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Abstract: We introduce the concept of “negative bubbles” as the mirror (but not necessarily exactly symmetric) image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen–Ledoit–Sornette (JLS) model of rational expectation bubbles with a hazard rate describing the collective buying pressure of noise traders. The price fall occurring during a transient negative bubble can be interpreted as an effective random down payment that rational agents accept to pay in the hope of profiting from the expected occurrence of a possible rally. We validate the model by showing that it has significant predictive power in identifying the times of major market rebounds. This result is obtained by using a general pattern recognition method that combines the information obtained at multiple times from a dynamical calibration of the JLS model. Error diagrams, Bayesian inference and trading strategies suggest that one can extract genuine information and obtain real skill from the calibration of negative bubbles with the JLS model. We conclude that negative bubbles are in general predictably associated with large rebounds or rallies, which are the mirror images of the crashes terminating standard bubbles. [Copyright &y& Elsevier]

Abstract: Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreement (repo) market size is a very important element in calculating the overall leverage in a financial market. Therefore, studying the behavior of repo market size can help to understand a process that can contribute to the birth of a financial crisis. We hypothesize that herding behavior among large investors led to massive over-leveraging through the use of repos, resulting in a bubble (built up over the previous years) and subsequent crash in this market in early 2008. We use the Johansen–Ledoit–Sornette (JLS) model of rational expectation bubbles and behavioral finance to study the dynamics of the repo market that led to the crash. The JLS model qualifies a bubble by the presence of characteristic patterns in the price dynamics, called log-periodic power law (LPPL) behavior. We show that there was significant LPPL behavior in the market before that crash and that the predicted range of times predicted by the model for the end of the bubble is consistent with the observations. [Copyright &y& Elsevier]

In this paper, a stochastic optimization framework is proposed to address the microgrid energy dispatching problem with random renewable generation and vehicle activity pattern, which is closer to the practical applications. The patterns of energy generation, consumption and storage availability are all random and unknown at the beginning, and the microgrid controller design (MCD) is formulated as a Markov decision process (MDP). Hence, an online learning-based control algorithm is proposed for the microgrid, which could adapt the control policy with increasing knowledge of the system dynamics and converges to the optimal algorithm. We adopt the linear approximation idea to decompose the original value functions as the summation of each per-battery value function. As a consequence, the computational complexity is significantly reduced from exponential growth to linear growth with respect to the size of battery states. Monte Carlo simulation of different scenarios demonstrates the effectiveness and efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The Johansen–Ledoit–Sornette (JLS) model of rational expectation bubbles with finite-time singular crash hazard rates has been developed to describe the dynamics of financial bubbles and crashes. It has been applied successfully to a large variety of financial bubbles in many different markets. Having been developed over a decade ago, the JLS model has been studied, analyzed, used and criticized by several researchers. Much of this discussion is helpful for advancing the research. However, several serious misconceptions seem to be present within this literature both on theoretical and empirical aspects. Several of these problems stem from the fast evolution of the literature on the JLS model and related works. In the hope of removing possible misunderstanding and of catalyzing useful future developments, we summarize these common questions and criticisms concerning the JLS model and synthesize the current state of the art and existing best practice. [Copyright &y& Elsevier]

In this paper we study spatial quantile regression estimation of multivariate threshold time series models. Bahadur’s representations for our estimators are established, which naturally lead to asymptotic normality of the estimators. Simulations and a real example are used to evaluate the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student’s t , Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results. [ABSTRACT FROM AUTHOR]

The development of logistics accelerates the transportation process, which in turn increases the exchanging efficiency between cities. To understand such exchanging interactions among cities, we build a city logistics network based on a unique data set, which contains millions of online freight orders spanning a period of one year. It is found that this network exhibits a disassortative configuration and its link weight (respectively, node strength) follows a Weibull (an exponential) distribution. We also find that the hierarchical structure of the city logistics network is highly consistent with the strategic layout of constructing modern comprehensive transportation system plan in China's "Thirteenth Five-Year Plan". Furthermore, it is observed that the cities with high Gross Domestic Product (GDP) have high centrality in the city logistics network and the logistics flows between two cities are proportional to the product of their GDPs. Our results not only uncover the topological and hierarchical structure of the city logistics network in China, but also provide an understanding of the structural shaping force on the growth of city logistics network. • We construct a Chinese city logistics network based on millions of freight orders. • The network exhibits a fat-tailed degree distribution and a disassortative structure. • The cities hierarchically occupy the position in the city logistics networks. • City centrality in logistics network positively correlates with its economic status. • The logistics flows between two cities are proportional to their GDP product. [ABSTRACT FROM AUTHOR]