SAFE: An early warning system for systemic banking risk

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk, incorporating the structural characteristics of the fi nancial system and a feedback amplification mechanism. The models explain fi nancial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the financial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifi cations that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifications for verification and adjustment of supervisory actions. This paper highlights the application of these models to stress testing, scenario analysis, and policy.Systemic risk ; Liquidity (Economics)

Influencers, are they responsible for Bitcoin's volatility? Transfer entropy and Granger causality in prol of an answer

Bitcoin, like any other cryptocurrency, is subject to fluctuations in price. The volatility of this market can be a reflection of several reasons, such as public opinion, social networks and news. Social networks, in particular Twitter, are increasingly used as an important source of value extraction because through this network, it is possible to find out about news in real-time, follow the repercussions, know what experts in the financial world are commenting or thinking and even decide based on influencer's opinion whether to invest or not. This study investigates the influence that a specific group of people exert on Bitcoin volatility. A selection of influencers from the “crypto world” was made, and through the Twitter API, it was possible to select the tweets of the object of study. To choose the classification model for sentiment analysis, two techniques were compared, one being very popular with a focus on the domain of social networks and the other recently created and focused on finance. From the selected technique, only positive and negative sentiments were considered, and then the daily series of the Sentiment Score was calculated. Next, the causal relationship between Bitcoin and sentiment was investigated using Granger causality and Transfer Entropy tests. Transfer Entropy showed encouraging results, suggesting that there is a transfer of information from Sentiment to Returns and that it is possible for an influencer to contribute to Bitcoin’s volatilityO Bitcoin, assim como qualquer outra criptomoeda, está sujeito a flutuações no preço. A volatilidade desse mercado pode ser reflexo de vários motivos, tais como, opinião pública, redes sociais e notícias. As redes sociais, em particular o Twitter, cada vez mais é utilizado como uma fonte importante de extração de valor, isto porque através desta rede é possível saber das novidades em tempo real, acompanhar as repercussões, saber o que entendedores do mundo financeiro estão a comentar e decidir até mesmo com base na opinião de um influenciador se irá investir ou não. Este estudo investiga a influência que determinadas pessoas exercem sobre a volatilidade do Bitcoin. Foi feita uma seleção de influenciadores do “mundo crypto” e através da API do Twitter foi possível selecionar os tweets de objeto de estudo. Para a escolha do modelo de classificação para análise de sentimento foram comparadas duas técnicas, sendo uma muito popular com foco no domínio de redes sociais e a outra recém-criada e focada em finanças. A partir da técnica selecionada, apenas os sentimentos positivos e negativos foram considerados e então calculada a série diária do Sentiment Score. A seguir foi investigada a relação causal entre o Bitcoin e o sentiment utilizando os testes de causalidade de Granger e Entropia de Transferência. A Entropia de Transferência mostrou resultados animadores que sugerem existir transferência de informação de Sentiment para Returns e que, portanto, é possível que um influencer contribua para a volatilidade do Bitcoin

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

News and Correlations of CEEC-3 Financial Markets

We investigate conditional correlations between six CEEC-3 financial markets estimated by DCC-MGARCH models. In general, the highest correlations exist between Hungary and Poland in foreign exchange and stock markets. Short-term money markets are rather isolated from each other. We find that the associations of CEEC-3 exchange rates versus the euro are weaker than those versus the US dollar. The persistence of the effect of shocks on the timevarying correlations is strongest for foreign exchange and stock markets, indicating a tendency toward contagion. In searching for the origins of financial market volatility in the CEEC-3, we uncover some evidence of Granger-causality on the foreign exchange markets. Finally, using a pool model, we investigate the impact of euro area, US, and CEEC-3 news on the correlations. Apart from ECB monetary policy news, we observe no broad effects of international news on correlations; instead, local news exerts an influence, which suggests adominance of country- or market-specific circumstances.Financial markets, Czech Republic, Hungary, Poland, political news, macroeconomic shocks, contagion, DCC-MGARCH

Is Nigeria Ready for Inflation Targeting?

This paper evaluates whether Nigeria is ready to adopt inflation targeting (IT), a monetary policy framework that several emerging markets have adopted over the last one decade. The paper reviewed literature on selected conditions for successful implementation of IT and then focused on whether one specific precondition of an empirically stable monetary transmission mechanism is tenable. Vector autoregressive (VAR) model was applied using select monetary policy and other macroeconomic variables to explore the various channels using the Granger causality tests, impulse responses, and variance decompositions. Results showed that inflation in Nigeria is impassive to monetary transmission variables in the model. Specifically, weak link between prices and credit and interest rate channels were established. However, evidence of strong inverse link between exchange rate and prices was found in the model. This suggests exchange rate pass-through on the level of prices in the economy. The paper, therefore, recommends the pursuance of IT lite in Nigeria.Inflation targeting, vector autoregressive model, Granger causality test, monetary transmission mechanism, exchange rate pass-through

Modeling dominance effects on nonverbal behaviors using granger causality

In this paper we modeled the effects that dominant people might induce on the nonverbal behavior (speech energy and body motion) of the other meeting participants using Granger causality technique. Our initial hypothesis that more dominant people have generalized higher influence was not validated when using the DOME-AMI corpus as data source. However, from the correlational analysis some interesting patterns emerged: contradicting our initial hypothesis dominant individuals are not accounting for the majority of the causal flow in a social interaction. Moreover, they seem to have more intense causal effects as their causal density was significantly higher. Finally dominant individuals tend to respond to the causal effects more often with complementarity than with mimicry

Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study

Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior. Facial expressions are widely described as being automatic and thus hard to overtly influence. As such, they are a perfect measure for a better understanding of unintentional behavior cues about social-emotional cognitive processes. With this view, this study is concerned with the analysis of the direction of emotional influence in dyadic dialogue based on facial expressions only. We exploit computer vision capabilities along with causal inference theory for quantitative verification of hypotheses on the direction of emotional influence, i.e., causal effect relationships, in dyadic dialogues. We address two main issues. First, in a dyadic dialogue, emotional influence occurs over transient time intervals and with intensity and direction that are variant over time. To this end, we propose a relevant interval selection approach that we use prior to causal inference to identify those transient intervals where causal inference should be applied. Second, we propose to use fine-grained facial expressions that are present when strong distinct facial emotions are not visible. To specify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions over selected relevant intervals. We tested our approach on newly, experimentally obtained data. Based on the quantitative verification of hypotheses on the direction of emotional influence, we were able to show that the proposed approach is most promising to reveal the causal effect pattern in various instructed interaction conditions.Comment: arXiv admin note: text overlap with arXiv:1810.1217