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We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion and a bubble regime represented by the stochastic super-exponential Sornette and Andersen (Int J Mod Phys C 13(2):171–188, 2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset i onto another asset j, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005–2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity variable as the difference between the transfer entropies from i to j and from j to i, which is used in a series of rank ordered regressions to predict the maximum loss (%MaxLoss) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is some predictability obtained by using the transfer entropy involving industrial sectors to explain the %MaxLoss of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics. 相似文献
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Didier Nibbering Richard Paap Michel van der Wel 《International Journal of Forecasting》2018,34(2):288-311
This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component. 相似文献
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The aim of this study was to model and characterize the psychologicalprocesses that underlie the dynamics of global self-esteem and physicalself over time. Twice a day for 228 consecutive days, seven participantscompleted a short inventory (PSI-6, Ninot et al., 2001) measuring sixsubjective dimensions: global self-esteem, physical self-worth, physicalcondition, sport competence, physical strength, and attractive body.Each series was modeled by means of ARIMA procedures. The resultsshowed that a simple moving average model provided a satisfactoryaccount for the dynamics of all series. This model suggests that acombination of two opposite processes underlies the dynamics ofself-concept: preservation, which tends to restore the previousvalue after a disturbance, and adaptation, which tends to inflectthe series in the direction of the perturbation. 相似文献
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In a model where a monopolistic downstream firm (assembler) negotiates simultaneously with each of its intermediate‐input suppliers the prices of the complementary components which enter its product, we analyze the process by which the assembler separates from its suppliers as a Markov Perfect equilibrium. Due to a negative strategic effect (the prices and profits of independent suppliers decrease when their number increases), the assembler’s marginal return from keeping an upstream subsidiary is lower than the market value of an independent supplier. Separation is immediate when the downstream firm’s initial number of upstream subsidiaries is below a critical level. It is progressive in the reverse case and eventually leads to a mixed strategy whereby the assembler keeps all the remaining subsidiaries with some probability, and sells all them off in one go with the complementary probability. 相似文献
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The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels. 相似文献
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