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The purpose of this paper is to find and describe some stylized facts for foreign exchange and stock market returns, which are explored using statistical methods.

Formal statistics for testing presence of autocorrelation, asymmetry, and other deviations from normality are applied. Dynamic correlations and different kernel estimations and approximations to the empirical distributions are also under scrutiny. Furthermore, dynamic analysis of mean, standard deviation, skewness and kurtosis are also performed to evaluate time‐varying properties in return distributions.

The findings include: different types of non‐normality in both markets, fat tails, excess furtosis, return clustering and unconditional time‐varying moments. Identifiable volatility cycles in both forex and stock markets are associated to common macro financial uncertainty events.

The paper is the first work of this type in Peru.

This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied.

In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method.

It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms.

This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc.

This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy.

This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

In this paper, the link between stock distribution announcements and capital markets is examined. The results show that stock split announcements result in higher share prices and narrower percentage bid‐ask spreads, while stock dividend announcements have little effect on either prices or percentage spreads.

Many prior tests of market efficiency, which occurred decades ago, were limited by data and did not employ methodology to correct for leptokurtosis in the stock return distribution. Furthermore, these studies did not test many aspects of conditional market efficiency. One aspect of a potential conditional violation of market efficiency is whether stock markets are efficient conditional on the level of stock return.

This paper uses quantile regressions to control for leptokurtosis in the stock return distribution and simultaneous quantile regressions to test whether markets are efficient conditional on the level of the market return. This paper uses market-level stock return data to bias against finding significant results in the efficiency tests. Furthermore, the author uses data from 1926 through 2018, providing the longest time period to date under which market efficiency is tested.

This paper presents evidence that the autoregressive coefficient decreases across return levels in stock market indices. The autoregressive coefficient is positive around highly negative returns and negative or insignificant around highly positive returns, which suggests that when stock returns are low they are more likely to continue lower, and when stock returns are high they are more likely to reverse. Results additionally suggest that market efficiency is not time-invariant and that stock markets have become more efficient over the sample period.

This paper extends the literature by finding evidence of a violation of weak-form market efficiency conditional on the level of stock returns. It further extends the literature by finding evidence that the stock market has become more efficient between 1926 and 2018.

The purpose of this paper is to investigate the opinions of managers of Indian firms on stock splits and bonus shares (stock dividends) and relate them to explanations for stock distributions identified in the prior literature.

The authors use descriptive statistics from a mail survey to the company secretaries of 500 firms listed on the National Stock Exchange of India to elicit their responses about statements involving stock splits and bonus shares.

The survey evidence shows that among the competing motives for stock splits, the liquidity hypothesis receives the highest level of support followed by the attention-getting variant of the signaling hypothesis, signaling, and the preferred trading range hypotheses. Regarding bonus shares, respondents express strong support for the retained earnings, liquidity, and signaling hypotheses but lesser support for the cash substitution and preferred trading range hypotheses.

The survey evidence provides new insights into the stated motivations for stock distributions, especially bonus shares, among Indian firms but the ability to generalize the results is tempered by the relatively small number of respondents. This limits the ability to test for statistically significant differences between the various competing hypotheses. Hence, the results are suggestive rather than definitive.

The survey evidence suggests that no single explanation dominates all others for issuing stock splits or bonus shares in India. Thus, managers have multiple reasons for engaging in stock distributions.

Few studies use survey methodology to examine Indian dividend policy. Given the dearth of survey evidence on stock distributions among Indian firms, this study not only updates the limited evidence on stock splits but also provides the first survey evidence about managerial views on bonus shares.

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.

This paper studies whether individual stocks provide higher returns than government bond in the Chinese market.

The authors compare individual stock returns and government bond returns in the Chinese market.

The authors find that more than half of individual stocks underperform government bonds over the same period in China, which highlights the important role of positive skewness in the distribution of individual stock returns. The high return of a few stocks is the reason why the stock market return is higher than that of government bond in China.

The results of this paper emphasize that portfolio diversification plays an important role in the Chinese market.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

The purpose of this paper is to investigate the effect of decentralised control on finished goods inventory levels in a distribution system, and to identify the factors that determine the overall inventory level.

The authors' study is based on a mixed method approach using both a survey and semi‐structured interviews to assess inventory management practices and firm performance.

It was found that the common assumptions that distribution outlets or dealers are homogenous and that their behaviour is uniform in response to central control, such as the manufacturer's strategy, do not hold in practice. In fact, the authors show that under conditions of decentralised control, the inventories held at outlet level vary greatly around the aggregate inventory at overall manufacturer level and in this sense bear little resemblance to it. Amongst other conclusions, these findings provide a possible explanation for previous studies' inconclusive evidence on inventory reduction.

The authors' research is based on evidence from the automotive industry in the USA; future research may include a wider industry analysis and geographical scope.

The paper identifies how incentives and decision‐making structures at the outlet level need to be considered in order to derive decisions that are optimal at the supply chain level.

The paper extends the current literature on the determinants of inventory levels by using dealer‐level data, as opposed to manufacturer or firm‐level data in previous studies, thereby identifying possible causes for the previously inconclusive evidence on inventory levels in distribution systems.

The relationship between idiosyncratic risk and stock return has been debated for decades; this study reexamined this relationship in the Pakistani stock market by using the quantile regression approach along with the prospect theory.

The present study is quantitative, and secondary data obtained from an emerging market are used. The quantile regression method allows the estimates of idiosyncratic risk to vary across the entire distribution of stock returns, i.e. the dependent variable. In this study, the standard deviation of regression residuals from the Fama and French three-factor model was used to measure idiosyncratic risk. Convenience sampling is employed; the sample consists of 82 firms listed on the KSE-100 index, with 820 annual observations for the ten years from 2011 to 2020. After computing results by using quantile regression, the study's findings, ordinary least squares (OLS) and least sum of absolute deviation (LAD) regression techniques are also compared.

The quantile regression estimation results indicate that idiosyncratic risk is positively correlated with stock returns and that this relationship is contingent on whether prices are rising or falling. Consistent with the prospect theory, the finding suggests that stock investors tend to avoid risk when they anticipate a loss but are more willing to take risks when they anticipate a profit. The results of the OLS and LAD regressions indicate that the method typically employed in previous studies does not adequately describe the relationship between idiosyncratic risk and stock return at extreme points or across the entire distribution of stock return.

These empirical findings shed new light on the relationship between idiosyncratic risk and stock return in Pakistani stock market literature.