Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Sentiment Series

__Abstract__ This paper features an analysis of the relationship between the volatility of the Dow Jones Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacic). The expansion of on-line financial news sources, such as internet news and social media sources, provides instantaneous access to financial news. Commercial agencies have started developing their own filtered financial news feeds, which are used by investors and traders to support their algorithmic trading strategies. In this paper we use a sentiment series, developed by TRNA, to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. A variety of forms of this measure, namely basic scores, absolute values of the series, squared values of the series, and the first differences of the series, are used to estimate three standard volatility models, namely GARCH, EGARCH and GJR. We use these alternative daily DJIA market sentiment scores to examine the relationship between financial news sentiment scores and the volatility of the DJIA return series. We demonstrate how this calibration of machine filtered news can improve volatility measures

An entropy based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

This paper features an analysis of the relationship between the DOW JONES Industrial Average Index (DJIA) and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA)1 provided by SIRCA (The Securities Industry Research Centre of the Asia Pacic). The recent growth in the availability of on-line financial news sources such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. Thomson Reuters News Analytics (TRNA)2 is one such data set. In this study we use the TRNA data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index componen

A Capital Adequacy Buffer Model

__Abstract__ In this paper, we develop a new capital adequacy buffer model (CABM) which is sensitive to dynamic economic circumstances. The model, which measures additional bank capital required to compensate for fluctuating credit risk, is a novel combination of the Merton structural model which measures distance to default and the timeless capital asset pricing model (CAPM) which measures additional returns to compensate for additional share price risk

Risk Measurement and Risk Modelling using Applications of Vine Copulas

__abstract__ This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite nancial risk. Copula-based dependence modelling is a popular tool in nancial applications, but is usuall

Nonparametric Multiple Change Point Analysis of the Global Financial Crisis

This paper presents an application of a recently developed approach by Matteson and James (2012) for the analysis of change points in a data set, namely major financial market indices converted to financial return series. The general problem concerns the inference of a change in the distribution of a set of time-ordered variables. The approach involves the nonparametric estimation of both the number of change points and the positions at which they occur. The approach is general and does not involve assumptions about the nature of the distributions involved or the type of change beyond the assumption of the existence of the α absolute moment, for some α ε (0,2). The estimation procedure is based on hierarchical clustering and the application of both divisive and agglomerative algorithms. The method is used to evaluate the impact of the Global Financial Crisis (GFC) on the US, French, German, UK, Japanese and Chinese markets, as represented by the S&P500, CAC, DAX, FTSE All Share, Nikkei 225 and Shanghai A share Indices, respectively, from 2003 to 2013. The approach is used to explore the timing and number of change points in the datasets corresponding to the GFC and subsequent European Debt Crisis

Multicomponent theory of buoyancy instabilities in magnetized plasmas: The case of magnetic field parallel to gravity

We investigate electromagnetic buoyancy instabilities of the electron-ion plasma with the heat flux based on not the magnetohydrodynamic (MHD) equations, but using the multicomponent plasma approach when the momentum equations are solved for each species. We consider a geometry in which the background magnetic field, gravity, and stratification are directed along one axis. The nonzero background electron thermal flux is taken into account. Collisions between electrons and ions are included in the momentum equations. No simplifications usual for the one-fluid MHD-approach in studying these instabilities are used. We derive a simple dispersion relation, which shows that the thermal flux perturbation generally stabilizes an instability for the geometry under consideration. This result contradicts to conclusion obtained in the MHD-approach. We show that the reason of this contradiction is the simplified assumptions used in the MHD analysis of buoyancy instabilities and the role of the longitudinal electric field perturbation which is not captured by the ideal MHD equations. Our dispersion relation also shows that the medium with the electron thermal flux can be unstable, if the temperature gradients of ions and electrons have the opposite signs. The results obtained can be applied to the weakly collisional magnetized plasma objects in laboratory and astrophysics.Comment: Accepted for publication in Astrophysics & Space Scienc

Volatility Spillovers from the US to Australia and China across the GFC

This paper features an analysis of volatility spillover eects from the US market, represented by the S&P500 index to the Australian capital market as represented by the Australian S&P200 for a period running from 12th September 2002 to 9th September 2012. This captures the impact of the Global Financial Crisis (GFC). The GARCH analysis features an exploration of whether there are any spillover eects in the mean equations as well as in the variance equations. We adopt a bi-mean equation to model the conditional mean in the Australian markets plus an ARMA model to capture volatility spillovers from the US. We also apply a Markov Switching GARCH model to explore the existence of regime changes during this period and we also explore the non-constancy of correlations between the markets and apply a moving window of 120 days of daily observations to explore time-varying conditional and tted correlations. There appears to be strong evidence of regime switching behaviour in the Australian market and changes in correlations between the two markets particularly in the period of the GFC. We also apply a tri-variate Cholesky-GARCH model to include potential eects from the Chinese market, as represented by the Hang Seng Inde

A Capital Adequacy Buffer Model

__Abstract__ In this paper, we develop a new capital adequacy buffer model (CABM) which is sensitive to dynamic economic circumstances. The model, which measures additional bank capital required to compensate for fluctuating credit risk, is a novel combination of the Merton structural model which measures distance to default and the timeless capital asset pricing model (CAPM) which measures additional returns to compensate for additional share price risk

Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non- linear models, including smoot

Volatility Spillover and Multivariate Volatility Impulse Response Analysis of GFC News Events

This paper applies two measures to assess spillovers across markets: the Diebold Yilmaz (2012) Spillover Index and the Hafner and Herwartz (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility estimates taken from the Oxford Man RV library, running from the beginning of 2000 to Octobe