Effects of Passive Intensity on Aggregate Price Dynamics

We find that passive intensity (PI), measured by the passive‐linked share of total stock market trading volume, is strongly related to the overall pattern of stock price movements. A one‐standard‐deviation increase in PI is associated with an 8% higher price synchronicity. We further investigate the channels through which this relation is established by separately analyzing its impact on aggregate systematic and idiosyncratic volatility of stock returns. PI has a positive effect on systematic volatility and a negative impact on firm‐specific volatility. Consistent with the effect of passive trading on price dynamics, we find evidence that PI is negatively associated with mutual funds alpha dissimilarity. After controlling for market and idiosyncratic volatility, a one‐standard‐deviation increase in PI corresponds to a 0.20% decrease in fund dissimilarity. Our findings are robust after controlling for various macro and corporate factors known to affect systematic or firm‐specific volatility.     

Volatility and covariation of financial assets: A high-frequency analysis

Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance–covariance matrix of n assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true efficient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructure noise affects portfolio decisions and calculations of the equity beta in a CAPM setting.     

IDENTIFYING REGIME CHANGES IN MARKET VOLATILITY

A casual inspection of a graph of volatility indexes over time indicates that volatility has undergone infrequent, but significant, shifts in its average level. The purpose of this article is to test for multiple structural breaks in the mean level of market volatility measured by the VIX and VXO, and to identify statistically the dates of these mean shifts. We find evidence of three distinct periods: pre‐1992, 1992–1997, and post‐1997. We find that the mean volatility, as well as its standard deviation, was lowest during 1992–1997. Our findings provide statistical evidence consistent with popular beliefs that market volatility changes over time.     

Exploring the predictability of range‐based volatility estimators using recurrent neural networks

We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only.     

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.     

ANALYZING STOCK MARKET VOLATILITY USING EXTREME‐DAY MEASURES

We develop a simple measure of volatility based on extreme‐day returns and apply it to market returns from 1885 to 2002. Because returns are not normally distributed, the extreme‐day measure, which is distribution free, might provide a better measure of stock market risk than the traditional standard deviation. The extreme‐day measure more accurately explains investor behavior relative to standard deviation as shown by equity fund flows, and we find evidence that large negative changes appear to influence investor behavior more than large positive changes.     

Investor Attention and Time‐varying Comovements

This paper analyses the effect of an increase in market‐wide uncertainty on information flow and asset price comovements. We use the daily realised volatility of the 30‐year treasury bond futures to assess macroeconomic shocks that affect market‐wide uncertainty. We use the ratio of a stock's idiosyncratic realised volatility with respect to the S&P500 futures relative to its total realised volatility to capture the asset price comovement with the market. We find that market volatility and the comovement of individual stocks with the market increase contemporaneously with the arrival of market‐wide macroeconomic shocks, but decrease significantly in the following five trading days. This pattern supports the hypothesis that investors shift their (limited) attention to processing market‐level information following an increase in market‐wide uncertainty and then subsequently divert their attention back to asset‐specific information.     

Range-Based Estimation of Stochastic Volatility Models

We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range-based Gaussian quasi-maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor.     

When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions

We examine high-frequency market reactions to an intraday stock-specific news flow. Using unique pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the indicated relevance, novelty and direction of company-specific news. Employing a high-frequency VAR model based on 20 s data of a cross-section of stocks traded at the London Stock Exchange we find distinct responses in returns, volatility, trading volumes and bid-ask spreads due to news arrivals. We show that a classification of news according to indicated relevance is crucial to filter out noise and to identify significant effects. Moreover, sentiment indicators have predictability for future price trends though the profitability of news-implied trading is deteriorated by increased bid-ask spreads.     

ESTIMATING EXPECTED EXCESS RETURNS USING HISTORICAL AND OPTION‐IMPLIED VOLATILITY

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward‐to‐risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option‐implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option‐implied volatility risk measure yields a positive, but statistically insignificant, risk‐return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal‐to‐noise ratio in the latter subperiod.     

WHEN‐ISSUED SHARES,SMALL TRADES,AND THE VARIANCE OF RETURNS AROUND STOCK SPLITS

The increases in volatility after stock splits have long puzzled researchers. The usual suspects of discreteness and bid‐ask spread do not provide a complete explanation. We provide new clues to solve this mystery by examining the trading of when‐issued shares that are available before the split. When‐issued trading permits noise traders to compete with a more homogenous set of traders, decreasing the volatility of the stock before the split. Following the split, these noise traders reunite in one market and volatility increases. Thus, the higher volatility after the ex date of a stock split is a function of the introduction of when‐issued trading, the new lower price level after the split date, and the increased activity of small‐volume traders around a stock split.     

Maximum likelihood estimation of stochastic volatility models

We develop and implement a method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by proxies based on the implied volatility of a short-dated at-the-money option. The approximation results in a small loss of accuracy relative to the standard errors due to sampling noise. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine Heston model and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.     

Investment Dynamics and Earnings‐Return Properties: A Structural Approach

We propose the standard neoclassical model of investment under uncertainty with short‐run adjustment frictions as a benchmark for earnings‐return patterns absent accounting influences. We show that our proposed benchmark generates a wide range of earnings‐return patterns documented in accounting research. Notably, our model generates a concave earnings‐return relation, similar to that of Basu [1997], and predicts that the earnings‐return concavity increases with the volatility of firms’ underlying shock processes and decreases with the level of firms’ investments. We find strong empirical support for these predictions. Overall, our evidence suggests that our proposed benchmark is useful for understanding the joint dynamics of variables of interest to accounting research (e.g., earnings, returns, investment, market‐to‐book) absent accounting influences, a necessary precondition for inferring the effects of accounting from these dynamics.     

PRESIDENTIAL ELECTION UNCERTAINTY AND COMMON STOCK RETURNS IN THE UNITED STATES

There is substantial evidence on the influence of political outcomes on the business cycle and stock market. We further hypothesize that uncertainty about the outcome of a U.S. presidential election should be reflected in pre‐election common stock returns. Prior research pools returns based on the party of the winning candidate, assuming that the outcome of the election is known a priori. We use candidate preference (i.e., polling) data to construct a measure of election uncertainty. We find that if the election does not have a candidate with a dominant lead, stock market volatility (risk) and average returns rise.     

Informed traders and limit order markets

We consider a dynamic limit order market in which traders optimally choose whether to acquire information about the asset and the type of order to submit. We numerically solve for the equilibrium and demonstrate that the market is a “volatility multiplier”: prices are more volatile than the fundamental value of the asset. This effect increases when the fundamental value has high volatility and with asymmetric information across traders. Changes in the microstructure noise are negatively correlated with changes in the estimated fundamental value, implying that asset betas estimated from high-frequency data will be incorrect.     

Investigating the determinants of banking coexceedances in Europe in the summer of 2008

We examine the nature, extent and possible causes of bank contagion in a high frequency setting. Looking at six major European banks in the summer and autumn of 2008, we model the lower coexceedances of these banks returns. We find that market microstructure, volatility (measured by range based measures) and limited general market conditions are key determinants of these coexceedances. We find some evidence that herding occurred.     

Sovereign CDS Spreads,Volatility, and Liquidity: Evidence from 2010 German Short Sale Ban

The paper examines global impact of 2010 German short sale ban on sovereign credit default swap (CDS) spreads, volatility, and liquidity across 54 countries. We find that CDS spreads continue rising after the ban in the debt crisis region, which suggests that the short selling ban is incapable of suppressing soaring borrowing costs in these countries. However, we find that the ban helps stabilize the CDS market by reducing CDS volatility. The reduction in CDS volatility is greater in the eurozone than that in the non‐eurozone. Furthermore, we find that the CDS market liquidity has been impaired during the ban for the PIIGS (Portugal, Ireland, Italy, Greece, and Spain) countries. In contrast, there are no dramatic changes in the market liquidity for non‐PIIGS eurozone and non‐eurozone samples. The findings suggest that the short sale ban is ineffective to reduce sovereign borrowing costs in the debt crisis region if the underlying economy has not been significantly improved.     

Spot price modeling and the valuation of electricity forward contracts: The role of demand and capacity

We propose a model where wholesale electricity prices are explained by two state variables: demand and capacity. We derive analytical expressions to price forward contracts and to calculate the forward premium. We apply our model to the PJM, England and Wales, and Nord Pool markets. Our empirical findings indicate that volatility of demand is seasonal and that the market price of demand risk is also seasonal and positive, both of which exert an upward (seasonal) pressure on the price of forward contracts. We assume that both volatility of capacity and the market price of capacity risk are constant and find that, depending on the market and period under study, it could either exert an upward or downward pressure on forward prices. In all markets we find that the forward premium exhibits a seasonal pattern. During the months of high volatility of demand, forward contracts trade at a premium. During months of low volatility of demand, forwards can either trade at a relatively small premium or, even in some cases, at a discount, i.e. they exhibit a negative forward premium.     

Fundamental indexation via smoothed cap weights

If prices of individual stocks are unbiased but noisy approximations to fundamental values, there will be a gap in returns between the standard cap-weighted market portfolio and the one based on fundamentals. The discrepancy occurs because, relative to fundamentals, cap-weights are too large (small) for stocks with positive (negative) deviations from fundamental values. It follows that the usual cap-weighted portfolio will underperform relative to the fundamental-based portfolio as long as prices revert to fundamental values. This has led Arnott et al. to propose new market indices based on a firm’s fundamental size as measured by its revenues, number of employees, and so on. In this paper we follow the same principle but propose to estimate fundamental weights using a smoothed average of standard cap-weights. Since the putative excess returns of a fundamentals-weighted portfolio requires reversion to fundamental values, and because fundamental values are likely to change slowly, we can estimate current fundamentals by smoothing the time series of a stock’s noisy prices. The determination of fundamental size in terms of accounting data is thereby replaced by a simple estimate based on price history. We derive expressions for expected returns of the market capitalization-based and fundamentals-based portfolios under various assumptions about (i) the random deviations from fundamental values and (ii) the change in fundamentals over time. We present empirical comparisons between portfolios and find the returns of the fundamentals-based portfolios exceed the standard indices by an amount comparable to the prior estimates that used accounting data to determine size.     

Shiller's CAPE: Market Efficiency and Risk

Robert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long‐term stock returns. This is often interpreted as evidence of market inefficiency. We present two findings contrary to such an interpretation. First, if markets are efficient, stock returns should be higher than the risk‐free rate. We find that even when CAPE is in its ninth decile, future 10‐year stock returns, on average, are higher than future returns on 10‐year U.S. Treasurys. Thus, the results are largely consistent with market efficiency. Second, consistent with a risk–return tradeoff, we find that CAPE is negatively associated with future stock market volatility.