New Economics Papers
on Financial Markets
Issue of 2009‒08‒30
four papers chosen by

  1. Statistical modelling of financial crashes: Rapid growth, illusion of certainty and contagion By John M. Fry
  2. Exploring Time-Varying Jump Intensities: Evidence from S&P500 Returns and Options By Peter Christoffersen; Kris Jacobs; Chayawat Ornthanalai
  3. News Aggregators, Volatility and the Stock Market By Bystöm, Hans
  4. Volatility spillover in Indonesia, USA, and Japan capital market By Mulyadi, Martin Surya

  1. By: John M. Fry
    Abstract: We develop a rational expectations model of financial bubbles and study ways in which a generic risk-return interplay is incorporated into prices. We retain the interpretation of the leading Johansen-Ledoit-Sornette model, namely, that the price must rise prior to a crash in order to compensate a representative investor for the level of risk. This is accompanied, in our stochastic model, by an illusion of certainty as described by a decreasing volatility function. The basic model is then extended to incorporate multivariate bubbles and contagion, non-Gaussian models and models based on stochastic volatility. Only in a stochastic volatility model where the mean of the log-returns is considered fixed does volatility increase prior to a crash.
    Keywords: Financial crashes, super-exponential growth, illusion of certainty, contagion, housing-bubble.
    JEL: C00 E30 G10
    Date: 2009–10–08
  2. By: Peter Christoffersen; Kris Jacobs; Chayawat Ornthanalai
    Abstract: Standard empirical investigations of jump dynamics in returns and volatility are fairly complicated due to the presence of latent continuous-time factors. We present a new discrete-time framework that combines heteroskedastic processes with rich specifications of jumps in returns and volatility. Our models can be estimated with ease using standard maximum likelihood techniques. We provide a tractable risk neutralization framework for this class of models which allows for separate modeling of risk premia for the jump and normal innovations. We anchor our models in the literature by providing continuous time limits of the models. The models are evaluated by fitting a long sample of S&P500 index returns, and by valuing a large sample of options. We find strong empirical support for time-varying jump intensities. A model with jump intensity that is affine in the conditional variance performs particularly well both in return fitting and option valuation. Our implementation allows for multiple jumps per day, and the data indicate support for this model feature, most notably on Black Monday in October 1987. Our results also confirm the importance of jump risk premia for option valuation: jumps cannot significantly improve the performance of option pricing models unless sizeable jump risk premia are present. <P>Les recherches empiriques standards portant sur la dynamique des sauts dans les rendements et dans la volatilité sont plutôt complexes en raison de la présence de facteurs inobservables en temps continu. Nous présentons un nouveau cadre d’étude en temps discret qui combine des processus hétéroscédastiques et des caractéristiques à concentration élevée de sauts dans les rendements et dans la volatilité. Nos modèles peuvent être facilement évalués à l’aide des méthodes standards du maximum de vraisemblance. Nous offrons une démarche souple de neutralisation du risque pour cette catégorie de modèles, ce qui permet de modéliser distinctement les primes de risque liées aux sauts et celles liées aux innovations normales. Nous imbriquons nos modèles dans la littérature en établissant leurs limites en temps continu. Ces derniers sont évalués en intégrant un échantillon de rendements à long terme de l’indice S&P 500 et en évaluant un vaste échantillon d’options. Nous trouvons un solide appui empirique en ce qui a trait aux intensités de sauts variant dans le temps. Un modèle avec intensité de saut affine dans la variance conditionnelle est particulièrement efficace sur les plans de l’ajustement des rendements et de l’évaluation des options. La mise en œuvre de notre modèle permet de multiples sauts par jour et les données appuient cette caractéristique, plus particulièrement en ce qui a trait au lundi noir d’octobre 1987. Nos résultats confirment aussi l’importance des primes liées au risque de sauts pour l’évaluation du prix des options : les sauts ne peuvent contribuer à améliorer considérablement la performance des modèles utilisés pour fixer les prix des options, sauf en présence de primes de risque de sauts assez importantes.
    Keywords: compound Poisson process, option valuation, filtering; volatility jumps, jump risk premia, time-varying jump intensity, heteroskedasticity. , processus composé de Poisson, évaluation du prix des options, filtrage, sauts liés à la volatilité, primes de risque de sauts, intensité des sauts variant dans le temps, hétéroscédasticité.
    JEL: G12
    Date: 2009–08–01
  3. By: Bystöm, Hans (Department of Economics, Lund University)
    Abstract: In this paper we employ the news aggregator GoogleTM News to demonstrate a strong link between the volatility in the stock market and the amount of news available to market participants. The paper also highlights some other areas, in finance and elsewhere, where news aggregators could be useful.
    Keywords: news aggregator; volatility
    JEL: C82 D80 G10
    Date: 2009–08–11
  4. By: Mulyadi, Martin Surya
    Abstract: Globalization and advanced information technology easing us for obtaining information from global stock markets. With that condition, volatility in domestic capital market could be affected by volatility from global stock markets. That concern will be answered in this research, about volatility spillover in Indonesia, USA, and Japan capital market. This research using daily return data from each country indices from January 2004 until December 2008 employing econometric model GARCH (1,1). The result showing us that there is one way volatility spillover between Indonesia and USA (USA effecting Indonesia). Meanwhile, there is bidirectional volatility spillover between Indonesia and Japan (Japan influnced Indonesia, and vice versa).
    Keywords: Volatility; Volatility Spillover; GARCH
    JEL: G14 G15
    Date: 2009–07

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