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The purpose of this article is to assess the pricing of stocks that are traded on both a US stock exchange and a non‐US stock exchange to determine whether interaction exists between the two exchanges.

This article identifies extreme price movements of stocks (winners and losers) in the non‐US stock exchanges that also trade as American depository receipts (ADRs) in the US market, and measure the US market response. Also identifies extreme price movements of stocks (winners and losers) in the US stock exchanges that also trade in the non‐US markets, and measure the non‐US market response.

Finds a significant reversal of winners and losers in the US market, which suggests that the US market attempts to correct the pricing in non‐US markets. Also finds that extreme ADR price movements in the US markets are followed by corrections in the non‐US market.

Market participants appear to monitor unusual stock price movements that just occurred in other markets, and correct for unusual price movements that cannot be rationalized. Such activity in global markets expedites the process by which price discrepancies are corrected. The evidence also suggests that the cost of equity in one market can be influenced by the actions of investors in another market.

This study of non‐US stocks that are cross‐listed in the US in the form of ADRs allows us to examine the interaction of pricing in a stock's local market with pricing in the US market.

Research on price extremes and overreactions as potential violations of market efficiency has a long tradition in investment literature. Arguably, very few studies to date have addressed this issue in cryptocurrencies trading. The purpose of this paper is to consider the extreme value modelling for forecasting COVID-19 effects on cryptocoin markets. Additionally, this paper examines the importance of technical trading indicators in predicting the extreme price behaviour of cryptocurrencies.

This paper decomposes the daily-time series returns of four cryptocurrency returns into potential maximum gains (PMGs) and potential maximum losses (PMLs) at first and then tests their lead–lag relations under an econometric framework. This paper also investigates the non-random properties of cryptocoins by computing the incremental explanatory power of PML–PMG modelling with technical trading indicators controlled. Besides, this paper executes an event study to identify significant changes caused by COVID-19-related events, which is capable of analysing the cryptocoin market overreactions.

The findings of this paper produce the evidence of both market overreactions and trend persistence in the potential gains and losses from coins trading. Extreme price behaviour explains volatility and price trends in crypto markets before and after the outbreak of a pandemic that substantiate the non-random walk behaviour of crypto returns. The presence of technical trading indicators as control variables in the extreme value regressions significantly improves the predictive power of models. COVID-19 crisis affects the market efficiency of cryptocurrencies that improves the usefulness of extreme value predictions with technical analysis.

This paper strongly supports for the robustness of technical trading strategies in cryptocurrency markets. However, the “beast is moving quick” and uncertainty as to the new normalcy about the post-COVID-19 world puts constraint on making best predictions.

The paper contributes substantially to our understanding of the pricing efficiency of cryptocurrency markets after the COVID-19 outbreak. The findings of continuing return predictability and price volatility during COVID-19 show that profitable investment opportunities for cryptocoin traders are prevailing in pandemic times.

The paper is unique to understand extreme return reversals behaviour of cryptocurrency markets regarding events related to COVID-19 breakout.

Purpose – The purpose of this chapter is to investigate the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements.

Methodology/Approach – We use a biorhythm approach characterized by the seasonal affective disorder (SAD) variable to study the impact of emotion on energy markets. Normal times and periods of extreme price movements are approximated by OLS and quantile estimations, respectively.

Findings – We use European energy forward prices of oil, gas, coal, and electricity. European equity future index (Dow Jones Euro Stoxx 50) and euro/dollar US exchange rate are used as control variables for economic and financial environment. Estimating OLS and quantile regressions, we find that seasonal patterns have a significant impact during extreme volatility periods only. Further investigations reveal that the SAD effect is significant during periods of price decrease, but insignificant during price increase times. The out-of-sample predictive ability properties show that our “SAD model” outperforms significantly the pure “macroeconomic” one.

Originality/Value of chapter – This topic is novel in energy finance since I use psychological background theory to understand energy price dynamics. I illustrate the relevance of our approach by comparing the out-of-sample predictive ability of our model against macroeconomic one. My results could be considered to improve energy porfolio allocation.

This research studies private information from extreme price movements or jumps. The authors calculate the private information using a reduced form model from the stochastic volatility jump process and use several statistical robustness tests as well as several frequencies to improve our consistency. This study reveals that private information is significant in explain the existence of jumps in capital markets in Southeast Asia, whereas macroeconomic events cannot explain them. The authors determine empirically that private information in Malaysia, Singapore, Thailand, and Indonesia are not persistent and its value gradually decreases when we use the lower frequency. Based on the Fama–Macbeth regression, this study shows that private information in the capital market has a strong positive relationship with individual returns in Indonesia’s capital market and Thailand’s capital market for all frequencies.

Modern property investment allocation techniques are typically based on recognised measures of return and risk. Whilst these models work well in theory under stable conditions, they can fail when stable assumptions cease to hold and extreme volatility occurs. This is evident in commercial property markets which can experience extended stable periods followed by large concentrated negative price fluctuations as a result of major unpredictable events. This extreme volatility may not be fully reflected in traditional risk calculations and can lead to ruin. The paper aims to discuss these issues.

This research studies 28 years of quarterly Australian direct commercial property market performance data for normal distribution features and signs of extreme downside risk. For the extreme values, Power Law distribution models were examined as to provide a better probability measure of large negative price fluctuations.

The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent, being by at least 30 per cent for the outermost data point. For the statistical outliers beyond 2 SD, a Power Law distribution can overcome many of the shortcomings of the standard deviation approach and therefore better measure the probability of ruin, being extreme downside risk.

In highlighting the challenges to measuring property market performance, analysis of extreme downside risk should be separated from traditional standard deviation risk calculations. In recognising these two different types of risk, extreme downside risk has a magnified domino effect with the tendency of bad news to come in crowds. Big price changes can lead to market crashes and financial ruin which is well beyond the standard deviation risk measure. This needs to be recognised and developed as there is evidence that extreme downside risk determinants are increasing by magnitude, frequency and impact.

Analysis of extreme downside risk should form a key part of the property decision process and be included in the property investment manager’s toolkit. Modelling techniques for estimating measures of tail risk provide challenges and have shown to be beyond traditional risk management practices, being too narrow and constraining a definition. Measuring extreme risk and the likelihood of ruin is the first step in analysing and dealing with risk in both an asset class and portfolio context.

The purpose of this paper is to investigate the possibility of monitoring stress on stock markets from the perspective of a central counterparty (CCP). Due to their balanced positions, CCPs are exposed to extreme price movements in both directions; thus, the major risk for them derives from extreme returns and market illiquidity. The authors examined the connection of the stress alarms of return- and liquidity-based measures to find an objective basis for stress measurement.

The authors defined two types of stress measures: indicators based on extreme returns and liquidity. It is suggested that the stress indicators should be based on the existing risk management methodology that examines different risk measure oversteps. The stress signals of the past nine years on the German stock market were analyzed. The authors investigated the connection between the chosen stress measures to obtain a robust measure for alarming stress.

Although extreme returns and illiquidity are both characteristics of stress, the correlation of returns- and liquidity-based stress indicators is low when taking daily values. On the other hand, the moving averages of the indicators correlate significantly in the case of measures of downward and upward extreme returns and liquidity measured by the relative spread. The results are robust enough to be used for monitoring stress periods.

This paper contributes to understanding the characteristics of stress periods and points to the fact that stress signals measured by different aspects can also differ within the same asset class. The moving averages of returns- and relative spread-based indicators, however, could provide a cost-effective quantitative support for the risk management of a CCP and make the margin calculation predictable for clearing members as well.

The paper aims to examine the link between firm‐level large share price movements, firm‐specific company announcements and corporate governance. Stock market regulation in the UK requires firms to disclose new price‐sensitive information immediately via official news providers. The paper investigates whether large share price movements are accompanied by firm disclosure. It also investigates whether corporate governance attributes influence the degree of disclosure by firms.

The disclosure measure is constructed by identifying the largest abnormal daily stock returns for sample firms, and then firm‐specific announcements in the three‐day window centred on the abnormal return day are searched. Corporate governance variables known to influence disclosure practice are then collected and tested to ascertain whether they influence disclosure for positive and negative (good and bad announcements) abnormal returns.

Large share price movements are accompanied by an official share price movement in 45.2 per cent of cases. This rises to 62.9 per cent when new analyst or newspaper articles are included as potential drivers of the abnormal share price return. The higher the proportion of non‐executive directors and CEO/chair duality lead to a higher incidence of bad news disclosure, suggesting increased scrutiny works. The higher the level of CEO and board ownership the lower the level of disclosure. Finally, institutional ownership concentration appears to negatively influence the level of disclosure.

Higher levels of corporate governance are shown to lead to better firm disclosure. At the same time, the authors find that in almost 40 per cent of large abnormal share price returns no information has come to the market to drive the share price. Thus, the paper has important messages for regulators, who need to investigate why prices often move a long way without accompanying news. Shareholders, particularly institutions, should ensure high levels of disclosure by company directors.

It is well known that the probability distribution of stock returns is non-Gaussian. The tails of the distribution are too “fat,” meaning that extreme price movements, such as stock market crashes, occur more often than predicted given a Gaussian model. Numerous studies have attempted to characterize and explain the fat-tailed property of returns. This is because understanding the probability of extreme price movements is important for risk management and option pricing. In spite of this work, there is still no accepted theoretical explanation. In this chapter, we use a large collection of data from three different stock markets to show that slow fluctuations in the volatility (i.e., the size of return increments), coupled with a Gaussian random process, produce the non-Gaussian and stable shape of the return distribution. Furthermore, because the statistical features of volatility are similar across stocks, we show that their return distributions collapse onto one universal curve. Volatility fluctuations influence the pricing of derivative instruments, and we discuss the implications of our findings for the pricing of options.

The purpose of this paper is to examine the investment behavior of Pakistani stock market participants, specifically with respect to their tendency to exhibit herd behavior.

The study employed two different methodologies suggested by Christie and Huang (1995) and Chang et al. (2000) to test herd formation. Results are based on daily and monthly stock of KSE-100 index for the period 2002-2007.

Results based on daily and monthly stock data from Karachi Stock Exchange indicate the non-existence of herd behavior for the period 2002-2007 and find no support for the rational asset pricing model and investor behavior found inefficient. This study denied proved evidence of herding due to market return asymmetry, high and low trading volume states and asymmetric market volatility. Macroeconomic fundamentals have insignificant role in decision-making process of investor therefore has no impact on herding behavior. However, during liquidity crisis of March 2005, Pakistani stock market exhibit herding behavior due to asymmetry of information among investors, presence of speculator and questionable badla financing-local leverage financing mechanism.

In future, this study can be improved by employing stock returns portfolios based on market capitalization or sector wise portfolio returns from KSE-100. Furthermore by identifying those factors that cause market to be overall inefficient and define the pattern of the investor trading activities.

For an accurate valuation of assets investors should incorporate the herding factor.

As the assets are mispriced, investor behavior is uncertain and markets are inefficient, foreign investors should invest with caution, as large numbers of securities are needed to achieve the same level of diversification than in an otherwise normal market.

In Karachi Stock Exchange, it is first attempt to uncover the herding behavior. This paper contribute to the body of knowledge by investigating the herding behavior in the emerging markets since most of the previous study concentrate more on the developed markets. Furthermore, the study also analyzed the herding behavior in different economic condition and includes economic variables and examines asymmetric effect. This study presents an integrated model to test herding behavior in Pakistani equity market. Consideration of said behavioral effect in the decision-making process of investor will make the decisions more rational and optimal.

The purpose of this paper is to investigate the asymptotic distribution of the extreme daily stock returns in African stock markets over the period 1996‐2007 and examine the implications for downside risk measurement.

Extreme value theory methods are used to model adequately the extreme minimum daily returns in a number of African emerging stock markets.

The empirical results indicate that the generalised logistic distribution best fitted the empirical data over the period of study.

Using the generalised extreme value and normal distributions for risk assessment could lead to an underestimation of the likelihood of extreme share price declines which could potentially lead to inadequate protection against catastrophic losses.

To the best of the author's knowledge, this is the first study to examine the lower tail distribution of daily returns for African emerging stock markets.