Codon Usage and Selection on Proteins

Selection pressures on proteins are usually measured by comparing homologous nucleotide sequences (Zuckerkandl and Pauling 1965). Recently we introduced a novel method, termed volatility, to estimate selection pressures on proteins on the basis of their synonymous codon usage (Plotkin and Dushoff 2003; Plotkin et al. 2004). Here we provide a theoretical foundation for this approach. Under the Fisher-Wright model, we derive the expected frequencies of synonymous codons as a function of the strength of selection on amino acids, the mutation rate, and the effective population size. We analyze the conditions under which we can expect to draw inferences from biased codon usage, and we estimate the time scales required to establish and maintain such a signal. We find that synonymous codon usage can reliably distinguish between negative selection and neutrality only for organisms, such as some microbes, that experience large effective population sizes or periods of elevated mutation rates. The power of volatility to detect positive selection is also modest—requiring approximately 100 selected sites—but it depends less strongly on population size. We show that phenomena such as transient hyper-mutators can improve the power of volatility to detect selection, even when the neutral site heterozygosity is low. We also discuss several confounding factors, neglected by the Fisher-Wright model, that may limit the applicability of volatility in practice. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Dr. Lauren Meyers]     

Confidence and the Stock Market: An Agent-Based Approach

Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations—indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior.     

Estimating selection pressures from limited comparative data

We recently introduced a novel method for estimating selection pressures on proteins, termed "volatility," which requires only a single genome sequence. Some criticisms that have been levied against this approach are valid, but many others are based on misconceptions of volatility, or they apply equally to comparative methods of estimating selection. Here, we introduce a simple regression technique for estimating selection pressures on all proteins in a genome, on the basis of limited comparative data. The regression technique does not depend on an underlying population-genetic mechanism. This new approach to estimating selection across a genome should be more powerful and more widely applicable than volatility itself.     

Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors'' network structure by measuring the investors'' communication patterns. Our results showed that the investors'' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors'' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum''s reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.     

Fourier Spot Volatility Estimator: Asymptotic Normality and Efficiency with Liquid and Illiquid High-Frequency Data

The recent availability of high frequency data has permitted more efficient ways of computing volatility. However, estimation of volatility from asset price observations is challenging because observed high frequency data are generally affected by noise-microstructure effects. We address this issue by using the Fourier estimator of instantaneous volatility introduced in Malliavin and Mancino 2002. We prove a central limit theorem for this estimator with optimal rate and asymptotic variance. An extensive simulation study shows the accuracy of the spot volatility estimates obtained using the Fourier estimator and its robustness even in the presence of different microstructure noise specifications. An empirical analysis on high frequency data (U.S. S&P500 and FIB 30 indices) illustrates how the Fourier spot volatility estimates can be successfully used to study intraday variations of volatility and to predict intraday Value at Risk.     

Universal Behavior of Extreme Price Movements in Stock Markets

Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evidence that a modification to the random model—adding a slow, but significant, fluctuation to the standard deviation of the process—accurately explains the probability of different-sized price changes, including the relative high frequency of extreme movements. Furthermore, we show that this process is similar across stocks so that their price fluctuations can be characterized by a single curve. Because the behavior of price fluctuations is rooted in the characteristics of volatility, we expect our results to bring increased interest to stochastic volatility models, and especially to those that can produce the properties of volatility reported here.     

The impact of market volatility on the cell therapy industry

Stock market volatility in the cell therapy industry has greatly hindered the investment necessary to fund translational therapies. Here, we review the volatility of leading companies and suggest that a distinct industry is maturing to a point at which the volatility should subside, providing a more attractive environment for future growth.     

Inferring on the Intentions of Others by Hierarchical Bayesian Learning

Inferring on others'' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser''s incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players'' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers'' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser''s intentions were presently stable. Finally, our model of the players'' inference predicted the players'' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers'' helpfulness and the advisers'' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.     

On the comparative static properties of the expected population density in the presence of stochastic fluctuations

We consider the impact of increased stochastic fluctuations on the expected density of an unstructured population evolving according to a regular diffusion process subject to a concave expected growth rate. By relying on the flow nature of the solutions of stochastic differential equations and Girsanovs theorem, we demonstrate that typically increased volatility decreases the expected future population density. As a consequence, we are able to characterize the sensitivity of the expected population density with respect to changes in the diffusion coefficient measuring the size of the stochastic fluctuations. We provide both qualitative and quantitative information about the consequences of a mis-specified volatility structure and, especially, of a deterministic approximation to stochastic population growth. We also consider the effect of uncertainty in the initial density and demonstrate that the sign of the relationship between the expected population density and initial uncertainty is unambiguosly negative. Received: 15 February 1999 / Revised version: 29 September 1999 / Published online: 5 May 2000     

Cross-Correlation Asymmetries and Causal Relationships between Stock and Market Risk

We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock prices from the Standard & Poor''s 500 index for 1994–2013. We focus on the behavior of the cross-correlations at the times of financial crises with significant jumps of market volatility. The observed historical dynamics showed that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when the volatility of an individual stock follows the market volatility and vice versa.     

Crossed and Locked Quotes in a Multi-Market Simulation

Financial markets are often fragmented, introducing the possibility that quotes in identical securities may become crossed or locked. There are a number of theoretical explanations for the existence of crossed and locked quotes, including competition, simultaneous actions, inattentiveness, fee structure and market access. In this paper, we perform a simulation experiment designed to examine the effect of simple order routing procedures on the properties of a fragmented market consisting of a single security trading in two independent limit order books. The quotes in the two markets are connected solely by the routing decision of the market participants. We report on the health of the consolidated market as measured by the duration of crossed and locked states, as well as the spread and the volatility of transaction prices in the consolidated market. We aim to quantify exactly how the prevalence of order routing among a population of market participants affects properties of the consolidated market. Our model contributes to the zero-intelligence literature by treating order routing as an experimental variable. Additionally, we introduce a parsimonious heuristic for limit order routing, allowing us to study the effects of both market order routing and limit order routing. Our model refines intuition for the sometimes subtle relationships between the prevalence of order routing and various market measures. Our model also provides a benchmark for more complex agent-based models.     

Does corn market uncertainty impact the US ethanol prices?

The growing interest in biofuel as a green energy source has intensified the linkages between corn and ethanol markets, especially in the United States that represents the largest producing and exporting country for ethanol in the world. In this study, we examine the effect of corn market uncertainty on the price changes of US ethanol applying a set of GARCH‐jump models. We find that the US ethanol price changes react positively to the corn market volatility shocks after controlling for the effect of oil price uncertainty. In addition, we document that the impact of corn price volatility on the US ethanol prices appears to be asymmetric. Specifically, only the positive corn market volatility shocks are found to influence the ethanol market returns. Our findings also suggest that time‐varying jumps do exist in the ethanol market.     

Intraspecific variability may not compensate for increasing climatic volatility

The role of intraspecific variability is being examined to improve predictions of responses to climate change or invasions and in research on diversity. Simultaneously, the probability and implications of increased high-frequency climate variability have been raised. An agent based model simulated two species on an environmental gradient representing an alpine treeline; a trend in its volatility was added. The species have different levels of variability, and each individual has further unique heterogeneity. Environmental volatility and individual heterogeneity were based on tree ring data from Pinus albicaulis. Simulations show that increasing volatility leads to population declines, including extinctions, and to sharper ecotones, and this impact is only slightly lessened by higher heterogeneity. Some simulation runs reveal an unanticipated selection for greater individual variability when volatility creates strong negative anomalies that fall short of extinction events. Increasing volatility can have significant ecological impacts because negative anomalies are not balanced by positive ones.     

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios.     

The variability and volatility of sleep: An ARCHetypal behavior

Using 1975–2005 Dutch time-diary data covering over 10,000 respondents for 7 consecutive days each, we show that sleep time exhibits non-constant variability, or volatility, characterized by stationary autoregressive conditional heteroscedasticity: The absolute values of deviations from a person’s average sleep on one day are positively correlated with those on adjacent days. Sleep is more variable on weekends and among younger people, those without young children, or with less education. Volatility is greater among parents with young children, slightly greater among men, but independent of other demographics. Economic incentives to minimize the dispersion of sleep imply higher-wage workers will exhibit less dispersion, which we observe. Sleep volatility spills over onto volatility in other personal activities, with no reverse causation onto sleep. The results illustrate a novel dimension of inequality among people and could be applied to a wide variety of human behavior and biological processes.     

What constitutes evidence of community structure? A reply to van der Maarel,Noest & Palmer

Abstract. Van der Maarel et al. (1995) — hereafter VNP — criticize our analyses of Öland limestone grassland. They have four general objections, addressed below. 1. VNP attribute our significant results to randomness (‘volatility’); however, significant deviation from randomness cannot be explained by randomness. VNP's conclusion of volatility derives from results that are inconsistent with ours. Their ecological interpretation assumes spatial and temporal near-constancy in the vegetation; we demonstrate that these assumptions are not correct. 2. We discussed physical limitations to plant module packing. VNP give estimated module sizes. We appreciate this information, though data on actual module sizes and overlap patterns would be required before the real module packing effects could be determined. If module packing were really the main cause of deficits of variance in richness, the effect would not fluctuate between sites and years, as VNP admit it does. 3. The shape of the richness frequency distribution is a potentially interesting additional form of analysis, albeit one that we had chosen not to include in our analysis. However, it is surprising that VNP dismiss analysis of richness variance, but then interpret a more subtle aspect of the richness distribution — the skewness. VNP's redefinition of the terms‘Niche facilitation’for a deficit of low richness values, and ‘Niche limitation’ for a deficit of high values, leads them to misrepresent our arguments. They are mistaken in suggesting that niche limitation will necessarily lead to a skewed curve: they do not allow for the fact that the species frequencies, on which the null model is based already, incorporate effects of species interactions.‘Niche facilitation’, as defined by VNP, would lead to a variance excess, not a deficit as they assume. 4. VNP's criticism of a priori guild classifications had already been met by our use of the intrinsic guild approach. Guild analyses offer the best way forward. VNP use principally the methodology of 1987. There have been a number of methodological advances since that time. We used these advances in our original paper specifically to circumvent the kinds of problems that they identify. We agree with VNP that it would be very useful to explore the mechanisms behind assembly rules with experiments, but the logical first step is to identify potential assembly rules, as we have been attempting to do. We conclude that, whilst VNP make some interesting observations, none of their criticisms invalidate our results or conclusions. Our original approach stands as the best known approach, we believe, for searching for community structure in such data, and we reaffirm the validity of our ecological conclusions.     

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture–recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture–recapture to illustrate volatility across estimation methods.     

A Financial Market Model Incorporating Herd Behaviour

Herd behaviour in financial markets is a recurring phenomenon that exacerbates asset price volatility, and is considered a possible contributor to market fragility. While numerous studies investigate herd behaviour in financial markets, it is often considered without reference to the pricing of financial instruments or other market dynamics. Here, a trader interaction model based upon informational cascades in the presence of information thresholds is used to construct a new model of asset price returns that allows for both quiescent and herd-like regimes. Agent interaction is modelled using a stochastic pulse-coupled network, parametrised by information thresholds and a network coupling probability. Agents may possess either one or two information thresholds that, in each case, determine the number of distinct states an agent may occupy before trading takes place. In the case where agents possess two thresholds (labelled as the finite state-space model, corresponding to agents’ accumulating information over a bounded state-space), and where coupling strength is maximal, an asymptotic expression for the cascade-size probability is derived and shown to follow a power law when a critical value of network coupling probability is attained. For a range of model parameters, a mixture of negative binomial distributions is used to approximate the cascade-size distribution. This approximation is subsequently used to express the volatility of model price returns in terms of the model parameter which controls the network coupling probability. In the case where agents possess a single pulse-coupling threshold (labelled as the semi-infinite state-space model corresponding to agents’ accumulating information over an unbounded state-space), numerical evidence is presented that demonstrates volatility clustering and long-memory patterns in the volatility of asset returns. Finally, output from the model is compared to both the distribution of historical stock returns and the market price of an equity index option.     

Being on the Field When the Game Is Still Under Way. The Financial Press and Stock Markets in Times of Crisis

This paper looks at the relationship between negative news and stock markets in times of global crisis, such as the 2008/2009 period. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility. Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable.     

The comparative method rules! Codon volatility cannot detect positive Darwinian selection using a single genome sequence

All established methods for detecting positive selection at the molecular level rely on comparisons between nucleotide sequences. An exceptional method that purports to detect selection on the basis of a single genomic sequence has recently been proposed. This method uses a measure called "codon volatility," defined for each codon as the ratio between the number of nonsynonymous codons that differ from the codon under study at a single nucleotide position and the number of sense codons that differ from the codon under study at a single nucleotide position. Here, we examine various properties of codon volatility and its derivatives and use simulation of evolutionary processes to determine whether they can be used to detect selective pressures. Codons for only four amino acids (glycine, leucine, arginine, and serine) show any variation in codon volatility. Thus, codon volatility is mainly a proxy for amino acid usage, rather than for codon usage, with 65% of all synonymous changes and 27% of all nonsynonymous changes being undetectable by this measure. Genes identified by the volatility method as being subject to positive selection tend to have idiosyncratic amino acid compositions (e.g., they are glycine rich or arginine poor). An additional property of codon volatility is the near zero variance of its mean expectation, which translates into overestimated statistical significance estimates, especially in the absence of corrections for multiple comparisons. A comparison with measures of selection inferred through comparative methodology reveals no relationship between the results of the two methods. Finally, we show that codon volatility can increase in the absence of positive Darwinian selection; that is, increased codon volatility is not indicative of positive selection.