nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒08‒13
four papers chosen by

  1. Does time-variation matter in the stochastic volatility components for G7 stock returns By Afees A. Salisu; Ahamuefula Ephraim Ogbonna
  2. When the market drives you crazy: Stock market returns and fatal car accidents By Corrado Giulietti; Mirco Tonin; Michael Vlassopoulos
  3. Directed Continuous-Time Random Walk with memory By Jaros{\l}aw Klamut; Tomasz Gubiec
  4. Transition drivers and crisis signaling in stock markets By Spelta, Alessandro; Pecora, Nicolò; Flori, Andrea; Pammolli, Fabio

  1. By: Afees A. Salisu (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam Centre for Econometric and Allied Research, University of Ibadan); Ahamuefula Ephraim Ogbonna (Centre for Econometric and Allied Research, University of Ibadan Department of Statistics, University of Ibadan, Ibadan, Nigeria)
    Abstract: This study empirically tests for time variation in the stochastic volatility (SV) components for the G7 stock returns. The time variation in both trend and transitory components of the SV is tested separately and jointly using the unobserved component model and following the approach developed by Chan (2018). Consequently, the computed Bayes factor obtained from the SavageDickey density ratio, which circumvents the computation of marginal likelihood, is used to adjudge the performance of each restricted time varying stochastic volatility model without the trend and transitory components against the unrestricted model that allows for same. The empirical evidence supports time variation in the transitory component of SV while the trend component is found to be relatively constant over time. These empirical estimates are not sensitive to data frequency.
    Keywords: Bayesian; Bayes factor; Transitory component; Trend component; Unobserved Component Model
    JEL: C11 C32 C53 E37 G17
    Date: 2018–07
  2. By: Corrado Giulietti (University of Southampton; Centre for Population Change; Global Labor Organization); Mirco Tonin (Free University of Bolzano‐Bozen, CESifo; Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University); Michael Vlassopoulos (University of Southampton; IZA)
    Abstract: The stock market in uences some of the most fundamental economic decisions of investors, such as consumption, saving, and labor supply, through the financial wealth channel. This paper provides evidence that daily uctuations in the stock market have important-and hitherto neglected-spillover effects in another, unrelated domain, namely driving. Using the universe of fatal road car accidents in the United States from 1990 to 2015, we find that a one standard deviation reduction in daily stock market returns is associated with a 0.5% increase in the number of fatal accidents. A battery of falsification tests support a causal interpretation of this finding. Our results are consistent with immediate emotions stirred by a negative stock market performance in uencing the number of fatal accidents, in particular among inexperienced investors, thus highlighting the broader economic and social consequences of stock market uctuations.
    Keywords: Stock market, Car accidents, Emotions
    JEL: D91 R41
    Date: 2018–07
  3. By: Jaros{\l}aw Klamut; Tomasz Gubiec
    Abstract: We propose a new Directed Continuous-Time Random Walk (CTRW) model with memory. As CTRW trajectory consists of spatial jumps preceded by waiting times, in Directed CTRW, we consider the case with only positive spatial jumps. Moreover, we consider the memory in the model as each spatial jump depends on the previous one. Our model is motivated by the financial application of the CTRW presented in [Phys. Rev. E 82:046119][Eur. Phys. J. B 90:50]. As CTRW can successfully describe the short term negative autocorrelation of returns in high-frequency financial data (caused by the bid-ask bounce phenomena), we asked ourselves to what extent the observed long-term autocorrelation of absolute values of returns can be explained by the same phenomena. It turned out that the bid-ask bounce can be responsible only for the small fraction of the memory observed in the high-frequency financial data.
    Date: 2018–07
  4. By: Spelta, Alessandro; Pecora, Nicolò; Flori, Andrea; Pammolli, Fabio
    Abstract: The present paper introduces an up-to-date methodology to detect Early Warning Signals of critical transitions, that manifest when distress stages in financial markets are about to take place. As a first step, we demonstrate that a high-dimensional dynamical system can be formulated in a simpler form but in an abstract phase space. Then we detect its approaching towards a critical transition by means of a set of observable variables that exhibit some particular statistical features. We name these variables the Leading Temporal Module. The impactful change in the properties of this group reflects the transition of the system from a normal to a distress state. Starting from these observations we develop an early warning indicator for determining the proximity of a financial crisis. The proposed measure is model free and the application to three different stock markets, together with the comparison with alternative systemic risk measures, highlights the usefulness in signaling upcoming distress phases. Computational results establish that the methodology we propose is effective and it may constitute a relevant decision support mechanism for macro prudential policies.
    Keywords: Financial Crisis, Early Warning Signals, Critical Transition, Leading Temporal Module
    JEL: C02 C53 E37 G01 G17
    Date: 2018–07–23

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.