Ricci curvature: An economic indicator for market fragility and systemic risk

Romeil S. Sandhu; Tryphon T. Georgiou; Allen R. Tannenbaum

doi:10.1126/sciadv.1501495

Ricci curvature: An economic indicator for market fragility and systemic risk

Science Advances ◽

10.1126/sciadv.1501495 ◽

2016 ◽

Vol 2 (5) ◽

pp. e1501495 ◽

Cited By ~ 32

Author(s):

Romeil S. Sandhu ◽

Tryphon T. Georgiou ◽

Allen R. Tannenbaum

Keyword(s):

Stock Returns ◽

Ricci Curvature ◽

Systemic Risk ◽

Economic Indicator ◽

System Level ◽

Geometric Feature ◽

Financial Systems ◽

Standard And Poor's ◽

High Level ◽

Business As Usual

Quantifying the systemic risk and fragility of financial systems is of vital importance in analyzing market efficiency, deciding on portfolio allocation, and containing financial contagions. At a high level, financial systems may be represented as weighted graphs that characterize the complex web of interacting agents and information flow (for example, debt, stock returns, and shareholder ownership). Such a representation often turns out to provide keen insights. We show that fragility is a system-level characteristic of “business-as-usual” market behavior and that financial crashes are invariably preceded by system-level changes in robustness. This was done by leveraging previous work, which suggests that Ricci curvature, a key geometric feature of a given network, is negatively correlated to increases in network fragility. To illustrate this insight, we examine daily returns from a set of stocks comprising the Standard and Poor’s 500 (S&P 500) over a 15-year span to highlight the fact that corresponding changes in Ricci curvature constitute a financial “crash hallmark.” This work lays the foundation of understanding how to design (banking) systems and policy regulations in a manner that can combat financial instabilities exposed during the 2007–2008 crisis.

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Network geometry and market instability

Royal Society Open Science ◽

10.1098/rsos.201734 ◽

2021 ◽

Vol 8 (2) ◽

pp. 201734

Author(s):

Areejit Samal ◽

Hirdesh K. Pharasi ◽

Sarath Jyotsna Ramaia ◽

Harish Kannan ◽

Emil Saucan ◽

...

Keyword(s):

Financial Markets ◽

Ricci Curvature ◽

Stock Prices ◽

System Level ◽

Financial Systems ◽

Major Market ◽

Pairwise Interactions ◽

The Usa ◽

Market Instability ◽

Business As Usual

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier–Ricci curvature and Forman–Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or ‘business-as-usual’ periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.

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Average Conditional Volatility: A Measure Of Systemic Risk For Commercial Banks

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v9i2.1819 ◽

2011 ◽

Vol 9 (2) ◽

Author(s):

Harikumar Sankaran ◽

Manish Saxena ◽

Christopher A. Erickson

Keyword(s):

Stock Returns ◽

Systemic Risk ◽

Equity Capital ◽

Capital Adequacy ◽

Probability Of Default ◽

Conditional Volatility ◽

Cross Sectional ◽

Bank Stock Returns ◽

Bank Stock ◽

High Level

We propose using the cross-sectional (daily) average conditional volatility of commercial bank stock returns as a measure of systemic risk for the U.S. banking industry. The performance of this measure is tested using data from the 2008 pre-crisis period. The measure is shown to incorporate individual bank risk as well as the cumulative riskiness of a cross-section of banks. Cross-sectional regressions indicate that individual bank’s probability of default is unrelated to the bank’s conditional volatility during times of low, industry wide risk (as measured by average conditional volatility). However, the bank’s conditional volatility significantly affects its probability of default when the industry is experiencing a high level risk. Regardless of the industry level risk, a bank’s probability of default has a significant negative relation with its capital adequacy (as measured by the proportion of equity capital). Additionally, at an aggregate level, Granger causality tests indicate that the conditional volatility of ‘big’ banks causes the riskiness of medium and small banks to increase.

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Wealth, Stock Returns, Government Bond Yields, and Systemic Risk

SSRN Electronic Journal ◽

10.2139/ssrn.1663638 ◽

2010 ◽

Author(s):

Ricardo M. Sousa

Keyword(s):

Stock Returns ◽

Systemic Risk ◽

Government Bond ◽

Bond Yields

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Aircraft Requirements for Sustainable Regional Aviation

Aerospace ◽

10.3390/aerospace8030061 ◽

2021 ◽

Vol 8 (3) ◽

pp. 61

Author(s):

Dominik Eisenhut ◽

Nicolas Moebs ◽

Evert Windels ◽

Dominique Bergmann ◽

Ingmar Geiß ◽

...

Keyword(s):

Systems Engineering ◽

System Level ◽

Policy Initiative ◽

The European Union ◽

Calculation Methods ◽

Figures Of Merit ◽

Performance Requirements ◽

High Level ◽

Different Levels ◽

The Eu

Recently, the new Green Deal policy initiative was presented by the European Union. The EU aims to achieve a sustainable future and be the first climate-neutral continent by 2050. It targets all of the continent’s industries, meaning aviation must contribute to these changes as well. By employing a systems engineering approach, this high-level task can be split into different levels to get from the vision to the relevant system or product itself. Part of this iterative process involves the aircraft requirements, which make the goals more achievable on the system level and allow validation of whether the designed systems fulfill these requirements. Within this work, the top-level aircraft requirements (TLARs) for a hybrid-electric regional aircraft for up to 50 passengers are presented. Apart from performance requirements, other requirements, like environmental ones, are also included. To check whether these requirements are fulfilled, different reference missions were defined which challenge various extremes within the requirements. Furthermore, figures of merit are established, providing a way of validating and comparing different aircraft designs. The modular structure of these aircraft designs ensures the possibility of evaluating different architectures and adapting these figures if necessary. Moreover, different criteria can be accounted for, or their calculation methods or weighting can be changed.

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Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia

Psychological Medicine ◽

10.1017/s0033291721002129 ◽

2021 ◽

pp. 1-14

Author(s):

Debo Dong ◽

Dezhong Yao ◽

Yulin Wang ◽

Seok-Jun Hong ◽

Sarah Genon ◽

...

Keyword(s):

Sensory Information ◽

Resting State Fmri ◽

Brain Regions ◽

High Order ◽

Hierarchical Organization ◽

System Level ◽

Integrative System ◽

Sensorimotor Region ◽

Cortical Hierarchy ◽

High Level

Abstract Background Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Methods We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). Results We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). Conclusions The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

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Idiosyncratic volatility, option-based measures of informed trading, and investor attention

Review of Derivatives Research ◽

10.1007/s11147-021-09175-7 ◽

2021 ◽

Author(s):

Hannes Mohrschladt ◽

Judith C. Schneider

Keyword(s):

Stock Returns ◽

Idiosyncratic Volatility ◽

Investor Attention ◽

Intuitive Idea ◽

Joint Examination ◽

Stock Market Investors ◽

Private Investors ◽

High Level ◽

Do So ◽

Option Market

AbstractWe establish a direct link between sophisticated investors in the option market, private stock market investors, and the idiosyncratic volatility (IVol) puzzle. To do so, we employ three option-based volatility spreads and attention data from Google Trends. In line with the IVol puzzle, the volatility spreads indicate that sophisticated investors indeed consider high-IVol stocks as being overvalued. Moreover, the option measures help to distinguish overpriced from fairly priced high-IVol stocks. Thus, these measures are able to predict the IVol puzzle’s magnitude in the cross-section of stock returns. Further, we link the origin of the IVol puzzle to the trading activity of irrational private investors as the return predictability only exists among stocks that receive a high level of private investor attention. Overall, our joint examination of option and stock markets sheds light on the behavior of different investor groups and their contribution to the IVol puzzle. Thereby, our analyses support the intuitive idea that noise trading leads to mispricing, which is identified by sophisticated investors and exploited in the option market.

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Dynamic modelling of a servo self-pierce riveting (SPR) process

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/09544054211037493 ◽

2021 ◽

pp. 095440542110374

Author(s):

Daniel Tang ◽

Mike Evans ◽

Paul Briskham ◽

Luca Susmel ◽

Neil Sims

Keyword(s):

Dynamic Modelling ◽

System Level ◽

Optimum Process ◽

Current Limit ◽

Level Model ◽

System Level Model ◽

Head Height ◽

Extensive Experimental Data ◽

High Level

Self-pierce riveting (SPR) is a complex joining process where multiple layers of material are joined by creating a mechanical interlock via the simultaneous deformation of the inserted rivet and surrounding material. Due to the large number of variables which influence the resulting joint, finding the optimum process parameters has traditionally posed a challenge in the design of the process. Furthermore, there is a gap in knowledge regarding how changes made to the system may affect the produced joint. In this paper, a new system-level model of an inertia-based SPR system is proposed, consisting of a physics-based model of the riveting machine and an empirically-derived model of the joint. Model predictions are validated against extensive experimental data for multiple sets of input conditions, defined by the setting velocity, motor current limit and support frame type. The dynamics of the system and resulting head height of the joint are predicted to a high level of accuracy. Via a model-based case study, changes to the system are identified, which enable either the cycle time or energy consumption to be substantially reduced without compromising the overall quality of the produced joint. The predictive capabilities of the model may be leveraged to reduce the costs involved in the design and validation of SPR systems and processes.

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