There's more to volatility than volume

It is widely believed that fluctuations in transaction volume, as reflected in the number of transactions and to a lesser extent their size, are the main cause of clustered volatility. Under this view bursts of rapid or slow price diffusion reflect bursts of frequent or less frequent trading, which cause both clustered volatility and heavy tails in price returns. We investigate this hypothesis using tick by tick data from the New York and London Stock Exchanges and show that only a small fraction of volatility fluctuations are explained in this manner. Clustered volatility is still very strong even if price changes are recorded on intervals in which the total transaction volume or number of transactions is held constant. In addition the distribution of price returns conditioned on volume or transaction frequency being held constant is similar to that in real time, making it clear that neither of these are the principal cause of heavy tails in price returns. We analyze recent results of Ane and Geman (2000) and Gabaix et al. (2003), and discuss the reasons why their conclusions differ from ours. Based on a cross-sectional analysis we show that the long-memory of volatility is dominated by factors other than transaction frequency or total trading volume.Comment: 25 pages, 9 figure

Small games and long memories promote cooperation

Complex social behaviors lie at the heart of many of the challenges facing evolutionary biology, sociology, economics, and beyond. For evolutionary biologists in particular the question is often how such behaviors can arise \textit{de novo} in a simple evolving system. How can group behaviors such as collective action, or decision making that accounts for memories of past experience, emerge and persist? Evolutionary game theory provides a framework for formalizing these questions and admitting them to rigorous study. Here we develop such a framework to study the evolution of sustained collective action in multi-player public-goods games, in which players have arbitrarily long memories of prior rounds of play and can react to their experience in an arbitrary way. To study this problem we construct a coordinate system for memory-$m$ strategies in iterated $n$-player games that permits us to characterize all the cooperative strategies that resist invasion by any mutant strategy, and thus stabilize cooperative behavior. We show that while larger games inevitably make cooperation harder to evolve, there nevertheless always exists a positive volume of strategies that stabilize cooperation provided the population size is large enough. We also show that, when games are small, longer-memory strategies make cooperation easier to evolve, by increasing the number of ways to stabilize cooperation. Finally we explore the co-evolution of behavior and memory capacity, and we find that longer-memory strategies tend to evolve in small games, which in turn drives the evolution of cooperation even when the benefits for cooperation are low

Impact of information cost and switching of trading strategies in an artificial stock market

This paper studies the switching of trading strategies and its effect on the market volatility in a continuous double auction market. We describe the behavior when some uninformed agents, who we call switchers, decide whether or not to pay for information before they trade. By paying for the information they behave as informed traders. First we verify that our model is able to reproduce some of the stylized facts in real financial markets. Next we consider the relationship between switching and the market volatility under different structures of investors. We find that there exists a positive relationship between the market volatility and the percentage of switchers. We therefore conclude that the switchers are a destabilizing factor in the market. However, for a given fixed percentage of switchers, the proportion of switchers that decide to buy information at a given moment of time is negatively related to the current market volatility. In other words, if more agents pay for information to know the fundamental value at some time, the market volatility will be lower. This is because the market price is closer to the fundamental value due to information diffusion between switchers.Comment: 15 pages, 9 figures, Physica A, 201

Two-level relationships and Scale-Free Networks

Through the distinction between ``real'' and ``virtual'' links between the nodes of a graph, we develop a set of simple rules leading to scale-free networks with a tunable degree distribution exponent. Albeit sharing some similarities with preferential attachment, our procedure is both faster than a na\"ive implementation of the Barab\'asi and Albert model and exhibits different clustering properties. The model is thoroughly studied numerically and suggests that reducing the set of partners a node can connect to is important in seizing the diversity of scale-free structures

Demystifying the Characteristics of 3D-Stacked Memories: A Case Study for Hybrid Memory Cube

Three-dimensional (3D)-stacking technology, which enables the integration of DRAM and logic dies, offers high bandwidth and low energy consumption. This technology also empowers new memory designs for executing tasks not traditionally associated with memories. A practical 3D-stacked memory is Hybrid Memory Cube (HMC), which provides significant access bandwidth and low power consumption in a small area. Although several studies have taken advantage of the novel architecture of HMC, its characteristics in terms of latency and bandwidth or their correlation with temperature and power consumption have not been fully explored. This paper is the first, to the best of our knowledge, to characterize the thermal behavior of HMC in a real environment using the AC-510 accelerator and to identify temperature as a new limitation for this state-of-the-art design space. Moreover, besides bandwidth studies, we deconstruct factors that contribute to latency and reveal their sources for high- and low-load accesses. The results of this paper demonstrates essential behaviors and performance bottlenecks for future explorations of packet-switched and 3D-stacked memories.Comment: EEE Catalog Number: CFP17236-USB ISBN 13: 978-1-5386-1232-

Some New Concepts of Shape Memory Effect of Polymers

In this study some new concepts regarding certain aspects related to shape memory polymers are presented. A blend of polylactic acid (PLA) (80%) and polybutylene succinate (PBS) (20%) was prepared first by extrusion, then by injection molding to obtain the samples. Tensile, stress-relaxation and recovery tests were performed on these samples at 70 °C. The results indicated that the blend can only regain 24% of its initial shape. It was shown that, this partial shape memory effect could be improved by successive cycles of shape memory tests. After a fourth cycle, the blend is able to regain 82% of its shape. These original results indicated that a polymer without (or with partial) shape memory effect may be transformed into a shape memory polymer without any chemical modification. In this work, we have also shown the relationship between shape memory and property memory effect. Mono and multi-frequency DMA (dynamic mechanical analyzer) tests on virgin and 100% recovered samples of polyurethane (PU) revealed that the polymer at the end of the shape memory tests regains 100% of its initial form without regaining some of its physical properties like glass transition temperature, tensile modulus, heat expansion coefficient and free volume fraction. Shape memory (with and without stress-relaxation) tests were performed on the samples in order to show the role of residual stresses during recovery tests. On the basis of the results we have tried to show the origin of the driving force responsible for shape memory effect