Determinants of stock market volatility and risk premia

Summary. We show the dynamics of diverse beliefs is the primary propagation mechanism of volatility in asset markets. Hence, we treat the characteristics of the market beliefs as a primary, primitive, explanation of market volatility. We study an economy with stock and riskless bond markets and formulate a financial equilibrium model with diverse and time varying beliefs. Agents states of belief play a key role in the market, requiring an endogenous expansion of the state space. To forecast prices agents must forecast market states of belief which are beliefs of others hence our equilibrium embodies the Keynes Beauty Contest. A market state of belief is a vector which uniquely identifies the distribution of conditional probabilities of agents. Restricting beliefs to satisfy the rationality principle of Rational Belief (see Kurz, 1994, 1997) our economy replicates well the empirical record of the (i) moments of the price/dividend ratio, risky stock return, riskless interest rate and the equity premium; (ii) Sharpe ratio and the correlation between risky returns and consumption growth; (iii) predictability of stock returns and price/dividend ratio as expressed by: (I) Variance Ratio statistic for long lags, (II) autocorrelation of these variables, and (III) mean reversion of the risky returns and the predictive power of the price/dividend ratio. Also, our model explains the presence of stochastic volatility in asset prices and returns. Two properties of beliefs drive market volatility: (i) rationalizable over confidence implying belief densities with fat tails, and (ii) rationalizable asymmetry in frequencies of bull or bear states.This research was supported by a grant of the Smith Richardson Foundation to the Stanford Institute for Economic Policy Research (SIEPR). We thank Kenneth Judd for constant advice which was crucial at several points in the development of this work. We also thank Kenneth Arrow, Min Fan, Michael Magill, Carsten Nielsen, Manuel Santos, Nicholas Yannelis, Ho-Mou Wu and Woody Brock for comments on earlier drafts. The RBE model developed in this paper and the associated programs used to compute it are available to the public on Mordecai Kurzs web page at http://www.stanford.edu/ mordecai.This revised version was published online in January 2005 with corrections to the Cover date.     

Less is more? New evidence from stock market volatility predictability

The complex nature of stock market volatility has motivated researchers to apply a variety of predictors to obtain reliable predictive information for precise forecasting. This study seeks to examine the effectiveness of the novel Global Financial Uncertainty (GFU) indices, comprising of only five sub-indices, in predicting stock market volatility using the widely used mixed-data sampling (MIDAS) model. The results demonstrate the remarkable and stable predictive power of GFU, even during crises and global financial uncertainty shocks. Specifically, the financial uncertainty index from Europe plays a significant role in our analysis. Importantly, we find that the GFU index outperforms a large number of other indicators in stock volatility forecasting. The statistical and economic significance of the predictive power of GFU is remarkable. Our study provides significant insights for market participants and policymakers that highlight the need to prioritize global financial uncertainty.     

Financial globalization and stock market risk

This paper examines stock market volatility measured by either “beta-volatility” or by the standard deviation of stock returns over 1995-2007. In our dynamic panel data framework, after controlling for size, turnover, and real output growth, we find some support to increases in financial integration reducing total stock return volatility for representative emerging markets, with almost no impact for industrial economies. Allowing for feedback effects from stock volatility to stock turnover, we obtain a richer interpretation for the broadening of investor basis hypothesis: more integrated financial markets leads to lower stock volatility, yet these are not so strong as found previously and are not accompanied by more turnover.     

Macroeconomic attention,economic policy uncertainty,and stock volatility predictability

This study adopts the newly constructed macroeconomic attention indices (MAI) and category-specific economic policy uncertainty (EPU) indices to predict stock volatility. Principal component analysis (PCA), scaled PCA (sPCA), and partial least squares (PLS) are used to extract the principal components from indicators. The results show that the combination of MAI and EPU indices can obtain additional information for predicting stock market volatility. In addition, the comprehensive index containing all indicator information (F_t^All) has the strongest short-term forecasting ability, whereas the MAI show the most substantial forecasting ability in long-term forecasting.     

Putty-clay technology and stock market volatility

Firms' first-order conditions imply that stock returns equal investment returns from the production technology. Much applied work uses the adjustment cost technology, which implies that the realized return is high when the investment-capital ratio is high. This paper derives, for an arbitrary stochastic discount factor, the investment return implied by the putty-clay technology. The combination of capital heterogeneity and irreversibility creates a novel channel for return volatility. The investment return is high when the ratio of investment to gross job creation is low. Empirically, the putty-clay feature helps account for U.S. stock market data.     

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.     

Information demand and stock market volatility

We study information demand and supply at the firm and market level using data for 30 of the largest stocks traded on NYSE and NASDAQ. Demand is approximated in a novel manner from weekly internet search volume time series drawn from the recently released Google Trends database. Our paper makes contributions in four main directions. First, although information demand and supply tend to be positively correlated, their dynamic interactions do not allow conclusive inferences about the information discovery process. Second, demand for information at the market level is significantly positively related to historical and implied measures of volatility and to trading volume, even after controlling for market return and information supply. Third, information demand increases significantly during periods of higher returns. Fourth, analysis of the expected variance risk premium confirms for the first time empirically the hypothesis that investors demand more information as their level of risk aversion increases.     

News sentiment and stock market volatility

Review of Quantitative Finance and Accounting - This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity...     

The US financial crisis,market volatility,credit risk and stock returns in the Americas

We employ the multivariate DCC-GARCH model to identify contagion from the USA to the largest developed and emerging markets in the Americas during the US financial crisis. We analyze the dynamic conditional correlations between stock market returns, changes in the general economy’s credit risk represented by the TED spread, and changes in the US market volatility represented by the CBOE Volatility Index^® (VIX). Our sample includes daily closing prices from January 1, 2002 to December 31, 2015, for the USA and stock markets in Argentina, Brazil, Canada, Chile, Colombia, Mexico, and Peru. We first identify that increases in VIX have a negative intertemporal and contemporaneous relationship with most of the stock returns, and these relationships increase significantly during the US financial crisis. We then find evidence of significant increases in contemporaneous conditional correlations between changes in the TED spread and stock returns. Increases in conditional correlations during the financial crisis are associated with financial contagion from the USA to the Americas. Our findings have policy implications and are of interest to practitioners since they illustrate that during periods of financial distress, US stock volatility and weakening credit market conditions could promote financial contagion to the Americas.

     

Non-trading,market making,and estimates of stock price volatility

We examine the effects of market making and intermittent trading on estimates of stock price volatility. When observed price changes are correctly tied to a stock's true price dynamics, it is found that non-trading per se causes a loss of efficiency but no bias in traditional volatility estimates. Non-trading induces substancial inefficiency in the extreme value estimator of volatility which it biases downward. Market making's effects add to the non-trading induced inefficiency in the traditional estimator, while information trading causes a downward bias, and liquidity trading a potentially removable upward bias, in that estimator.     

Trading volume,time-varying conditional volatility,and asymmetric volatility spillover in the Saudi stock market

Despite the well known importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important exchange market in the Middle East. We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the Saudi stock market. Overall our results support the mixture of distribution hypothesis at the firm level. We also use two different proxies for information arrival, intra-day volatility, and overnight indicators. We find that these are good proxies for information and are important as contemporaneous volume in explaining conditional volatility. We also test for the volatility spillover direction between large- and small-cap portfolios. Our results show that the spillover effect is larger and statistically significant from large to small companies.     

Belief-based momentum indicator and stock market return predictability

Li et al. (2022) propose a new momentum indicator that combines past returns and consistent belief information, and show that the indicator positively predicts cross-sectional stock returns. Based on the momentum indicator of Li et al. (2022), we further develop a conditional past return (CPR) indicator that additionally adds the direction information for the investors' consistent belief. We examine the effectiveness of CPR as a predictor for stock market returns. Our evidence shows that CPR significantly and positively predicts future one-month market returns. And CPR provides unique predictive information that is not related to the other popular predictors. The abundant out-of-sample evidence further supports CPR’s predictive ability. Additionally, we detect the asymmetric role of CPR in predicting market returns and find that much of the predictive ability of CPR is attributed to the interaction between the positive past returns and the positive consistent belief.     

Category-specific EPU indices,macroeconomic variables and stock market return predictability

This paper mainly investigates whether the category-specific EPU indices have predictability for stock market returns. Empirical results show that the content of category-specific EPU can significantly predict the stock market return, no matter the individual category-specific EPU index or the principal component of category-specific EPU indices. In addition, the information of category-specific EPU indices can also have higher economic gains than traditional macroeconomic variables, even considering the trading cost and different investor risk aversion coefficients. During different forecasting windows, multi-period forecast horizons and the COVID-19 pandemic, we find the information contained in category-specific EPU indices can have better performances than that of the macroeconomic variables. Our paper tries to provide new evidence for stock market returns based on category-specific EPU indices.     

Intraday and interday volatility in the Japanese stock market

This paper characterizes the volatility in the Japanese stock market based on a 4-year sample of 5-min Nikkei 225 returns from 1994 through 1997. The intradaily volatility exhibits a doubly U-shaped pattern associated with the opening and closing of the separate morning and afternoon trading sessions on the Tokyo Stock Exchange. This feature is consistent with market microstructure theories that emphasize the role of private and asymmetric information in the price formation process. Meanwhile, readily identifiable Japanese macroeconomic news announcements explain little of the day-to-day variation in the volatility, confirming previous findings for US equity markets. Furthermore, by appropriately filtering out the strong intradaily periodic pattern, the high-frequency returns reveal the existence of important long-memory interdaily volatility dependencies. This supports recent results stressing the importance of exploiting high-frequency intraday asset prices in the study of long-run volatility properties of asset returns.     

Forecasting stock market volatility and the informational efficiency of the DAX-index options market

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market.     

Impact of stock market trading on currency market volatility spillovers

This research aims to detect the volatility linkages among various currencies during operating and non-operating hours of three major stock markets (Tokyo, London and New York) by employing bivariate VAR-BEKK-GARCH model in selected currency pairs. In particular, the aim is to analyze whether the major stock markets have a differential impact on volatility linkages in currency markets. The results indicate that volatility linkages in intraday are far stronger then in daily results. One remarkable result is that rather than major currencies, some minor and exotic currencies play a leading role in volatility transmission during trading hours of major stock markets.     

Predictive models for disaggregate stock market volatility

This paper incorporates macroeconomic determinants into the forecasting model of industry-level stock return volatility in order to detect whether different macroeconomic factors can forecast the volatility of various industries. To explain different fluctuation characteristics among industries, we identified a set of macroeconomic determinants to examine their effects. The Clark and West (J Econom 138(1):291–311, 2007) test is employed to verify whether the new forecasting models, which vary among industries based on the in-sample results, make better predictions than the two benchmark models. Our results show that default return and default yield have a significant impact on stock return volatility.     

Information diffusion and the predictability of New Zealand stock market returns

This paper examines the impact of international predictors from liquid markets on the predictability of excess returns in the New Zealand stock market using data from May 1992 to February 2011. We find that US stock market return and VIX contribute significantly to the out‐of‐sample forecasts at short horizons even after controlling for the effect of local predictors, while the contribution by Australian stock market return is not significant. We further demonstrate that the predictability of New Zealand stock market returns using US market predictors could be explained by the information diffusion between these two countries.     

Financial crises and labor market turbulence

Financial crises in emerging markets trigger a significant reallocation of labor as exchange rate depreciations and interest rate increases cause relative prices to change drastically. Household survey data for Mexico reveal that individuals who switched industry or occupation during the 1994–1995 crisis lost about 10% of their hourly earnings on average compared to similar workers who did not move. This suggests that many workers became less productive in the process of migrating to different economic activities. These productivity losses, in turn, can explain about 40% of the observed fall in TFP in Mexico in 1995.     

The determination and international transmission of stock market volatility

This paper extends the literature on low-frequency analysis of the causes and transmission of stock market volatility. It uses end-monthly data on stock market returns, interest rates, exchange rates, inflation, and industrial production for five countries (Britain, France, Germany, Japan, and the US) from July 1973 to December 1994. Efficient portfolios of world, European, and Japanese/US equity are first constructed, the existence of multivariate cointegrating relationships between them is demonstrated, and the transmission of conditional volatility between them is described. The transmission of conditional volatility from world equity markets and national business cycle variables to national stock markets is then modeled. Among the main findings are: first, world equity market volatility is caused mostly by volatility in Japanese/US markets and transmitted to European markets, and second, changes in the volatility of inflation are associated with changes of the opposite sign in stock market volatility in all markets where a significant effect is found to exist. To the extent that the volatility of inflation is positively related to its level, this implies that low inflation tends to be associated with high stock market volatility.