nep-rmg New Economics Papers
on Risk Management
Issue of 2011‒06‒04
three papers chosen by
Stan Miles
Thompson Rivers University

  1. Adding to the Regulator's Toolbox: Integration and Extension of Two Leading Market Models By Brian Tivnan; Matthew Koehler; Matthew McMahon; Matthew Olson; Neal Rothleder; Rajani Shenoy
  2. Investment risk taking by institutional investors By Janko Gorter; Jacob Bikker
  3. Fact or friction: Jumps at ultra high frequency By Kim Christensen; Roel Oomen; Mark Podolskij

  1. By: Brian Tivnan; Matthew Koehler; Matthew McMahon; Matthew Olson; Neal Rothleder; Rajani Shenoy
    Abstract: As demonstrated during the recent financial crisis, regulators require additional analytical tools to assess systemic risk in the financial sector. This paper describes one such tool; namely a novel market modeling and analysis capability. Our model builds upon two leading market models: one which emphasizes market micro-structure and another which emphasizes an ecology of trading strategies. We address a limitation of market modeling, namely the consideration of only one dominant trading strategy (i.e., long positions). Our model aligns closely with several widely held stylized facts of financial markets. And a final contribution of this work stems from our empirical analysis of the fractal nature of both empirical markets and our market model.
    Date: 2011–05
  2. By: Janko Gorter; Jacob Bikker
    Abstract: This paper is the first that formally compares investment risk taking by pension funds and insurance firms. Using a unique and extended dataset that covers the volatile investment period 1995-2009, we find that, in the Netherlands, insurers take substantially less investment risk than pension funds, even though a market risk capital charge for insurers is yet absent. This result can be explained from financial distress costs, which only insurers face. We also find that institutional investors' risk taking is determined by their risk bearing capacity, where this risk bearing capacity depends on capital, size, reinsurance, underwriting risk and human and financial wealth per pension plan participant. Finally, and in line with the ownership structure hypothesis, stock insurers are found to take significantly more investment risk than mutual insurers.
    Keywords: Portfolio Choice, Insurance Companies, Pension Funds, Ownership Structure
    JEL: G11 G22 G23 G32
    Date: 2011–05
  3. By: Kim Christensen (Aarhus University and CREATES); Roel Oomen (Deutsche Bank, London); Mark Podolskij (University of Heidelberg and CREATES)
    Abstract: In this paper, we demonstrate that jumps in financial asset prices are not nearly as common as generally thought, and that they account for only a very small proportion of total return variation. We base our investigation on an extensive set of ultra high-frequency equity and foreign exchange rate data recorded at milli-second precision, allowing us to view the price evolution at a microscopic level. We show that both in theory and practice, traditional measures of jump variation based on low-frequency tick data tend to spuriously attribute a burst of volatility to the jump component thereby severely overstating the true variation coming from jumps. Indeed, our estimates based on tick data suggest that the jump variation is an order of magnitude smaller. This finding has a number of important implications for asset pricing and risk management and we illustrate this with a delta hedging example of an option trader that is short gamma. Our econometric analysis is build around a pre-averaging theory that allows us to work at the highest available frequency, where the data are polluted bymicrostructure noise. We extend the theory in a number of directions important for jump estimation and testing. This also reveals that pre-averaging has a built-in robustness property to outliers in high-frequency data, and allows us to show that some of the few remaining jumps at tick frequency are in fact induced by data-cleaning routines aimed at removing the outliers.
    Keywords: jump variation, high-frequency data, market microstructure noise, pre-averaging, realised variation, outliers.
    JEL: C10 C80
    Date: 2011–05–26

This nep-rmg issue is ©2011 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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.