“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

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.     

Competitive moment matching of a New-Keynesian and an Old-Keynesian model

The paper considers two rival models referring to the new macroeconomic consensus: a standard three-equation model of the New-Keynesian variety versus dynamic adjustments of a business and an inflation climate in an ‘Old-Keynesian’ tradition. Over the two subperiods of the Great Inflation and Great Moderation, both of them are estimated by the method of simulated moments. An innovative feature is here that the moments do not only include the autocovariances up to eight lags of quarterly output, inflation and the interest rate, but optionally also a measure of the raggedness of the three variables. In short, the performance of the Old-Keynesian model is very satisfactory and similar to the New-Keynesian model, or even better. In particular, the Old-Keynesian model is better suited to match the new moments without deteriorating the original second moments too much.     

Obtaining collaboration benefits: the role of collaboration-specific investment and absorptive capacity in China

This study examines the role of collaboration-specific investment and absorptive capacity on the attainment of interorganizational collaboration benefits. Grounded in the extended resource-based view, and using survey data from Chinese executives, we study the driver for, and test the impacts of, collaboration-specific investment and organizational learning on collaboration performance. Our findings indicate that resource similarity between the collaborative partners affects the level of collaboration-specific investment and learning, and demonstrate an approach that firms can use to obtain both abnormal common and private benefits from participation in an interorganizational collaboration. Specifically, the findings suggest that collaboration-specific investment has a direct effect on the enhancement of absorptive capacity and attainment of common and private collaboration benefits. Furthermore, due to the direct effect of absorptive capacity on attainment of collaboration benefits, commitment of collaboration-specific investment has an indirect effect on the attainment of common and private collaboration benefits. This study is the first to apply both the competence-capability framework and extended resource-based view to study interorganizational collaboration. In fact, this study aims to determine mechanisms for a collaboration-participating firm to obtain more benefit, whether common or private. Our findings provide support for the importance of learning capability as a factor in the acquisition of collaboration benefits.     

Does a central clearing counterparty reduce liquidity needs?

This study investigates whether and how central clearing influences the overall liquidity needs in a network of financial obligations. Utilizing the approach of flow network theory, we show that the effect of adding a central clearing counterparty (CCP) is decomposed into two effects: central routing, and central netting effects. Each effect can produce different liquidity needs according to different liquidity scenarios. The analysis indicates that adding a CCP in times of financial distress successfully reduces the overall liquidity needs if and only if the netting efficiency of the CCP is sufficiently high. Furthermore, once the economy is no longer in financial distress, higher netting efficiency of the CCP could conversely increase the overall liquidity needs. The results have implications for the effectiveness of CCPs in mitigating systemic risk in times of financial distress, and their operating costs once the distress has passed.     

Cognitive flexibility and adaptive decision‐making: Evidence from a laboratory study of expert decision makers

Research Summary : How can strategic decision makers overcome inertia when dealing with change? In this article we argue that cognitive flexibility (i.e., the ability to match the type of cognitive processing with the type of problem at hand) enables decision makers to achieve significantly higher decision‐making performance. We show that superior decision‐making performance is associated with using semiautomatic Type 1 cognitive processes when faced with well‐structured problems, and more deliberative Type 2 processes when faced with ill‐structured problems. Our findings shed light on the individual‐level mechanism behind organizational adaptation and complement recent work on strategic inertia. In addition, our findings extend management studies that have stressed the relevance of cognitive flexibility for responding to the demands of increasingly open, flexible, and rapidly changing organizations. Managerial Summary : Humans are creatures of habits. We tend to prefer known courses of action over new ones. In many cases, habits are good. However, when things change in unpredictable ways, the past may not be good guidance for the future. We argue that “cognitive flexibility”—the ability of understanding when to rely on habits vs. when to explore new courses of action—enables managers to switch from a “fast” decision mode, based on habits, to a “slow,” more deliberate decision mode that facilitates the exploration of new courses of action. Managers high in cognitive flexibility reflect on the situation at hand, recognize and value diversity in viewpoints, and integrate such diversity in their own decision processes. By valuing diversity, they are more likely to overcome inertia.     

Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring

In this paper, a structural analysis of hybrid censoring models is presented. This new modularization approach to hybrid censoring models enables a convenient derivation of distributional results. For instance, it allows to derive the exact distribution of the MLEs under an exponential assumption for very complex hybrid scenarios. In order to illustrate the benefit of this idea, we apply it to four new unified progressive hybrid censoring schemes. They are extensions of already proposed unified Type-I/II/III/IV hybrid censoring schemes to progressively Type-II censored data. The resulting analysis shows that the modularization approach provides a powerful, efficient, and elegant tool to study even more complex hybrid censoring models.     

Inference for the two-parameter bathtub-shaped distribution based on record data

Here we consider the record data from the two-parameter of bathtub-shaped distribution. First, we develop simplified forms for the single moments, variances and covariance of records. These distributional properties are quite useful in obtaining the best linear unbiased estimators of the location and scale parameters which can be included in the model. The estimation of the unknown shape parameters and prediction of the future unobserved records based on some observed ones are discussed. Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems. The likelihood method, moment based method, bootstrap methods as well as the Bayesian sampling techniques are applied for the inference problems. The point predictors and credible intervals of future record values based on an informative set of records can be developed. Monte Carlo simulations are performed to compare the so developed methods and one real data set is analyzed for illustrative purposes.     

Stochastic comparisons of coherent systems

The study of stochastic comparisons of coherent systems with different structures is a relevant topic in reliability theory. Several results have been obtained for specific distributions. The present paper is focused on distribution-free comparisons, that is, orderings which do not depend on the component distributions. Different assumptions for the component lifetimes are considered which lead us to different comparison techniques. Thus, if the components are independent and identically distributed (IID) or exchangeable, the orderings are obtained by using signatures. If they are just ID (homogeneous components), then ordering results for distorted distributions are used. In the general case or in the case of independent (heterogeneous) components, a similar technique based on generalized distorted distributions is applied. In these cases, the ordering results may depend on the copula used to model the dependence between the component lifetimes. Some illustrative examples are included in each case.     

The multiple filter test for change point detection in time series

A framework for the detection of change points in the expectation in sequences of random variables is presented. Specifically, we investigate time series with general distributional assumptions that may show an unknown number of change points in the expectation occurring on multiple time scales and that may also contain change points in other parameters. To that end we propose a multiple filter test (MFT) that tests the null hypothesis of constant expectation and, in case of rejection of the null hypothesis, an algorithm that estimates the change points.The MFT has three important benefits. First, it allows for general distributional assumptions in the underlying model, assuming piecewise sequences of i.i.d. random variables, where also relaxations with regard to identical distribution or independence are possible. Second, it uses a MOSUM type statistic and an asymptotic setting in which the MOSUM process converges weakly to a functional of a Brownian motion which is then used to simulate the rejection threshold of the statistical test. This approach enables a simultaneous application of multiple MOSUM processes which improves the detection of change points that occur on different time scales. Third, we also show that the method is practically robust against changes in other distributional parameters such as the variance or higher order moments which might occur with or even without a change in expectation. A function implementing the described test and change point estimation is available in the R package MFT.