Excess Market Volatility Research Articles

Construction of resilience mechanisms in response to container shipping market volatility during the pandemic period: From the perspective of market supervision

As a key enabler of international trade, the container shipping market experienced port congestion, space shortages, and skyrocketing freight rates following the outbreak of the COVID-19 pandemic. Therefore, this paper first utilizes the vector autoregressive model with exogenous variables (VARX) to analyze the interactive mechanisms of economy, capacity, freight rate, and port congestion during the pandemic to identify the causes behind the excessive market volatility. We then introduce government supervision to improve the policy deficiencies in pricing rules and assess whether prohibiting liner companies from charging excessive surcharges can solve market dilemmas by constructing a tripartite evolutionary game model. The results show that liner companies deliberately maintained congestion by using strict control over shipping capacity to raise freight rates and obtain high profits, which is one of the main reasons for the problems. However, we also conclude that relying solely on the spontaneous behavior of the market is unlikely to resolve the predicament. Instead, government intervention can play a crucial role in encouraging liner companies to increase appropriate container capacity, stabilize freight rates, and mitigate the risk of supply chain disruptions due to port congestion. This, in turn, can enhance the resilience of the container transport system.

Herding and China's market-wide circuit breaker

China stock market regulators implemented market-wide circuit breakers when the market crash was imminent on the 4th of January 2016. This paper examines whether traders’ herding behaviour led to the circuit breaker trigger and limited success in moderating market reaction. Using intraday data, we show extensive herding in the pre-halt and post-halt periods on the event day. We find herding and excessive market volatility are mutually causative. Importantly, we identify herding stems from both market sentiments and fundamentals around the circuit breaker trigger. In a market dominated by individual investors, non-fundamental herding primarily characterises the Chinese stock market. Nonetheless, the uncertain and disruptive impact of the circuit breaker led to massive and rapid stocks sale underlying the fundamental herding. Investors trade in the direction of the crowd giving rise to self-enforced herding and greater market volatility, and culminating in the circuit breaker trigger.

An empirical examination of investor sentiment and stock market volatility: evidence from India

Understanding the irrational sentiments of the market participants is necessary for making good investment decisions. Despite the recent academic effort to examine the role of investors’ sentiments in market dynamics, there is a lack of consensus in delineating the structural aspect of market sentiments. This research is an attempt to address this gap. The study explores the role of irrational investors’ sentiments in determining stock market volatility. By employing monthly data on market-related implicit indices, we constructed an irrational sentiment index using principal component analysis. This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility. The results showed that irrational sentiment significantly causes excess market volatility. Moreover, the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns. The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

How do investors behave in the context of a market crash? Evidence from India

PurposeThe main aim of this paper is to empirically test at market level, the investors' differential reaction to information, contribution of their confidence level and adaptive behaviour to excessive market volatility in Indian stock market.Design/methodology/approachThe Bivariate Vector Autoregression and Impulse Response Analysis are used to study whether investors over/under-react to private and public information. EGARCH models are used to study the contribution of investors' over/under-confidence and adaptive behaviour to excessive market volatility.FindingsThe investors over-react to private information and under-react to public information during pre-crash period, become overconfident and contribute to excessive volatility. They under-react to both private and public information during after-crash period, become under-confident and also conform to adaptive market hypothesis (AMH).Research limitations/implicationsThe empirical results of the study can help investors to minimize the negative impact of over/under-confidence on their expected utility.Practical implicationsThe investors shall perform a post-analysis of investment, become aware of their past behavioural mistakes and start adapting to changing market conditions. This shall move the markets towards a new equilibrium in long run thus conforming AMH. However, the investors sometimes display an apparently irrational behaviour during this process.Originality/valueTo the best of the author's knowledge, this is the first study at market level data examining investors' over/under-reaction, over/under-confidence and adaptive behaviour in the context of stock market crash.

Rethinking Margin and Volatility

This paper tests whether margin requirements cause asset price volatility. Using novel data on margin requirements and by exploiting exogenous variation from a threshold-based regression discontinuity design, I document that increasing margin causes a persistent decrease in future volatility (negative level effect) and a decline in trading volume. For an average 13.7% increase in margin requirement, I find an average 3.7% decrease in volatility. These results contrast with prior empirical studies that largely find no relationship between volatility and margin and provide support for the efficacy of financial market policies that seek to curb excess market volatility.

Testing for herding behaviour among energy sectors in Chinese stock exchange

Herding behaviour in stock markets leads a group of investors to imitate others and make the same economic decisions as other market participants, causing excess market volatility and price instability. This paper aims to test for the herding behaviour in different energy sectors of Chinese Stock Exchange. Firstly, Generalized Capital Asset Pricing Model (GCAPM) is employed to observe the stocks in energy sectors during financial crisis, which can test for the nonlinear relationship between return of particular portfolio and the average return of market. The empirical results indicate the presence of herding behaviour in energy sectors. Secondly, artificial neural networks are used to predict the herding behaviour in Chinese stock markets. It is advisable that during the periods when the stock market is unstable, investors should keep awake to avoid herding behaviour.

Systematic Risk and Share Turnover [or Beta as a Measure of Mispricing

This paper documents a simple and powerful cross-sectional mechanism: a higher rate of investor participation in trading a stock translates into a greater contribution of the stock to market movements. The participation-driven overreaction (1) forms a persistent source of excess market volatility and, by implication, return predictability, (2) introduces a strong mechanical element into the beta-return relation, making this relation conditional on market state and challenging the meaningfulness of traditional tests of the CAPM, and (3) suggests that the low-risk anomaly (Black et al. (1972)) reflects a reversal of overreaction in the cross-section of stock returns. The endogeneity of market beta challenges the mainstream interpretation of the systematic risk-return relation and the notion of risk-adjusted returns in the finance literature.

Herding and excessive risk in the American stock market: A sectoral analysis

This paper aims to test whether herding behavior is a driving force of excessive market volatility and increasing bubbles in the US stock market at a sectoral level. Trading volume turnover and investors’ sentiment are ubiquitous factors besides market return to fuel herding movement in most sectors. Our sample covers all listed companies in the American stock market over four major turmoil periods. Granger causality test shows that herding is a vital ingredient to increasing bubbles in some sectors, but not all. Moreover, herding and trading volume have an inhibiting effect on both overall and in-sector market volatility in large markets, as opposed to concentrated markets commonly studied in the literature.

Is the CAPM a Biased Estimator of Market Risk?

The Capital Asset Pricing Model (CAPM) is often used to determine an appropriate return on equity for regulated utilities. However, the CAPM will sometimes produce unrealistically high or low results during times of excess market volatility. I hypothesize that these results do not indicate a failure in the model itself. Instead, they are caused by the traditional implementation of the CAPM and the underlying definition of risk used by many financial theorists.My paper uses 1997-2001 data to provide an empirical comparison of betas generated in a manner consistent with the capital asset pricing model (CAPM betas) to betas generated using a method based on market losses (“Alternative Betas”). I use psychological literature and a mathematical example to suggest that when a loss-based notion of risk is used, only those observations in which the market declines constitute market risk; and that the CAPM is a systematically biased estimator of market risk because the variability of gains receive the same weight as the variability of losses.In the CAPM world, there are only two types of risk: market risk (measured by beta), and firm-specific risk; with the assumption that only market risk will be rewarded since firm specific risk can be diversified away. The CAPM assumes that the market is mean-variance efficient; predicts that the constant term will be 0; and predicts that a security's risk premium is proportional to both beta and the risk premium of the market portfolio (Bodie, 1996). Mathematically, the risk premium is equal to β[E(rM) − rf], where rM is the market return and rf is the risk-free rate.In the 30 years since the initial publication of the CAPM, there have been numerous criticisms of the model in the financial literature. Studies have suggested that the CAPM is not testable because the composition of the true market portfolio is unknown (Roll, 1977); the constant term is positive and significant (Miller and Scholes, 1972); the estimated risk premium is significantly lower than the actual risk premium (Black, Jensen, and Scholes, 1972); non-systematic risk has a significant effect on excess returns (Lintner, 1965b); and the expected return-beta relationship is not fully consistent with empirical observations (Fama and MacBeth, 1973).

Systematic Risk and Share Turnover

I show that the stocks’ contribution to market volatility increases uniformly with their share turnover. The source of this relation is the market force driving the persistence of cross-sectional variation in turnover, namely the rate of investor participation in a stock. A higher rate of participation generates a persistent pattern of relative overreaction to common shocks, resulting in a large cross-sectional dispersion in market beta at both short and long return horizons. This mechanism (i) sheds light on the source of excess market volatility, (ii) introduces a strong mechanical element into the beta-return relation, challenging the traditional interpretation of this relation and the meaningfulness of the existing tests of the CAPM, and (iii) suggests that the low-beta anomaly (Black et al., 1972) reflects a reversal of the beta-driven overreaction in the cross-section of stock returns.

The US zero-coupon yield spread as a predictor of excess daily stock market volatility

Slope of the yield curve has often been cited as an indicator of economic activity. Based on this premise, we extend the study to examine whether one can use the US zero-coupon yield spread to predict excess stock market volatility of three international stock markets – the United States, the United Kingdom and Hong Kong. By using daily trading data and changes in the US yield spread, our study entails four spread maturity spectrums, three stock markets, 7-trading day forecast horizons and four probit models. In the static models, we find evidence to support a US spread–volatility relationship in all three stock markets in the medium spread maturity up to 3 days ahead. In the dynamic models, although the predictive power of yield spread weakens slightly, the lagged dependent variable plays an important role.

Competition, premature trading and excess volatility

A substantial body of research suggests that it is difficult to account for all of the volatility of asset prices in terms of news. This paper attempts to explain the excess volatility puzzle as a consequence of competitive interaction between market participants in the presence of noisy information. We develop a model of competitive interaction between market participants in response to unverified information. Our model shows that in the presence of competitive pressures, market participants find it optimal to act prematurely on unverified information. This premature reaction leads to lower total profits and excess market volatility in equilibrium. Our model also shows that the spike in volatility at the closing time of the market can be modelled as a direct consequence of premature trading.

Emerging Market Volatility: Lessons from The Taper Tantrum

Accommodative monetary policies in advanced economies have spurred increased capital inflows into emerging markets since the global financial crisis. Starting in May 2013, when the Federal Reserve publicly discussed its plans for tapering unconventional monetary policies, these emerging markets have experienced financial turbulence at the same that their domestic economic activity has slowed. This paper examines their experiences and policy responses and draws broad policy lessons. For emerging markets, good macroeconomic fundamentals matter, and early and decisive measures to strengthen macroeconomic policies and reduce vulnerabilities help dampen market reactions to external shocks. For advanced economies, clear and effective communication about the exit from unconventional monetary policy can and did help later to reduce the risk of excessive market volatility. And for the global community, enhanced global cooperation, including a strong global financial safety net, offers emerging markets effective protection against excessive volatility.

Competition, Premature Trading and Excess Volatility

A substantial body of research suggests that it is difficult to account for all of the asset price volatility in terms of news. This paper attempts to explain the excess volatility puzzle as a consequence of competitive interaction between market participants. We develop a model of competitive interaction in the presence of noisy, but verifiable information. Our model shows that in the presence of competitive pressures, market participants find it optimal to act prematurely on unverified, noisy information. This premature reaction leads to lower total profits and excess market volatility in equilibrium. Our model also suggests that the spike in volatility at the closing time of the market can be modelled as a direct consequence of premature trading.

Corporate Social Responsibility, Noise, and Stock Market Volatility

Organizational signals about corporate social responsibility may have a harmful impact on equity markets for two main reasons. First, corporate social responsibility is not systematically correlated with companies' economic fundamentals. Second, opportunistic managers are incentivized to distort information provided to market participants about their firms' corporate social responsibility. Either causal force, by itself, makes it difficult for market participants to interpret information about corporate social responsibility accurately. This greater noise in financial markets typically invites more noise trading, which in turn leads to excess market volatility (among all publicly traded firms) and, in a particular context of social-institutional processes and structures, to excess market valuations of firms that are widely perceived as socially responsible.

Purdah—On the Rationale for Central Bank Silence around Policy Meetings

Many central banks share the practice of purdah, a guideline of abstaining from communication around policy meetings. Although seemingly contradicting the virtue of transparency by withholding information precisely when it is sought after intensely, it has been justified on grounds that such communication may create excessive market volatility. This paper assesses the purdah for the Federal Reserve and confirms that financial markets are substantially more sensitive to central bank communication around policy meetings. Short‐term interest rates react three to four times more strongly in the purdah before Federal Open Market Committee meetings than otherwise, and volatility increases (compared to a reduction otherwise).

How the Financial Crisis Affects Pensions and Insurance and Why the Impacts Matter

This paper discusses the key sources of vulnerabilities for pension plans and insurance companies in light of the global financial crisis of 2008. It also discusses how these institutional investors transit shocks to the rest of the financial sector and economy. The crisis has re-ignited the policy debate on key issues such as: 1) the need for countercyclical funding and solvency rules; 2) the tradeoffs implied in marked based valuation rules; 3) the need to protect contributors towards retirement from excessive market volatility; 4) the need to strengthen group supervision for large complex financial institutions including insurance and pensions; and 5) the need to revisit the resolution and crisis management framework for insurance and pensions.

How the Financial Crisis Affects Pensions and Insurance and Why the Impacts Matter

This paper discusses the key sources of vulnerabilities for pension plans and insurance companies in light of the global financial crisis of 2008. It also discusses how these institutional investors transit shocks to the rest of the financial sector and economy. The crisis has re-ignited the policy debate on key issues such as: 1) the need for countercyclical funding and solvency rules; 2) the tradeoffs implied in marked based valuation rules; 3) the need to protect contributors towards retirement from excessive market volatility; 4) the need to strengthen group supervision for large complex financial institutions including insurance and pensions; and 5) the need to revisit the resolution and crisis management framework for insurance and pensions.

A Quantitative Behavioral Model and its Implications for Market Volatility, Underreaction, and Overreaction

This paper develops a model of weight assignments using a pseudo-Bayesian approach that reflects investors' behavioral biases. In this parsimonious model of investor sentiment, weights induced by investors' conservative and representative heuristics are assigned to observations of the earning shocks of stock prices. Such weight assignments enable us to provide a quantitative link between some market anomalies and investors' behavioral biases. Using this model, we can show that while learning may contribute to market anomalies, anomalies still exist even after the learning process has been completed. In particular, we can deduce the following: (1) Excess market volatility will result from investors' biased heuristics. It is the representative heuristic, rather than the conservative heuristic, which contributes to excess volatility in the market. In addition, excess volatility is more prominent when the discount rate is small. (2) Through a misapplication of Bayes' rule, investors' behavioral biases lead to short-term underreaction and long-term overreaction in the market. The more conservative/representative the heuristic, the larger the magnitude of the return auto-correlation and the larger is the contrarian/momentum trading profit. Further analysis shows that the representative heuristic contributes to the contrarian trading profit and the conservative heuristic contributes to the momentum trading profit. In addition, the smaller the discount rate, the larger the contrarian/momentum profit. (3) Investors' behavioral biases induce a magnitude effect in the under- and overreaction phenomena, i.e., the more severe the earnings shock, the larger the market autocorrelation and the larger the momentum/contrarian trading profit. (4) The magnitude effect described in (3) is convex in nature.

Regulatory Response to Market Volatility and Manipulation: A Case Study of Mumbai and Karachi Stock Exchanges

This study examines the regulatory intervention in India and Pakistan in response to episodes of excessive market volatility and manipulation and its effectiveness in achieving declared objectives. Our empirical analysis indicates that while the Indian regulatory agencies seem to have achieved their objectives in curtailing manipulative and speculative behavior, there appears to be little impact on such behavior in the case of the Karachi Stock Exchange. Significant differences in the regulatory effectiveness and industry structure may explain the difference in the market behavior outcomes following regulatory interventions. A stronger competitive environment in India, because of the existence of multiple organized exchanges, seems to facilitate effective enforcement of public policy.