Volume-volatility Relationship Research Articles

Forecasting stock market returns with a lottery index: Evidence from China

AbstractThis study constructs a Chinese lottery index (LI) based on six popular lottery preference variables by using the partial least squares method and examines the relationship between the LI and future stock market returns during the period from January 2000 to December 2021. We find that the LI can negatively predict stock market excess returns in‐sample and out‐of‐sample. In addition, the LI can generate a large economic gain for a mean–variance investor. Finally, the predictive sources of the LI stem from a cash flow channel and can be explained by the positive volume–volatility relationship and investor attention.

The Bitcoin volume-volatility relationship: A high frequency analysis of futures and spot exchanges

We examine the volume-volatility relationship across Bitcoin futures and spot markets, using daily realised volatility measures estimated from high frequency intraday data. We estimate realised spot volatility across five major exchanges using both the standard volume weighted price and using a new approach, inspired by the CME Bitcoin Reference Rate methodology. We find that unexpected trading volume is the most important explanatory variable for BRR spot volatility, explaining 20% of variation in price volatility at exchange level. Conversely, we find that both expected and unexpected CME Bitcoin futures volumes play a very limited or even calming role in systemic volatility. Our findings suggest that CME Bitcoin futures are not independently contributing to systemic risk in Bitcoin over the period studied.

Nonlinearity of the volume–volatility correlation filtered through the pointwise Hurst–Hölder regularity

We identify market inefficiency as a pivotal explanatory variable of the puzzling volume–volatility relationship. The result, that can bring together into a coherent framework many apparently conflicting findings, follows from translating the realized volatility into the corresponding pointwise Hurst–Hölder exponent. This allows to measure, at any time t, markets’ departures from the martingale property, i.e. from efficiency as stated by the Efficient Market Hypothesis. We find that when efficiency is not accounted for, a positive contemporaneous relationship emerges; conversely, it disappears as soon as efficiency is taken into account.

Stochastic Interest Rates, Heterogeneous Valuations, and the Volatility-Volume Relation with Search Frictions

Abstract We propose a dynamic equilibrium model with stochastic interest rates in which agents hold heterogeneous valuations for the same asset and take on positions against each other. The model shows that interest rate uncertainty and investor heterogeneity are key determinants of price dispersion. Higher search intensity reduces price dispersion, while raising volume, leading to a negative volatility-volume relation. The sensitivity of volatility to volume is high when liquidity is low, interest rate variations are high and investors’ valuations are more heterogeneous. Evidence supports our model’s predictions and shows that search frictions play an important role in driving the volatility-volume relation. (JEL G12, G13) Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Short selling, divergence of opinion and volatility in the corporate bond market

We investigate the impact of short selling activity on price volatility in the corporate bond market. We find that bond short selling activity is positively related to volatility, trading activity, and the volume-volatility relation. During the Global Financial Crisis, when investors’ expectations tend to be more homogenous, the positive relation between short selling activity and price volatility breaks down. We further show that bond short selling is not a substitute for equity short selling and put option trading. Overall, our study highlights the importance of bond short selling as a platform to express investors’ differences of opinion.

The Bitcoin Volume-volatility Relationship: a High Frequency Analysis of Futures and Spot Exchanges

The Bitcoin Volume-volatility Relationship: a High Frequency Analysis of Futures and Spot Exchanges

Stock Returns, Trading Volumes and Market Volatility: A Study on the Indian Stock Market

This paper aims at investigating the effect of stock returns on trading volume and the trading volume on volatility. To accomplish the study objective, daily data from 1 April 2014 to 30 June 2020 are used. The GLS model is employed to investigate the contemporaneous relationship between the stock returns and trading volume. The EGARCH (1, 1) model is used to explore the volume–volatility relationships, and the causal relationship of stock returns–trading volumes–volatility is attained through the Granger causality test. The study finds that the effect of stock returns on trading volume is asymmetric. It indicates that the negative price changes have a less considerable impact on trading volume than the non-negative price changes. The study also supports the mixture of the distribution hypothesis that postulated the volume–volatility relationship to be positive. The volatility persistence level remains high, even after the conditional volatility model incorporates trading volumes as an exogenous variable. Further, the analysis also reveals that stock return causes trading volume and not vice versa; however, trading volumes cause return volatility. JEL Codes: G10, G12, C22

Volatility, trading activity and maximum lot amounts: Evidence from Borsa Istanbul

Intraday analyses of volatility-volume relation by decomposing the volume into number of trades and average trade size is measured in Borsa Istanbul equity market. Results confirm that the number of trades has more explanatory power over the short-term volatility comparably parallel with the existing literature. The unique maximum lot amount rule, classifying stocks according to their average trade size and limiting the order size for the stocks traded, has given the chance to control for the size of the trades naturally and analyse the relation of the volatility with other trading activity figures. The number of trades is the main factor explaining the variation in the short-term volatility parallel to the literature. Results show that heterogeneous effects arise from binding of trading constraints. The spread, which is mostly equal to the price tick, has the power to explain the volatility for high trading volumes significantly. Analyses show that stealth trading is possible for thinly traded stocks.

Rule-based trading on an order-driven exchange: a reassessment

A core research area of computational behavioral finance investigates emergent price dynamics when heterogeneous traders follow a mix of rule-based strategies and interact indirectly through a limit order book. This paper offers a detailed specification of such a model in order to raise questions about some previous findings. The questions force a comprehensive reconsideration of the price dynamics of a well-known model. This leads to a surprising clarification of the contributions of various trading strategies to market outcomes: a popular characterization of chartism proves largely irrelevant for price dynamics. We also shed new light on the volume-volatility relationship, and provide improved visualizations to expose market behavior.

Do short sellers exacerbate volatility?

PurposeThis paper examines whether short sellers aggravate volatility in the Australian stock market by using five different realized volatility (RV) measures during a more stable period.Design/methodology/approachThe authors develop a measure to capture the abnormal level of short selling for each stock and examine the bivariate and trivariate dynamic relationships between abnormal short selling and five volatility measures: the RV, continuous and jump components of RV, upside and downside volatilities.FindingsOverall, the findings indicate a weak association between abnormal short selling and volatility. Where the relationships are significant, the authors generally find that lagged abnormal short selling is negatively associated with both upside and downside volatilities. In general, short selling does not drive or amplify the decline in stock prices.Originality/valueThis paper contributes to existing literature in various aspects. First, the authors offer evidence on the relationship between abnormal short selling and volatility in a general market condition while existing studies often found mixed results of the effects of short selling on volatility around extreme events. Second, the authors add to the literature on the volume-volatility relation by introducing abnormal short selling. Although abnormal short volume does not supplant the number of trades in the volume-volatility relation, it has some incremental, albeit negative, effect on volatility. Finally, the study provides further evidence for the debate on the desirability of short sellers in financial markets.

Are disagreements agreeable? Evidence from information aggregation

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high-sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

Theoretical and experimental evidence on stock market volatilities: a two-phase flow model

The volume–volatility relationship usually ignores possible effects of stock shares. This article proposes a two-phase flow model assuming that capital and stock flows determine stock price and return volatility. Computational simulations suggest that monodirectional capital or stock flows and collective flows exert different effects on stock return volatilities. Considering the impact of stock flows, the positive relationship between capital and return volatility is no longer guaranteed. The inflow of capital and the outflow of stock increase stock price similarly; but exhibit completely different effects on stock return volatilities. A persistent stock inflow (outflow) reduces (intensifies) return volatilities, whereas a monodirectional persistent capital outflow has no such effect. When capital and stock flows’ velocities satisfy critical values determined by the initial state of the market, the market enlargement accompanied with increasing stock and capital shows no impact on market stability because of stable return volatilities. Otherwise, stock flows drive return volatilities with stronger effects than capital flows. Further experimental studies that simulate the real stock market through a trading system provide strong evidence supporting the two-phase flow model. Given similar driving forces of capital and stock flows, the interaction of them should be considered in constructing investment strategies and setting policies.

Investor participation and the volatility-volume relation: Evidence from an emerging market

Using a unique dataset from the Chinese stock market that keeps track of the daily number of shareholders, we find that the ownership breadth (proxied by the number of shareholders) is negatively related to stock price volatility. However, consistent with the previous literature on volatility-volume relation, we find that the stock price volatility remains positively related to number of traders participating in the market. We also find that the relations of both the daily order imbalance and the daily depth imbalance with the number of shareholders (traders) are negative (positive). Our results suggest that a higher number of shareholders tend to supply liquidity and reduce volatility, while a higher number of traders tend to compete for liquidity and increase volatility.

Monotonicity, linearity and symmetry in the price volatility–volume relationship

PurposeThis paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy.Design/methodology/approachNon-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA.FindingsTrading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally.Originality/valueTo the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

A three-stage model of the volatility-volume relation in the Junk bond market during the 2007-2008 financial crisis

PurposeThe purpose of this paper is to examine the joint dynamics of volatility–volume relation in the high-yield (junk) corporate bond market during the 2007–2008 financial crisis.Design/methodology/approachThe author proposes a new empirical model of three-stage equations to better estimate the volume–volatility relation that helps in alleviating three econometrical problems. In Stage 1, the author estimates the fitted values of trading volume using a censored regression model, to alleviate the truncation problems of using Transaction Reporting and Compliance Engine data. In Stage 2, the author calculates the fitted values of bond return volatility using asymmetric Sign-GARCH model, to control for the asymmetric volatility in return series. In Stage 3, the author uses the fitted values of trading volume from the censored regression model (Stage 1) and the fitted values of return volatility from the GARCH model (Stage 2), to better alleviate the endogeneity problems between both variables.FindingsThe central finding is that conclusions about the statistical significance and the direction of the volume–volatility relationship in the junk bond market are dependent on the econometric methodology used.Originality/valueFrom a practitioner perspective, it is important for professional traders holding positions in fixed income securities in their trading accounts to be aware of their asymmetric time-varying volume–volatility shifting trends. Such knowledge helps traders diversify their positions and manage their portfolios more appropriately.

Does Cross-Border Equity Trading Destabilize the Stock Market: Evidence from Chinese Stock Markets

This paper examines the effects of bi-directional cross-border equity flows on stock returns, volatility and valuation differential using the unique Stock Connect program in China as a natural experiment. Via Stock Connect, investors from Hong Kong (mainland China) can trade qualified stocks listed in Shanghai (Hong Kong). We find that southbound (northbound) investors net purchases predict returns of Hong Kong (Shanghai) stocks. However, southbound and northbound trades exhibit asymmetry in volatility-volume relationship; there is a positive (negative) relationship between lagged southbound (northbound) volume and volatility of connected stocks in Hong Kong (Shanghai). Southbound investors net purchase is positively related to lagged AH share price premium for dual-listed stocks and such trades reduces future valuation differential. Our study shows that stock market integration facilitates information sharing and narrow valuation differential across previously segmented markets. But the effects of stock market integration depend importantly on local market environments such as investor clienteles.

The heterogeneous volume-volatility relations in the exchange-traded fund market: Evidence from China

We decompose the trading volume of exchange-traded funds (ETFs) into specific components according to different triggers of trades: (i) private information, (ii) disagreement among investors due to their different opinions on public information or having different information, and (iii) investor impatience. Then we examine the particular impact of each type of ETF trade on the market volatility of the tracked index. Focusing on the three ETFs tracking the CSI 300, we show that ETF trades stemming from investor disagreement are a key determinant of CSI 300 volatility, dominating other factors considered. Liquidity ETF trades can partially explain CSI 300 volatility. However, little evidence supports a significant correlation between privately informed trades of ETFs and CSI 300 volatility.

Loss aversion in an agent-based asset pricing model

A well-defined agent-based asset pricing model able to match the widely observed properties of financial time series is valuable for testing the implications of various biases associated with investors' behaviour. Extending one of the most successful models in capturing traders behaviour, we present a new behavioural agent-based asset pricing model. Specifically, we introduce a well-known behavioural bias in the model, loss aversion, and evaluate its implications. First, measuring how close the simulated time series are to its empirical counterparts, we show that the model with loss aversion better matches and explains the properties of real-world financial data, compared with the base model without the behavioural bias. Secondly, we assess the impact of different levels of loss aversion not only on the agents' switching mechanisms, but also on the properties of the time series generated by the model. We demonstrate how for different levels of the loss aversion parameter, the biased agents tend to be driven out of the market at different points in time. Since even the simplest strategies have been shown to survive the competition in an agent-based setting, we can link our findings with the behavioural finance literature, which states that investors' systematic biases lead to unexpected market behaviour, instabilities and systematic errors. Finally, we provide an in-depth analysis of the simulated time series and show the resulting dynamics replicate a rich set of the stylized facts including: absence of autocorrelation, heavy tails, volatility clustering and conditional heavy tails of returns, long memory of absolute returns, as well as volume–volatility relations, gain–loss asymmetry, power-law behaviour and long memory of volume.

A Three-Stage Model of the Volume-Volatility Relation in the Junk Bond Market during the 2007–2008 Financial Crisis

This article examines the joint dynamics of volatility-volume relation in the high-yield (junk) corporate bond market during the 2007-2008 financial crisis. I propose a new empirical model of three-stage equations to better estimate the volatility-volume relation that helps in alleviating econometric problems. My central finding is that different methodologies can easily lead to different inferences about the volatility-volume relation in the junk bond market. More specifically, conclusions about the statistical significance and/or the direction of the association between both variables is dependent on the econometric methodology used. From a practitioner perspective, it is important for professional traders holding positions in fixed income securities in their trading accounts to be aware of their asymmetric time-varying volatility-volume shifting trends. Such knowledge helps traders diversify their positions and manage their portfolios more appropriately.

Bitcoin futures: trade it or ban it?

This paper examines the impact of South Korea’s ban on Bitcoin futures on intraday spot volatility, liquidity and volatility–volume relationship. The results show that while reducing the permanent component of intraday spot volatility, the imposition of a ban on Bitcoin futures trading increases the transitory component. For intraday spot liquidity, different liquidity proxies indicate heterogeneous results. Moreover, we identify a positive and unidirectional effect of intraday spot volume on volatility. This effect appears to be stronger in the post-ban period. Overall, over the past few months, South Korea’s Bitcoin futures ban generally has had a significant impact on the intraday dynamics of the Bitcoin spot market.