New Economics Papers
on Risk Management
Issue of 2008‒08‒21
six papers chosen by

  1. Risk Analysis in Investment Appraisal By Savvides, Savvakis C.
  2. The Jump component of S&P 500 volatility and the VIX index By Ralf Becker; Adam Clements; Andrew McClelland
  3. Zero variance in Markov chain Monte Carlo with an application to credit risk estimation By Tenconi Paolo
  4. The option-iPoD. The Probability of Default Implied by Option Prices based on Entropy By Christian Capuano
  5. Do high-frequency measures of volatility improve forecasts of return distributions? By John M Maheu; Thomas H McCurdy
  6. Momentum in Australian Stock Returns: An Update By A. S. Hurn; V.Pavlov

  1. By: Savvides, Savvakis C.
    Abstract: The methodology and uses of Monte-Carlo simulation technique are presented as applied to the analysis and assessment of risk in the evaluation of investment projects. The importance of risk analysis in investment appraisal is highlighted and the stages in the process introduced. The results generated by a risk analysis application are interpreted, including the investment decision criteria and measures of risk based on the expected value concept. Conclusions are drawn regarding the usefulness and limitations of risk analysis in investment appraisal.
    Keywords: risk analysis; investment appraisal; Monte Carlo simulation; project evaluation; measures of risk; investment decision criteria
    JEL: G31 G32 M21
    Date: 1994–03
  2. By: Ralf Becker; Adam Clements; Andrew McClelland
    Abstract: Much research has investigated the differences between option implied volatilities and econometric model-based forecasts in terms of forecast accuracy and relative informational content. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing to generate forecasts. Therefore, implied volatility has the potential to reflect information that a model-based forecast could not. Specifically, this paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information relative to model based forecasts pertaining to future jumps. It is found that the VIX index both subsumes information relating to past jump contributions to volatility and reflects incremental information pertaining to future jump activity, relative to modelbased forecasts. This is an issue that has not been examined previously in the literature and expands our understanding of how option markets form their volatility forecasts.
    Keywords: Implied volatility, VIX, volatility forecasts, informational efficiency, jumps
    JEL: C12 C22 G00 G14
    Date: 2008–03–17
  3. By: Tenconi Paolo (Department of Economics, University of Insubria, Italy)
    Abstract: We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics lit- erature by Assaraf and Caarel ( 1999). The potential of the new idea is illustrated with some toy examples and a real application to Bayesian inference for credit risk estimation.
    Keywords: Markov chain Monte Carlo, Metropolis-Hastings algorithm, Variance re- duction, Zero-Variance principle.
    Date: 2008–04
  4. By: Christian Capuano
    Abstract: We present a framework to derive the probability of default implied by the price of equity options. The framework does not require any strong statistical assumption, and provide results that are informative on the expected developments of balance sheet variables, such as assets, equity and leverage, and on the Greek letters (delta, gamma and vega). We show how to extend the framework by using information from the price of a zero-coupon bond and CDS-spreads. In the episode of the collapse of Bear Stearns, option-iPoD was able to early signal market sentiment.
    Date: 2008–08–08
  5. By: John M Maheu; Thomas H McCurdy
    Abstract: Many finance questions require a full characterization of the distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns.
    Keywords: RV, multiperiod, out-of-sample, term structure of density forecasts, observable SV
    JEL: C1 C50 C32 G1
    Date: 2008–08–06
  6. By: A. S. Hurn; V.Pavlov
    Abstract: It has been documented that a momentum investment strategy based on buying past well performing stocks while selling past losing stocks, is a profitable one in the Australian context particularly in the 1990s. The aim of this short paper is to investigate whether or not this feature of Australian stock returns is still evident. The paper confirms the presence of a medium-term momentum effect, but also provides some interesting new evidence on the importance of the size effect on momentum.
    Keywords: Stock returns, Momentum portfolios, Size effect
    JEL: G11 G12
    Date: 2008–02–26

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