The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility

Does cross-sectional dispersion in the returns of different stocks help forecast volatility of the S&P 500 index? This paper develops a model of stock returns where dispersion in returns across different stocks is modeled jointly with aggregate volatility. Although specifications that allow for feedback from cross-sectional dispersion to aggregate volatility have a better fit in sample, they prove not to be robust for purposes of out-of-sample forecasting. Using a full cross-section of stock returns jointly, however, I find that use of cross-sectional dispersion can help improve parameter estimates of a GARCH process for aggregate volatility to generate better forecasts both in sample and out of sample. Given this evidence, I conclude that cross-sectional information helps predict market volatility indirectly rather than directly entering in the data-generating process.     

Emerging markets’ spreads and global financial conditions

In this article, we analyze how much of the reduction in emerging markets’ spreads can be ascribed to specific factors—linked to the improvement in a given country's fundamentals, rather than to common factors—linked to global liquidity conditions and agents’ risk aversion. By means of factor analysis, we find that a single common factor is able to explain a large part of the co-variation in emerging market economies’ (EMEs) spreads observed in the last 4 years; in turn, this common factor can be traced back mainly to financial market volatility. Due to the particularly benign global financial conditions of recent years, spreads seem to have declined to below the levels warranted by improved fundamentals. As a consequence, EMEs do remain vulnerable to sudden shifts in financial market conditions.     

On the empirical relationship between market value and residual income in the U.K.

This paper examines the empirical cross-sectional relationship between residual income and market value for U.K. firms. It does so because of recent claims that RI is a better measure for use by firms in internal planning and control activities than, say earnings. If such is the case then, presumably, we would expect that RI has a stronger association with market value than, for example, earnings. We conclude that the relationship between RI and market value is by no means perfect. Nonetheless, RI has a stronger association with market value, in conjunction with RD expenditures and opening and closing book value, than does earnings in conjunction with RD expenditures and closing book value. As a consequence, the capital charge element of RI does appear to add explanatory power to equations involving merely earnings, RD expenditures and closing book value. Our view, therefore, would be that the evidence presented in the paper provides some support for advocates of the use of RI for planning and control.     

A model-independent measure of aggregate idiosyncratic risk

This paper introduces a model-independent measure of aggregate idiosyncratic risk, which does not require estimation of market betas or correlations and is based on the concept of gain from portfolio diversification. The statistical results and graphical analyses provide strong evidence that there are significant level and trend differences between the average idiosyncratic volatility measures of Campbell et al. [Campbell, J.Y., Lettau, M., Malkiel, B.G., and Xu, Y., 2001, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, Journal of Finance 56, 1–43.] and the new methodology. Although both approaches indicate a noticeable increase in the firm-level idiosyncratic risk, the volatility measure of CLMX is greater and has a stronger upward trend than the new idiosyncratic volatility measure. For both measures of idiosyncratic risk, the upward trend is found to be stronger for smaller, lower-priced, and younger firms. The analytical and empirical results show that the significant upward trend in the differences of the two idiosyncratic volatility measures is related to the increase in the cross-sectional dispersion of the volatility of individual stocks.     

ACCOUNTING FOR THE COST OF CAPITAL: AN EMPIRICAL EXAMINATION

One of accounting's oldest issues, accounting for interest costs, has become a subject of increased concern in recent years. This study empirically addresses the issue of whether the cost of equity capital should be charged against income. Fourteen measures of the cost of equity capital are examined. The empirical tests involve contemporaneous correlation of accounting betas for each income measure with market betas for a sample of 200 companies. The results of these association tests indicate that income measures, net of the cost of equity capital, may have greater information content than income as presently reported.     

Systematic risk and the cross section of hedge fund returns

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund-specific or residual risk components. Contrary to the popular understanding that hedge funds are market neutral, we find that systematic risk is a highly significant factor explaining the dispersion of cross-sectional returns while at the same time measures of residual risk and tail risk seem to have little explanatory power. Funds in the highest SR quintile generate 6% more average annual returns compared with funds in the lowest SR quintile. After controlling for a large set of fund characteristics and risk factors, systematic risk remains positive and highly significant, whereas the relation between residual risk and future fund returns continues to be insignificant. Hence, systematic risk is a powerful determinant of the cross-sectional differences in hedge fund returns.     

全球股票市场系统性风险传递网络研究

本文采用系统性风险度量新指标LASSO-ΔCoVaR,构建全样本时期及各极端时期全球股票市场系统性风险传递网络,考察全球股票市场系统性风险传递水平及结构特征,并着重对极端状态下的风险传递进行分析。研究发现:第一,无论风险输入水平还是风险输出水平,不同股市的动态变化趋势大体一致,但波动幅度迥然不同,且单个股市风险输出水平的波动幅度远大于风险输入水平;第二,成熟经济体经济基本面恶化往往会增强其股市的系统性风险贡献,而新兴经济体则不同;第三,法国、荷兰、中国香港、德国和英国股市的风险溢出水平较高,同其他股市间的风险传递途径较多,是系统性风险传递网络中的核心节点;第四,我国股市与全球股市间的风险关联较弱,但我国股市潜在风险来源面广,同区域股市及金砖国家股市在我国股市与全球股市间的风险传递发挥重要作用。     

Association between environmental factors and equity market performance: evidence from a nonparametric frontier method

In this cross-sectional study, equity market performance is assessed in a multidimensional risk-adjusted return framework using a nonparametric procedure known as data envelopment analysis. Employing a censored regression procedure, the association between equity market performance and a set of variables that proxy market characteristics and the political and business environment in which the market operates is investigated. The paper contributes to the literature on the association between environmental factors and equity market performance by using a methodology not previously employed in such investigations. The results reveal that equity market performance may be positively related to the size of the market and friendliness of the business environment. Friendliness of the business environment is an objective measure of regulations conducive to business and their enforcement.     

Investors’ risk perceptions in the US and global stock market integration

Investors’ risk perceptions have significant implications for international stock markets. This paper estimates the time-varying impact of the VIX index – a widely used measure of investors risk perceptions – on the dynamic correlation across international stock markets. Results show that risk perceptions significantly impact the dynamic correlation between the U.S. market and the leading stock markets of the world. Further, in 17 out of 20 international stock markets, risk perceptions Granger cause dynamic correlations. The impact of VIX is positive on the correlation of the U.S. market with European and Latin American markets. In contrast, the relationship of the U.S. market with all the Asian markets weakens (strengthens) as the VIX index rises (falls). In all cases, the time-varying parameter model shows that the impact of VIX on these correlations varies significantly across time.     

Improving the Precision of Analysts' Earnings Forecasts by Adjusting for Predictable Bias

This research demonstrates that publicly-available information can be used to develop estimates of analysts' optimistic bias in earnings forecasts. These bias estimates can be used to produce more accurate forecasts, resulting in significant reductions of both cross-sectional mean forecast error and error variance. When bias estimates are based on past observations of forecast error alone, however, reductions in mean forecast error are smaller, and forecast precision is unimproved. Further tests provide evidence of a significant association between returns and the bias predictable from contemporaneously-available information, suggesting that predictable bias is only partially discounted by market participants. This study has significant implications for researchers and investors. The pricing of predictable bias in analysts' forecasts may add error toinferences which are based on the association between returns and analyst forecast errors, and knowledge of the market's partial discounting of predictable bias may help investors to make more efficient resource allocations.     

Stock market volatility: friend or foe?

Although a good deal of research effort has been allocated to understanding the time-series volatility of stock returns – as both market (or systematic) volatility and idiosyncratic (or non-systematic) volatility – the relationship of such volatility with cross-sectional volatility or dispersion of outcomes is sparse. Nevertheless, the quest to understand one must involve the quest to understand the other. In this paper, we investigate the dispersion of returns in relation to inter-temporal volatility, as well as the dynamic of dispersion of returns in generating a portfolio’s return outcome. We find that the level of such dispersion is highly significant for portfolio performance and the notion of risk.     

A new measure of cross-sectional risk and its empirical implications for portfolio risk management

Litterman et al. [Litterman, R., Scheinkman, J., Weiss, L., 1991. Volatility and the yield curve. Journal of Fixed Income 1 (June), 49–53] and Engle and Ng [Engle, R.F., Ng, V.K., 1993. Time-varying volatility and the dynamic behavior of the term structure. Journal of Money, Credit and Banking 25(3), 336–349] provide empirical evidence of a relation between yield curve shape and volatility. This study offers theoretical support for that finding in the general context of cross-sectional time series. We introduce a new risk measure quantifying the link between cross-sectional shape and market risk. A simple econometric procedure allows us to represent the risk experienced by cross-sections over a time period in terms of independent factors reproducing possible cross-sectional deformations. We compare our risk measure to the traditional cross-yield covariance according to their relative performance. Empirical investigation in the US interest rate market shows that (1) cross-shape risk factors outperform cross-yield risk factors (i.e., yield curve level, slope, and convexity) in explaining the market risk of yield curve dynamics; (2) hedging multiple liabilities against cross-shape risk delivers superior trading strategies compared to those stemming from cross-yield risk management.     

Emerging market bond returns—An investor perspective

The novel features of this study consist in applying a conventional multifactor global market model to emerging market sovereign bond index rates of return that are denominated in US dollars and subsequently relating the unexplained residuals from the market model's estimates of each country's total bond index return to country-specific factors. They include political and financial risks as well as other presumed determinants of bond index rates of return. The results of our study confirm that sovereign countries’ bond index rates of return that include interest payments and capital gains/losses may be explained in terms of conventional bond pricing models by combining global market factors with local risk and other country-specific influences.     

Beta dispersion and market timing

The beta dispersion, which is the spread of betas on a stock market, can be interpreted as a measure of market vulnerability. This study examines the economic idea of the beta dispersion and its application as a market return predictor. Based on the empirical beta dispersion observed in the US equity market, the study develops measures to predict future market returns. These dispersion measures have substantial predictive power for future market movements. Moreover, I show that the information content of beta dispersion can be successfully exploited by market timing strategies with the help of distributional regressions. The innovative application of this novel approach of modeling the relationship between multiple variables appears to be quite useful for timing strategies.     

Bank systemic risk and macroeconomic shocks: Canadian and U.S. evidence

This paper investigates how banks, as a group, react to macroeconomic risk and uncertainty; more specifically, it examines the relationship between bank systemic risk and changes and disruptions in economic conditions. Adopting the methodology of Beaudry et al. (2001), we introduce a new estimation procedure based on EGARCH to refine the framework developed by Baum et al., 2002, Baum et al., 2004, Baum et al., 2009 and Quagliariello, 2007, Quagliariello, 2009, and we analyze the relationship in the current industry context—i.e., in the context of market-based banking. Our results confirm that banks tend to behave more homogeneously vis-à-vis macroeconomic uncertainty. In particular, we find that both the cross-sectional dispersion of loans-to-assets and the cross-sectional dispersion of non-interest income share shrink during slow growth episodes, and particularly during financial crises, when the resilience of the banking system is at its lowest. More importantly, our main findings indicate that the cross-sectional dispersion of loans-to-assets has increased in the last decade, whereas the cross-sectional dispersion of non-interest income share appears to be more volatile and sensitive to macroeconomic shocks.     

Asymmetric multifractal features of the price–volume correlation in China’s gold futures market based on MF-ADCCA

In recent years, fractal theory has become a recognized research direction for explaining various complex phenomena that are difficult to constrain in the conventional efficient market hypothesis for financial markets. Moreover, because the gold futures prices are crucial to the futures market, research on the relationship between quantity and price is important for understanding market fluctuations. Therefore, this paper conducts an empirical analysis of the multifractal features and asymmetry in the price–volume correlation of China’s gold futures market based on the multifractal asymmetric detrended cross-correlation analysis method¹ . Results show that the cross-correlation between market price and volume is asymmetric and multifractal and that multifractal features are stronger when the price increases compared with when it declines. Moreover, the multifractal features vary over time. These findings indicate that the risk of China’s gold futures market will change with the price trend over time.     

Forecasting correlation among equity mutual funds

Investors often buy multiple funds that are actively managed, specializing within narrowly defined market segments. To successfully implement a strategy of diversification investors must obtain accurate estimates of correlation among mutual fund returns. This paper forecasts mutual fund correlations using eight models that are broadly classified into historical, mean and index. Results indicate that estimate of future correlations from the Multi-Style Index, Dynamic and Fama–French 3-Factor models have the lowest prediction errors. Moreover, the relative ranks of Multi-Style Index and Fama–French 3-Factor models have lower dispersion across different forecasting time periods and in sub-samples of funds belonging to similar or different ‘style’ categories.     

Does one size fit all? Determinants of insurer capital structure around the globe

As financial markets become more global, the question arises whether any country specific considerations are still relevant for insurance companies’ capital structure. This research examines this question with firm-level data across a broad range of countries including those in developing markets. What we find is that the optimal capital structure of insurance companies is not homogeneous across countries. We find that country-level factors explain a substantial fraction of the cross-sectional variation in insurance companies’ capitalization levels. Our results add to the current policy discussion on global regulatory capital requirements. If insurer capital structure is not homogeneous across countries, a global capital standard – if desired – should take differences in the institutional environments across countries into account to avoid market distortions.     

Conservatism measures that control for the effects of economic rents on stock returns

Recent studies show that regression-based estimates of accounting conservatism reflect both differences in the asymmetric recognition of bad news and differences in asset composition. In particular, a firm’s market value and returns reflect both assets-in-place and expected future rents, while book values tend to reflect only assets-in-place. We propose two tests that remove the effect of asset composition on cross-sectional comparisons of accounting conservatism. First, a test based on a ratio of regression coefficients allows for valid cross-sectional comparisons of conservatism relative to overall news recognition. Second, in some cases, researchers can separately identify and make cross-sectional comparisons of the fraction of good news recognized and the fraction of bad news recognized. The estimates in this second scenario use a regression of earnings on returns interacted with a book-to-market ratio. We validate our model by deriving and testing several predictions based on it.     

What's your real cost of capital?

In valuing any investment project or corporate acquisition, executives must decide what discount rate to use in their estimates of future cash flows. The traditional approach is to apply the capital asset pricing model (CAPM), which has remained fundamentally unchanged for 40 years. But the formula--in particular, its beta element--has long been a source of frustration. In fact, corporate executives and investment bankers routinely fudge their CAPM estimates, say the authors, because experience and intuition tell them the model produces inappropriate discount rates. CAPM has three main problems: First, beta is a measure of both a stock's correlation and its volatility; second, beta is based on historical data; and third, CAPM rates don't take into account the term of the investment. These factors together result in discount rates that defy common sense. As an alternative to CAPM and its beta element, the authors developed a forward-looking approach to calculating a company's cost of capital, the market-derived capital pricing model (MCPM). It does not incorporate any measure of historical stock-to-market correlation, relying instead on estimates of future volatility derived from the options market. This is helpful since investor expectations from the options market are built into a company's current stock price. Using GE as an example, the authors give step-by-step instructions for how to calculate discount rates with MCPM. They also offer evidence from a range of industries to show that MCPM's discount rates are more realistic--especially from the corporate investor's perspective--than are CAPM's.