returns. In fact, the decompositions of aggregate bond volatility have a higher contemporaneous correlation with bond yields in comparison to idiosyncratic stock risk. Additionally, bond volatility and idiosyncratic risk are significant predictors of corporate three‐month and six‐month ahead bond excess returns. We also find that corporate bond volatility contains both slow moving and time‐varying components.