New Economics Papers
on Financial Markets
Issue of 2014‒02‒02
seven papers chosen by

  1. Self-affinity in financial asset returns By John Goddard; Enrico Onali
  2. Modeling Credit Spreads Using Nonlinear Regression By Radoslava Mirkov; Thomas Maul; Ronald Hochreiter; Holger Thomae
  3. Comparison of Model for Pricing Volatility Swaps By Néstor Romero
  4. Interconnected risk contributions: an heavy-tail approach to analyse US financial sectors By M. Bernardi; L. Petrella
  5. Shift-Volatility Transmission in East Asian Equity Markets By Marcel Aloy; Gilles de Truchis; Gilles Dufrénot; Benjamin Keddad
  6. The Impact of the French Securities Transaction Tax on Market Liquidity and Volatility By Gunther CAPELLE-BLANCARD; HAVRYLCHYK, Olena
  7. Modelling Stock Return Volatility Dynamics in Selected African Markets By Daniel King and Ferdi Botha

  1. By: John Goddard; Enrico Onali
    Abstract: We test for departures from normal and independent and identically distributed (NIID) returns, when returns under the alternative hypothesis are self-affine. Self-affine returns are either fractionally integrated and long-range dependent, or drawn randomly from an L-stable distribution with infinite higher-order moments. The finite sample performance of estimators of the two forms of self-affinity is explored in a simulation study which demonstrates that, unlike rescaled range analysis and other conventional estimation methods, the variant of fluctuation analysis that considers finite sample moments only is able to identify either form of self-affinity. However, when returns are self-affine and long-range dependent under the alternative hypothesis, rescaled range analysis has greater power than fluctuation analysis. The finite-sample properties of the estimators when returns exhibit either form of self-affinity can be exploited to determine the source of self-affinity in empirical returns data. The techniques are illustrated by means of an analysis of the fractal properties of the daily logarithmic returns for the indices of 11 stock markets.
    Date: 2014–01
  2. By: Radoslava Mirkov; Thomas Maul; Ronald Hochreiter; Holger Thomae
    Abstract: The term structure of credit spreads is studied with an aim to predict its future movements. A completely new approach to tackle this problem is presented, which utilizes nonlinear parametric models. The Brain-Cousens regression model with five parameters is chosen to describe the term structure of credit spreads. Further, we investigate the dependence of the parameter changes over time and the determinants of credit spreads.
    Date: 2014–01
  3. By: Néstor Romero
    Abstract: The popularity of volatility derivatives has increased through these years of financial turmoil. In particular, variance and volatility swap seem interesting to analyse due to its growing trading volume. Hence, the aim of this work is to present a full revision of these two volatility derivatives, comparing pricing methodologies, like Taylor expansion and Heston (1993) volatility process. In addition, there will be a complete section dedicated to the study of the volatility skew and the wings or “smile”. The results showed that the Taylor expansion has a reasonable level of convergence at some values of the parameters of the volatility dynamics, though the findings concluded that higher order of this expansion yielded poor results than the lower order. In the other hand, the smile and the volatility skew showed that the former may change the final value of the fair volatility strike, whereas the latter has almost null impact on this one.
    Date: 2013–12
  4. By: M. Bernardi; L. Petrella
    Abstract: In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply the multivariate student-t Markov Switching model and the Multiple-CoVaR (CoES) risk measures introduced in Bernardi et. al. (2013b) to account for both the known stylised characteristics of the data and the contemporaneous joint distress events affecting financial sectors. Our empirical investigation finds that banks appear to be the major source of risk for all the remaining sectors, followed by the financial services and the insurance sectors, showing that insurance sector significantly contributes as well to the overall risk. Moreover, we find that the role of each sector in contributing to other sectors distress evolves over time accordingly to the current predominant financial condition, implying different interconnection strength.
    Date: 2014–01
  5. By: Marcel Aloy (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS); Gilles de Truchis (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS); Gilles Dufrénot (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS); Benjamin Keddad (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)
    Abstract: This paper attempts to provide evidence of “shift-volatility” transmission in the East Asian equity markets. By shift-volatility, we mean the volatility shifts from a low level to a high level, corresponding respectively to tranquil and crisis periods. We examine the interdependence of equity volatilities between Hong-Kong, Indonesia, Japan, Malaysia, the Philippines, Singapore, Thailand and the United States. Our main issue is whether shift-volatility needs to be considered as a regional phenomenon, or from a more global perspective. We find that the timing/spans of high volatility regimes correspond adequately to years historically documented as those of crises (the Asian crisis and the years following the 2008 crisis). Moreover, we suggest different indicators that could be useful to guide the investors in their arbitrage behavior in the different regimes: the duration of each state, the sensitivity of the volatility in a market following a change in the volatility in another market. Finally, we are able to identify which market can be considered as leading markets in terms of volatility.
    Keywords: Regime shifts, Equity Volatility, East Asia, TVPMS
    JEL: R31 G15 C32
    Date: 2014–01
    Abstract: In this paper, we assess the impact of the securities transaction tax (STT) introduced in France in 2012 on market liquidity and volatility. To identify causality, we rely on the unique design of this tax that is imposed only on large French firms, all of which are listed on Euronext. This provides two reliable control groups (smaller French firms and foreign firms also listed on Euronext) and allows using difference-in-difference methodology to isolate the impact of the tax from other economic changes occurring simultaneously. We find that the STT has reduced trading volume, but we find no effect on theoretically based measures of liquidity, such as price impact, and no significant effect on volatility. The results are robust if we rely on different control groups (German stocks included in the DAX and MDAX indexes), analyze dynamic effects, or construct a control group by propensity score matching.
    Date: 2014–01
  7. By: Daniel King and Ferdi Botha
    Abstract: This paper examines whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the paper, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. Accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important in capturing heteroscedasticity. However, those univariate models including a GARCH term perform comparatively poorly when used for forecasting purposes. In the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns relative to portfolios constructed using the more conventional single-state models. While there is evidence that some Markov-switching models can provide better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture heteroscedasticity in the data remains problematic.
    Keywords: Stock returns, volatility, GARCH, Africa
    JEL: C52 C58
    Date: 2014

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.