arXiv subjects

Baosheng Yuan

Publications and source records attributed to Baosheng Yuan.

7 recordsLinked to original sources

arXiv2006

Conditional Probability as a Measure of Volatility Clustering in Financial Time Series

In the past few decades considerable effort has been expended in characterizing and modeling financial time series. A number of stylized facts have been identified, and volatility clustering or the tendency toward persistence has emerged as the central feature. In this paper we propose an appropriately defined conditional probability as a new measure of volatility clustering. We test this measure by applying it to different stock market data, and we uncover a rich temporal structure in volatility fluctuations described very well by a scaling relation. The scale factor used in the scaling provides a direct measure of volatility clustering; such a measure may be used for developing techniques for option pricing, risk management, and economic forecasting. In addition, we present a stochastic volatility model that can display many of the salient features exhibited by volatilities of empirical financial time series, including the behavior of conditional probabilities that we have deduced.

physics.soc-ph↗

arXiv2005

Growing Directed Networks: Organization and Dynamics

We study the organization and dynamics of growing directed networks. These networks are built by adding nodes successively in such a way that each new node has $K$ directed links to the existing ones. The organization of a growing directed network is analyzed in terms of the number of ``descendants'' of each node in the network. We show that the distribution $P(S)$ of the size, $S$, of the descendant cluster is described generically by a power-law, $P(S) \sim S^{-η}$, where the exponent $η$ depends on the value of $K$ as well as the strength of preferential attachment. We determine that, in the case of growing random directed networks without any preferential attachment, $η$ is given by $1+1/K$. We also show that the Boolean dynamics of these networks is stable for any value of $K$. However, with a small fraction of reversal in the direction of the links, the dynamics of growing directed networks appears to operate on ``the edge of chaos'' with a power-law distribution of the cycle lengths. We suggest that the growing directed network may serve as another paradigm for the emergence of the scale-free features in network organization and dynamics.

cond-mat.stat-mech↗

arXiv2005

Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations

While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price fluctuations. We propose and study a model of an artificial stock market consisting of heterogeneous agents with DRA, and we find that DRA is the main driving force for excess price fluctuations and the associated volatility clustering. We employ a popular power utility function, $U(c,γ)=\frac{c^{1-γ}-1}{1-γ}$ with agent specific and time-dependent risk aversion index, $γ_i(t)$, and we derive an approximate formula for the demand function and aggregate price setting equation. The dynamics of each agent's risk aversion index, $γ_i(t)$ (i=1,2,...,N), is modeled by a bounded random walk with a constant variance $δ^2$. We show numerically that our model reproduces most of the ``stylized'' facts observed in the real data, suggesting that dynamic risk aversion is a key mechanism for the emergence of these stylized facts.

physics.soc-ph↗

arXiv2004

Evolutionary Dynamics in Complex Networks of Competing Boolean Agents

We investigate the dynamics of network minority games on Kauffman's NK networks (Kauffman nets), growing directed networks (GDNets), as well as growing directed networks with a small fraction of link reversals (GDRNets). We show that the dynamics and the associated phase structure of the game depend crucially on the structure of the underlying network. The dynamics on GDNets is very stable for all values of the connection number $K$, in contrast to the dynamics on Kauffman's NK networks, which becomes chaotic when $K>K_c=2$. The dynamics of GDRNets, on the other hand, is near critical. Under a simple evolutionary scheme, the network system with a "near" critical dynamics evolves to a high level of global coordination among its agents. In particular, the performance of the system is close to the optimum for the GDRNets; this suggests that criticality leads to the best performance. For Kauffman nets with $K>3$, the evolutionary scheme has no effect on the dynamics (it remains chaotic) and the performance of the MG resembles that of a random choice game (RCG).

cond-mat.other↗

arXiv2004

Evolutionary Dynamics and the Phase Structure of the Minority Game

We show that a simple evolutionary scheme, when applied to the minority game (MG), changes the phase structure of the game. In this scheme each agent evolves individually whenever his wealth reaches the specified bankruptcy level, in contrast to the evolutionary schemes used in the previous works. We show that evolution greatly suppresses herding behavior, and it leads to better overall performance of the agents. Similar to the standard non-evolutionary MG, the dependence of the standard deviation $σ$ on the number of agents $N$ and the memory length $m$ can be characterized by a universal curve. We suggest a Crowd-Anticrowd theory for understanding the effect of evolution in the MG.

cond-mat.stat-mech↗

arXiv2004

Theory of the Three-Group Evolutionary Minority Game

Based on the adiabatic theory for the evolutionary minority game (EMG) that we proposed earlier[1], we perform a detail analysis of the EMG limited to three groups of agents. We derive a formula for the critical point of the transition from segregation (into opposing groups) to clustering (towards cautious behaviors). Particular to the three-group EMG, the strategy switching in the "extreme" group does not occur at every losing step and is strongly intermittent. This leads to an correction to the critical value of the number of agents at the transition, $N_c$. Our expression for $N_c$ is in agreement with the results obtained from our numerical simulations.

cond-mat.stat-mech↗

arXiv2003

Theory of Phase Transition in the Evolutionary Minority Game

We discover the mechanism for the transition from self-segregation (into opposing groups) to clustering (towards cautious behaviors) in the evolutionary minority game (EMG). The mechanism is illustrated with a statistical mechanics analysis of a simplified EMG involving three groups of agents: two groups of opposing agents and one group of cautious agents. Two key factors affect the population distribution of the agents. One is the market impact (the self-interaction), which has been identified previously. The other is the market inefficiency due to the short-time imbalance in the number of agents using opposite strategies. Large market impact favors "extreme" players who choose fixed strategies, while large market inefficiency favors cautious players. The phase transition depends on the number of agents ($N$), the reward-to-fine ratio ($R$), as well as the wealth reduction threshold ($d$) for switching strategy. When the rate for switching strategy is large, there is strong clustering of cautious agents. On the other hand, when $N$ is small, the market impact becomes large, and the extreme behavior is favored.

cond-mat↗