The interdependence, dynamics and riskiness of financial institutions are the key features frequently tackled in financial econometrics. We propose a Tail Event driven Network Quantile Regression (TENQR) model which addresses these three aspects. More precisely, our framework captures the risk...

Data Science looks at raw numbers and informational objects created by different disciplines. The Digital Society creates information and numbers from many scientiHic disciplines. The amassment of data though makes is hard to Hind structures and requires a skill full analysis of this massive raw...

This paper contributes to model the industry interconnecting structure in a network context. General predictive model (Rapach et al. 2016) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry interdependency networks. Empirical results show a...

The JEL classification system is a standard way of assigning key topics to economic articles in order to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal...

Systemic risk quantification in the current literature is concentrated on market-based methods such as CoVaR(Adrian and Brunnermeier (2016)). Although it is easily implemented, the interactions among the variables of interest and their joint distribution are less addressed. To quantify systemic...

The CRIX (CRyptocurrency IndeX) has been constructed based on a number of cryptos and provides a high coverage of market liquidity, hu.berlin/crix. The crypto currency market is a new asset market and attracts a lot of investors recently. Surprisingly a market for contingent claims hat not been...

We distill sentiment from a huge assortment of NASDAQ news articles by means of machine learning methods and examine its predictive power in single-stock option markets and equity markets. We provide evidence that single-stock options react to contemporaneous sentiment. Next, examining return...

News move markets and contains incremental information about stock reactions. Future trading volumes, volatility and returns are a ected by sentiments of texts and opinions expressed in articles. Earlier work of sentiment distillation of stock news suggests that risk prole reactions might differ...

We study investor sentiment on a non-classical asset, cryptocurrencies using a “cryptospecificlexicon” recently proposed in Chen et al. (2018) and statistical learning methods.We account for context-specific information and word similarity by learning word embeddingsvia neural network-based...

We distill tone from a huge assortment of NASDAQ articles to examine the predictive power of media-expressed tone in single-stock option markets and equity markets. We find that (1) option markets are impacted by media tone; (2) option variables predict stock returns along with tone; (3) option...