Distribution Of Returns Research Articles

Gender differences in the stability of trust and risk-taking

The individual willingness to trust is compared to the inclination to take lottery risk in six distinct scenarios, controlling the return distributions. Trust responds to changes in the admissible return levels, but exhibits significantly smaller responsiveness to return expectations compared to parallel risk-taking. Paired comparisons suggest that the investors sacrifice 5% of the expected payoff to trust anonymous responders. Trust is more calculated and volatile for males, while appearing relatively stable for females. The results connect with evidence regarding physiological differences between trust and risk-taking, and in addition suggest that trust is more of a distinctive trait for females compared to males. The paper broadly discusses the results and their implications, connecting to diverse streams in the trust literature.

What Is Mature and What Is Still Emerging in the Cryptocurrency Market?

In relation to the traditional financial markets, the cryptocurrency market is a recent invention and the trading dynamics of all its components are readily recorded and stored. This fact opens up a unique opportunity to follow the multidimensional trajectory of its development since inception up to the present time. Several main characteristics commonly recognized as financial stylized facts of mature markets were quantitatively studied here. In particular, it is shown that the return distributions, volatility clustering effects, and even temporal multifractal correlations for a few highest-capitalization cryptocurrencies largely follow those of the well-established financial markets. The smaller cryptocurrencies are somewhat deficient in this regard, however. They are also not as highly cross-correlated among themselves and with other financial markets as the large cryptocurrencies. Quite generally, the volume V impact on price changes R appears to be much stronger on the cryptocurrency market than in the mature stock markets, and scales as R(V)∼Vα with α≳1.

Presidential elections and stock return volatility: evidence from selected sub-Saharan African stock markets

PurposeThis paper aims to investigate the effect of presidential elections on stock return volatility in five leading stock markets in sub-Saharan Africa.Design/methodology/approachThis paper uses various criteria to select an appropriate generalized autoregressive conditional heteroscedasticity model to estimate the second moment of the return distribution with the inclusion of pre- and post-presidential election dummy variables that capture the effect of presidential elections on stock market volatility.FindingsThe empirical results show that high pre-election uncertainty increases volatility in the Nairobi Stock Exchange, Stock Exchange of Mauritius and the Nigeria Stock Exchange. Furthermore, the results show that volatility in stock return is reduced 90 days after an election in Nigeria and South Africa but increases 90 days after elections in Ghana.Originality/valueContrary to the previous studies that are conducted in a single country with focus on specific elections, this paper provides a comparative analysis of presidential elections and stock return volatility in five leading stock markets in sub-Saharan Africa.

Swap variance hedging and efficiency: The role of high moments

AbstractIn this article, we propose a new theoretical approach for developing hedging strategies based on swap variance (SwV). SwV is a generalized risk measure equivalent to a polynomial combination of all moments of a return distribution. Using the S&P 500 index and West Texas Intermediate (WTI) crude oil spot and futures price data, as well as simulations by varying the distribution of asset returns, we investigate the dynamic differences between hedge ratios and portfolio performances based on SwV (with high moments) and variance (without high moments). We find that, on average, the minimizing‐SwV hedging suggests more short futures contracts than minimizing‐variance hedging; however, when market conditions deteriorate, the minimizing‐SwV hedging suggests fewer short positions in futures. The superior posthedge performances of the mean‐SwV hedged portfolios over the mean‐variance hedged portfolios in highly volatile or extremely calm markets confirm the efficiency of the mean‐SwV hedging strategy.

The Effect of Investor Sentiment throughout the Distribution of Returns in the Chinese Stock Market

This paper adopts the data of xueqiu.com in the first half of 2015 from the Datago database and selects 100 A-shares stocks in CSI100. The number of followers is considered when building the bullishness index. In addition, quantile regression is used to study the contem-poraneous and predictive effects of sentiment on stock returns. The result suggests that sen-timent has negative contemporaneous effects on stock returns, while for the predictive mod-el, the effect is positive. Furthermore, the effects of sentiment are stronger in extreme cir-cumstances and more substantial at lower quantile than at higher quantile. However, the weighted bullishness index does not explain well as the original one. Overall, this paper ver-ifies the difference of effects between extreme and normal conditions, then attempts to use a new method to construct a bullishness index and offer more evidence about the relationship between social network sentiment and stock returns in the Chinese stock market.

Active versus Passive: In the Bond Universe

The recent rapid growth of passive investment instruments in the fixed-income market raises the question of their relative outperformance compared to corresponding active investment strategies. This article analyzes an ample sample of actively managed global bond funds over the period from 2010 to 2020 and compares them with the performance of the passive instruments tracking the apposite bond market index, namely, the Bloomberg Barclays Multiverse Index. On average, the active bond managers and index are roughly in line. However, a closer look at the distribution of returns on active strategies shows that some managers are successful in beating the index frequently or over many years.

A performance evaluation of portfolio insurance under the Black and Scholes framework: An application of the economic index of riskiness

The investment performance of an option-based portfolio insurance strategy is compared with a buy-and-hold strategy under the Black and Scholes framework. Under this setup, we can derive the return distributions for the portfolio insurance and buy-and-hold strategies. According to the economic performance measure, which generalizes the Sharpe measure, the portfolio insurance with no transaction costs almost outperforms buy-and-hold in all scenarios studied. Further, if the percent of principal protected is chosen optimally, the portfolio insurance is better than buy-and-hold. Although the portfolio insurance loses some ground for short investment horizons with transaction costs, the portfolio insurance performs relatively well for long investment horizons.

Hellinger’s distance to normal distribution as market invariant

Main purpose of distance based portfolio constructions is for portfolio imitation. Here we used distance from normal distribution for other purpose. We attempted to find static market invariant within possible linear combinations of given random variables. We conjectured that “closeness” to normal distribution of possible portfolios in market may reliably represent market microstructure with possible correlations between assets. Taking the squared Hellinger’s distance, we sought for each level of desired mean return the portfolio whose return distribution is closest to Gaussian, with variance taken from efficient frontier found by initially solving mean-variance problem. We found that minimal distance differs significantly from market to market. The sensitivity check showed small average sensitivity for 5% change of a 5% portion of data, small sensitivity for adding new variables in simulated market, and extreme sensitivity to bin numbers. Though distance to normality differed among markets, its sensitivity being small enough in average sometimes showed extreme changes.

Generating synthetic laser scanning data of forests by combining forest inventory information, a tree point cloud database and an open-source laser scanning simulator

Abstract Airborne laser scanning (ALS) data are routinely used to estimate and map structure-related forest inventory variables. The further development, refinement and evaluation of methods to derive forest inventory variables from ALS data require extensive datasets of forest stand information on an individual tree-level and corresponding ALS data. A cost-efficient method to obtain such datasets is the combination of virtual forest stands with a laser scanning simulator. We present an approach to simulate ALS data of forest stands by combining forest inventory information, a tree point cloud database and the laser scanning simulation framework HELIOS++. ALS data of six 1-ha plots were simulated and compared to real ALS data of these plots. The synthetic 3D representations of the forest stands were composed of real laser scanning point clouds of individual trees that were acquired by an uncrewed aerial vehicle (UAV), and, for comparison, simplified tree models with cylindrical stems and spheroidal crowns. The simulated ALS point clouds of the six plots were compared with the real point clouds based on canopy cover, height distribution of returns and several other point cloud metrics. In addition, the performance of biomass models trained using these synthetic data was evaluated. The comparison revealed that, in general, both the real tree models and the simplified tree models can be used to generate synthetic data. The results differed for the different study sites and depending on whether all returns or only first returns were considered. The measure of canopy cover was better represented by the data of the simplified tree models, whereas the height distribution of the returns was – for most of the study sites – better represented by the real tree model data. Training biomass models with metrics derived from the real tree model data led to an overestimation of biomass, while using metrics of the simplified tree model data resulted in an underestimation of biomass. Still, the accuracy of models trained with simulated data was only slightly lower compared to models trained with real ALS data. Our results suggest that the presented approach can be used to generate ALS data that are sufficiently realistic for many applications. The synthetic data may be used to develop new or refine existing ALS-based forest inventory methods, to systematically investigate the relationship between point cloud metrics and forest inventory variables and to analyse how this relationship is affected by laser scanning acquisition settings and field reference data characteristics.

Paying for inattention

We augment the standard rational inattention model by allowing the decision maker to alter the distribution of payoffs. The model captures real-life circumstances in which decision makers choose their incentives to pay attention, for instance, through the choice of insurance (full-coverage contracts reduce the incentives to pay attention compared to partial coverage). This new framework, specifically the ability to observe the decision maker’s choice of payoff redistribution, allows us to elicit the decision maker’s targeted attention level. This is a novel method of eliciting the object of interest—attention—typically obtained through performance (i.e., repetitions) in the literature. Furthermore, by manipulating the cost of payoff redistribution, the framework allows us to examine rational inattention models without making parametric assumptions on the cost of attention function. With a laboratory experiment, we validate novel comparative static predictions of our model. The subjects respond to the link between payoff redistribution decisions and attention in accordance to the theory.

Identification and estimation of categorical random coefficient models

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A generalized method of moments estimation procedure is proposed, also employed by Peter Schmidt and his coauthors to address heterogeneity in time effects in panel data models. Using Monte Carlo simulations, we find that moments of the random coefficients can be estimated reasonably accurately, but large samples are required for the estimation of the parameters of the underlying categorical distribution. The utility of the proposed estimator is illustrated by estimating the distribution of returns to education in the USA by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing returns to education for those with postsecondary education, whereas within-group heterogeneity has been rising mostly in the case of individuals with high school or less education.

Quantile prediction for Bitcoin returns using financial assets’ realized measures

This paper explores which properties of financial asset prices drive Bitcoin’s return distributions, using quantile regressions with lagged realized moment measures of various financial assets. The result shows that Bitcoin’s lagged realized volatility predicts its return distributions very well, revealing Bitcoin’s aspect as a risk asset. Moreover, its lagged realized kurtosis plays some role in prediction in recent periods. In contrast, other financial assets’ realized measures have limited predictive power, which implies the relative uniqueness of Bitcoin’s price movements. Finally, out-of-sample predictions using lasso quantile regressions confirm the robust predictive power of lagged Bitcoin variables even in the Covid-19 period.

A method for predicting VaR by aggregating generalized distributions driven by the dynamic conditional score

Constructing a more effective value at risk (VaR) prediction model has long been a goal in financial risk management. In this paper, we propose a novel parametric approach and provide a standard paradigm to demonstrate the modeling. We establish a dynamic conditional score (DCS) model based on high-frequency data and a generalized distribution (GD), namely, the GD-DCS model, to improve the forecasts of daily VaR. The model assumes that intraday returns at different moments are independent of each other and obey the same kind of GD, whose dynamic parameters are driven by DCS. By predicting the motion law of the time-varying parameters, the conditional distribution of intraday returns is determined; then, the bootstrap method is used to simulate daily returns. An empirical analysis using data from the Chinese stock market shows that Weibull-Pareto -DCS model incorporating high-frequency data is superior to traditional benchmark models, such as RGARCH, in the prediction of VaR at high risk levels, which proves that this approach contributes to the improvement of risk measurement tools.

Investing in Emerging Markets: An Empirical Comparative Analysis

Purpose: Investing in emerging markets may present a growing list of opportunities against a backdrop of additional risks because the drivers of returns in these markets are increasingly domestic and decreasingly global. Despite the proliferation of research on the risk involved in investing in emerging markets, very little is known on the distribution of their returns and their maximum expected losses. Therefore, the aim of this study was to fill in this gap.
 Design/Methodology/Approach: A Cramer-von Mises, Watson and Modified value at Risk approach was used on a sample of five emerging and five developed markets from May 11, 2018, to May 11, 2023.
 Findings: The findings revealed no significant difference in the returns distribution between these two markets but a much higher expected loss in emerging markets than their developed counterparts. Although investing in emerging markets may have growing opportunities.
 Implications/Originality/Value: Several strategies can be used to mitigate some of the risks involved. One strategy is to invest in a diversified portfolio of emerging market assets, which can help reduce exposure to individual country and sector risks. Another strategy is to invest in emerging market funds. This study is the first as per the author’s knowledge to provide an empirical comparative analysis between emerging markets and developed markets

Stock Return of Companies on the Indonesia Stock Exchange: A Comparative Study Before and After a Stock Split

A stock split is one of the corporate actions that the company can take to reduce the nominal value of the share price. A stock split is done by increasing the number of outstanding shares to encourage more share trading. This study aims to test whether there is a significant change in the performance of stock returns before and after the stock split operation. This study will use a quantitative research methodology, and the sampling strategy will be purposive sampling. These two things will go hand in hand. The data type used is secondary data, which is the daily closing price of shares. These prices are then processed so that the stock returns can be calculated. The length of time for the study was 20 days, starting ten days before the stock split activity and ending ten days after the stock split activity. This is done according to the stock split date. Because the normality test results showed that the returns obtained before and after the stock split are not normally distributed, the Wilcoxon marked rating test applied in SPSS version 25 will be used to test the hypothesis regarding the distribution of returns before and after the stock split. Hypothesis testing resulted in the conclusion that there is a substantial difference between return and stock split. This conclusion was reached based on the test findings.

Age and market capitalization drive large price variations of cryptocurrencies

Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28% of the cryptocurrencies as they age and grow in market capitalization.

Market efficiency of cryptocurrency: evidence from the Bitcoin market

This study examines whether the Bitcoin market satisfies the (weak-form) efficient market hypothesis using a quantum harmonic oscillator, which provides the state-specific probability density functions that capture the superimposed Gaussian and non-Gaussian states of the log return distribution. Contrasting the mixed evidence from a variance ratio test, the high probability allocated to the ground state suggests a near-efficient Bitcoin market. Findings imply that as Bitcoin evolves into an efficient market, speculators might encounter difficulty in exploiting profitable trading strategies. Furthermore, when policymakers initiate tight regulations to control the market, they should closely monitor market efficiency as an index of price distortion.

A marine cleaning mutualism provides new insights in biological market dynamics.

Most mutually beneficial social interactions (cooperation within species, mutualism between species) involve some degree of partner choice. In an analogy to economic theory as applied to human trading practices, biological market theory (BMT) focuses on how partner choice affects payoff distributions among non-human traders. BMT has inspired a great diversity of research, including research on the mutualism between cleaner fish Labroides dimidiatus and other marine fish, their 'clients'. In this mutualism, clients have ectoparasites removed and cleaners obtain food in return. We use the available data on L. dimidiatus cleaner-client interactions to identify avenues for future expansion of BMT. We focus on three main topics, namely how partner quality interacts with supply-to-demand ratios to affect service quality, the role of threats and forms of forceful intervention, and the potential role of cognition. We consider it essential to identify the specifics of each biological market as a basis for the development of more sophisticated BMT models. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Exuberance by design? Hyping cryptocurrencies markets through token underpricing

ABSTRACT We investigate potential factors influencing token after-market returns to explain the extreme levels of underpricing experienced in the cryptocurrencies market. This research analyses a sample of 300 tokens issued between September 2015 and May 2018, fully capturing the window of maximum ICO activity. The results confirm the presence of significant underpricing with an average of 180.66% and a median of 32.21%, which far exceed that of the IPO market. The cumulative after-market mean returns at three months, six months, nine months, and twelve months continue to be remarkably high and above 100%. However, the return distributions exhibit substantial negative median values, highlighting the poor post-ICO performance of most tokens. The research also confirms the IPO ‘fads hypothesis’ as a reasonable explanation of token underpricing, which attributes positive initial returns to investor overreactions. This hypothesis provides a rationale for ICO higher underpricing with respect to the IPO market as well. Finally, the quality of the management team positively affects token after-market returns while the project technical validity does not significantly impact on them. Blockchain entrepreneurs may use these results to design tokens and the offering campaigns with the aim of reducing negative swings in the after-market.

Dynamic Asset-Backed Security Design

Abstract Borrowers obtain liquidity by issuing securities backed by the current period payoff and resale price of a long-lived collateral asset, and they are privately informed about the payoff distribution. Asset price can be self-fulfilling: a higher asset price lowers adverse selection and allows borrowers to raise greater funding, which makes the asset more valuable, leading to multiple equilibria. Optimal security design eliminates multiple equilibria, improves welfare, and can be implemented as a repo contract. Persistent adverse selection lowers debt funding, generates volatility in asset prices, and exacerbates credit crunches. The theory demonstrates the role of asset-backed securities on stability of market-based financial systems.