What derives the bond portfolio value-at-risk: Information roles of macroeconomic and financial stress factors

This paper first develops a new approach, which is based on the Nelson-Siegel term structure factor-augmented model, to compute the VaR of bond portfolios. We then applied the model to examine whether information contained on macroeconomic variables and financial shocks can help to explain the variations of VaR. A principal component analysis is used to incorporate the information contained in different variables. The empirical result shows that, including macroeconomic variables and financial shocks in the Nelson-Siegel term structure factor model, we can observe an obvious tendency towards better VaR forecasting performance. Moreover, the impact of incorporating financial shocks seems to be stronger than that of incorporating macroeconomic variables

Sentiment-induced bubbles in the cryptocurrency market

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect

Data science & digital society

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 material. The thoughts presented here on DS2 - Data Science & Digital Society analyze these challenges and offers ways to handle the questions arising in this evolving context. We propose three levels of analysis and lay out how one can react to the challenges that come about. Concrete examples concern Credit default swaps, Dynamic Topic modeling, Crypto currencies and above all the quantitative analysis of real data in a DS2 context

Deep learning-based cryptocurrency sentiment construction

We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity using efficient language modeling tools such as construction of featurized word representations (embeddings) and recursive neural networks. We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in- and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns and volatility predictability of the cryptocurrency market index

Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries

This paper presents presents presents a fractionally cointegrated vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations between stock returns and downside risk. Evidence from major advanced markets markets markets markets supports the supports the notion that notion that notion that downside risk measured by value value value-at -risk ( risk (VaRVaRVaR) has significant information content content that reflects that reflects that reflects that reflects that reflects lagged long-run variance and higher moments of risk for for predict redict ing stock returns. stock returns. stock returns. stock returns. The e The e vidence vidence vidence supports the positive tradeoff hypothesis and and the leverage effect leverage effect leverage in the long in the long in the long run and and for markets in the short run. We find that US downside risk accounts for 54.36% of price discovery, whereas the whereas the whereas the whereas the own effect from own effect from the country itself only 27.06%

The integration ofcredit default swapmarkets in the pre andpost-subprime crisis incommon stochastic trends

It was evident that credit default swap (CDS) spreads have been highly correlated during the recent financial crisis. Motivated by this evidence, this study attempts to investigate the extent to which CDS markets across regions, maturities and credit ratings have integrated more in crisis. By applying the Panel Analysis of Non-stationarity in Idiosyncratic and Common components method (PANIC) developed by Bai and Ng (2004), we observe a potential shift in CDS integration between the pre- and post-Lehman collapse period, indicating that the system of CDS spreads is tied to a long-run equilibrium path. This finding contributes to a credit risk management task and also coincides with the missions of Basel III since the more integrated CDS markets could result in correlated default, credit contagion and simultaneous downgrading in the future

FRM financial risk meter

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilising tail event information. FRM (Financial Risk Meter) is based on Lasso quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identifies risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. We identify companies exhibiting extreme "co-stress", as well as "activators" of stress. With the SRM@EuroArea, we extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behaviour in a network of financial risk factors

FRM Financial Risk Meter

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilising tail event information. FRM (Financial Risk Meter) is based on Lasso quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identifies risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. We identify companies exhibiting extreme "co-stress", as well as "activators" of stress. With the SRM@EuroArea, we extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behaviour in a network of financial risk factors