Idiosyncratic risk and the cross-section of expected stock returns

Theories such as Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predict a positive relation between idiosyncratic risk and expected return when investors do not diversify their portfolio. Ang, Hodrick, Xing, and Zhang [2006. The cross-section of volatility and expected returns. Journal of Finance 61, 259–299], however, find that monthly stock returns are negatively related to the one-month lagged idiosyncratic volatilities. I show that idiosyncratic volatilities are time-varying and thus, their findings should not be used to imply the relation between idiosyncratic risk and expected return. Using the exponential GARCH models to estimate expected idiosyncratic volatilities, I find a significantly positive relation between the estimated conditional idiosyncratic volatilities and expected returns. Further evidence suggests that Ang et al.'s findings are largely explained by the return reversal of a subset of small stocks with high idiosyncratic volatilities.     

The macroeconomic determinants of technology stock price volatility

Stock prices reflect the value of anticipated future profits of companies. Since business cycle conditions impact the future profitability of firms, expectations about the business cycle will affect the current value of firms. This paper uses daily and monthly data from July 1986 to December 2000 to investigate the macroeconomic determinants of US technology stock price conditional volatility. Technology share prices are measured using the Pacific Stock Exchange Technology 100 Index. One of the novel features of this paper is to incorporate a link between technology stock price movements and oil price movements. The empirical results indicate that the conditional volatilities of oil prices, the term premium, and the consumer price index each have a significant impact on the conditional volatility of technology stock prices. Conditional volatilities calculated using daily stock return data display more persistence than conditional volatilities calculated using monthly data. These results further our understanding of the interaction between oil prices and technology share prices and should be of use to investors, hedgers, managers, and policymakers.     

Capital Asset Pricing Model and Stochastic Volatility: A Case Study of India

The existing literature demonstrates that under a general equilibrium model, the performance of the Capital Asset Pricing Model (CAPM) can be improved significantly by using conditional consumption and market return volatilities as factors. This article tests the validity of these factors explaining stock return differences using a less developed country (India) as a case study. While the earlier studies used panel data to test CAPM, we use portfolios sorted by size and book-to-market equity (BE/ME) ratio. We found that conditional volatility has a limited effect on firms with large capitalization but a significant impact on small-growth and small-value firms.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.     

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.     

Multiple Regimes and Volatility Transmission in Securitized Real Estate Markets

We examine the dynamics and transmission of conditional volatilities with multiple structural changes in return volatility using Bai and Perron (2003)’s methodology, across five major securitized real estate markets as well as employing a multivariate regime-dependent asymmetric dynamic covariance methodology (MRDADC) that allows the conditional matrix to be both time- and state-varying. Our results imply that a multiple-regime time varying asymmetric variance and covariance approach is important in modeling real estate securities valuation and selection and portfolio optimization, and is consistent with popular beliefs that market volatility changes over time. Our MRDADC models detect the presence of significant mean-volatility linkages across the five major securitized real estate markets under different volatility regimes and would have implications for global investor in terms of estimating a dynamic risk-minimizing hedge ratio in international portfolio management.     

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.     

The impacts of climate policy uncertainty on stock markets: Comparison between China and the US

The interplay between climate policy uncertainty and stock market performance has emerged as a pressing research question in light of the challenges posed by climate change to financial markets. This paper measures China's daily and monthly climate policy uncertainty (CPU) from Jan 2000 to Mar 2022 based on Chinese news data for the first time. Then, the nonlinear and lag impacts of the US CPU and China's CPU on the return, volatility, correlation and tail dependence of China's and US stock markets are investigated and compared by adopting copula function and the distribution lag nonlinear model (DLNM). The data of stock markets includes the Shanghai Composite Index (SSCI) and NASDAQ from Jan 2000 to Mar 2022 from the Choice database, and the Shenzhen Composite Index (SCI) and S&P 500 are used for the robustness test. The empirical results indicate that (1) the growth trend of China’s CPU index is similar to that of the US. However, there are significant differences between the impacts of these two CPUs on stock markets. (2) For China, high CPU decreases current stock market return and increases volatility but decreases it in the future. It could also increase the upper tail dependence between China’s and the US stock markets’ volatilities in current period. (3) For the US, CPU decreases stock market return in the short term but increases it in the long term. High CPU increases volatility in short term, decreases volatility in 5 months and increases it again after 6 months. Both low and high CPU could increase the correlation between China's and US stock markets' volatilities.     

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.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

The spillover effects of target interest rate news from the U.S. Fed and the European Central Bank on the Asia-Pacific stock markets

This paper provides comprehensive evidence on the spillover effects of the U.S. Fed's and the European Central Bank (ECB)’s target interest rate news on the market returns and return volatilities of 12 stock markets in the Asia-Pacific over the period 1999–2006. The news spillover effects on the returns are generally consistent with the literature where a majority of stock markets shows significant negative returns in response to unexpected rate rises. While the results of the speed of adjustment for the Fed's news are mixed across the markets, the ECB news was absorbed slowly, in general. The return volatilities were higher in response to the interest rate news from both sources. In addition, both the Fed and the ECB news elicited tardy or persisting volatility responses. These findings have important implications for all levels of market participants in the Asia-Pacific stock markets.     

Do Global Risk Perceptions Play a Role in Emerging Market Equity Return Volatilities?

This paper investigates whether global risk perceptions lead emerging market return volatilities. In so doing, we analyzed the period of interest in three parts to determine the effects of the changes in global risk perceptions on the volatility of emerging markets. We uncovered volatility spillover from risk perceptions to the MXEF returns before the crisis. Our results show that all the effects on emerging market volatilities are severed in 2008, during which MXEF follows a downward trend. However, we observe that volatility transmission emerges during the recovery period of MXEF again. Hence, risk perceptions should be considered while analyzing emerging markets.     

The empirical risk–return relation: A factor analysis approach

Existing empirical literature on the risk–return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors—termed “volatility,” “risk premium,” and “real” factors—contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16–20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk–return correlation.     

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.     

A Likelihood Ratio Test of Price Volatilities: Comparing Stock Index Spot and Futures

This paper uses three methods to estimate the price volatility of two stock market indexes and their corresponding futures contracts. The classic variance measure of volatility is supplemented with two newer measures, derived from the Garman-Klass and Ball-Torous estimators. A likelihood ratio test is used to compare the classic variance measure of price volatilities of two stock market indexes and their corresponding futures contracts during the bull market of the 1980s. The stock market volatilities of the Standard & Poor's 500 (S&P 500) and New York Stock Exchange (NYSE) indexes were found to be significantly lower than their respective futures price volatilities. Since information may flow faster in the futures markets than in the corresponding stock market, our results support Ross's information-volatility hypothesis. It was also noted that the NYSE spot volatility was lower than the S&P 500 spot volatility. If the rate of information flow and firm size are positively related, then the lower NYSE spot volatility is explained by the size effect. The futures price volatilities for the two indexes were insignificantly different from each other. With stock index spot-futures price correlations approaching unity, one implication of our results for index futures activity is that smaller positions in futures contracts may suffice to achieve hedging or arbitrage goals.     

Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries

This paper investigates the time-varying conditional correlation between oil price and stock market volatility for six major oil-importing and oil-exporting countries. The period of the study runs from January 2000 until December 2014 and a Diag-BEKK model is employed. Our findings report the following regularities. (i) The correlation between the oil and stock market volatilities changes over time fluctuating at both positive and negative values. (ii). Heterogeneous patterns in the time-varying correlations are evident between the oil-importing and oil-exporting countries. (iii) Correlations are responsive to major economic and geopolitical events, such as the early-2000 recession, the 9/11 terrorist attacks and the global financial crisis of 2007–2009. These findings are important for risk management practices, derivative pricing and portfolio rebalancing.     

New evidence on the implied-realized volatility relation

We consider the relation between the volatility implied in an option's price and the subsequently realized volatility. Earlier studies on stock index options have found biases and inefficiencies in implied volatility as a forecast of future volatility. More recently, Christensen and Prabhala find that implied volatility in at-the-money one-month OEX call options on the S&P 100 index in fact is an unbiased and efficient forecast of ex-post realized index volatility after the 1987 stock market crash. In this paper, the robustness of the unbiasedness and efficiency result is extended to a more recent period covering April 1993 to February 1997. As a new contribution, implied volatility is constructed as a trade weighted average of implied volatilities from both in-the-money and out-of-the-money options and both puts and calls. We run a horse race between implied call, implied put, and historical return volatility. Several robustness checks, including a new simultaneous equation approach, underscore our conclusion, that implied volatility is an efficient forecast of realized return volatility.     

Volatility Spillovers of Stock Markets between China and the Countries along the Belt and Road

ABSTRACT

This article intensively studies the stock market volatility spillover effects between China and the countries along the Belt and Road (B&R) based on the covered selection of Morgan Stanley Capital International Inc (MSCI) index by using multiplicative error model to measure stock market volatility with daily price range. The results show that during the whole sample period, there are bilateral linkages of volatility between the stock markets of China and all of B&R countries. Most of B&R and China’s markets are sensitive to positive news but the asymmetry is trivial. Financial crisis intensified the volatility spillover effects across countries while the markets’ volatilities tend to be influenced by the negative shocks from foreign markets. The B&R markets as risk absorbers exhibit significant sensitivities to the negative news from Chinese market during the crisis period.     

MEAN AND VOLATILITY SPILLOVERS ACROSS MAJOR NATIONAL STOCK MARKETS: FURTHER EMPIRICAL EVIDENCE

This paper provides additional insight into the nature and degree of interdependence of stock markets of the United States, Japan, the United Kingdom, Canada, and Germany, and it reports the extent to which volatility in these markets influences expected returns. The analysis uses the multivariate GARCH-M model. Although they are considered weak, statistically significant mean spillovers radiate from stock markets of the U.S. to the U.K., Canada, and Germany, and then from the stock markets of Japan to Germany. No relation is found between conditional market volatility and expected returns. Strong time-varying conditional volatility exists in the return series of all markets. The own-volatility spillovers in the U.K. and Canadian markets are insignificant, supporting the view that conditional volatility of returns in these markets is “imported” from abroad, specifically from the U.S. Significant volatility spillovers radiate from the U.S. stock market to all four stock markets, from the U.K. stock market to the Canadian stock market, and from the German stock market to the Japanese stock market. The results are robust and no changes occur in the correlation structure of returns over time.     

Margin regulation and volatility

An infinite-horizon asset-pricing model with heterogeneous agents and collateral constraints can explain why adjustments in stock market margins under US Regulation T had an economically insignificant impact on market volatility. In the model, raising the margin requirement for one asset class may barely affect its volatility if investors have access to another, unregulated class of collateralizable assets. Through spillovers, however, the volatility of the other asset class may substantially decrease. A very strong dampening effect on all assets׳ return volatilities can be achieved by a countercyclical regulation of all markets.