nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒10‒22
eight papers chosen by
Stan Miles
Thompson Rivers University

  1. On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging By Hou, Yang; Holmes, Mark
  2. A General Framework for Portfolio Theory. Part I: theory and various models By Stanislaus Maier-Paape; Qiji Jim Zhu
  3. A General Framework for Portfolio Theory. Part II: drawdown risk measures By Stanislaus Maier-Paape; Qiji Jim Zhu
  4. Large deviations for risk measures in finite mixture models By Valeria Bignozzi; Claudio Macci; Lea Petrella
  5. Counterparty Trading Limits Revisited:CSAs, IM, SwapAgent(r), from PFE to PFL By Chris Kenyon; Mourad Berrahoui; Benjamin Poncet
  6. Capital Requirements for Government Bonds - Implications for Financial Stability By Sterzel, André; Neyer, Ulrike
  7. Default Risk, Sectoral Reallocation, and Persistent Recessions By Amanda Michaud; David Wiczer
  8. Household Finance and the Value of Life By Bommier, Antoine; Harenberg, Daniel; Le Grand, François

  1. By: Hou, Yang; Holmes, Mark
    Abstract: While dynamic optimal hedging is of major interest, it remains unclear as to whether incorporating higher moments of a return distribution leads to better hedging decisions. We examine the effects of introducing a bivariate skew-Student density function with static and autoregressive conditional skewness and kurtosis on dynamic minimum-variance hedging strategies. Static higher order moments improve reductions in variance and value at risk of hedged portfolios. The inclusion of dynamics through an autoregressive component extends these improvements further. These benefits avail for short and long hedging horizons, which is highlighted in the global financial crisis. The static and conditional higher order moments enhance the notion that the size and smoothness of hedge ratios positively relate to hedging effectiveness while volatility does the reverse. Improved effectiveness can be explained given an upgrade of size and smoothness and a downgrade of volatility of hedge ratios attributed to the dynamics of higher order moments.
    Keywords: dynamic optimal hedging, multivariate GARCH models, skew-Student density, conditional skewness and kurtosis, hedging effectiveness
    JEL: G11 G13
    Date: 2017–10–17
  2. By: Stanislaus Maier-Paape; Qiji Jim Zhu
    Abstract: Utility and risk are two often competing measurements on the investment success. We show that efficient trade-off between these two measurements for investment portfolios happens, in general, on a convex curve in the two dimensional space of utility and risk. This is a rather general pattern. The modern portfolio theory of Markowitz [H. Markowitz, Portfolio Selection, 1959] and its natural generalization, the capital market pricing model, [W. F. Sharpe, Mutual fund performance , 1966] are special cases of our general framework when the risk measure is taken to be the standard deviation and the utility function is the identity mapping. Using our general framework, we also recover the results in [R. T. Rockafellar, S. Uryasev and M. Zabarankin, Master funds in portfolio analysis with general deviation measures, 2006] that extends the capital market pricing model to allow for the use of more general deviation measures. This generalized capital asset pricing model also applies to e.g. when an approximation of the maximum drawdown is considered as a risk measure. Furthermore, the consideration of a general utility function allows to go beyond the "additive" performance measure to a "multiplicative" one of cumulative returns by using the log utility. As a result, the growth optimal portfolio theory [J. Lintner, The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets, 1965] and the leverage space portfolio theory [R. Vince, The Leverage Space Trading Model, 2009] can also be understood under our general framework. Thus, this general framework allows a unification of several important existing portfolio theories and goes much beyond.
    Date: 2017–10
  3. By: Stanislaus Maier-Paape; Qiji Jim Zhu
    Abstract: The aim of this paper is to provide several examples of convex risk measures necessary for the application of the general framework for portfolio theory of Maier-Paape and Zhu, presented in Part I of this series (arXiv:1710.04579 [q-fin.PM]). As alternative to classical portfolio risk measures such as the standard deviation we in particular construct risk measures related to the current drawdown of the portfolio equity. Combined with the results of Part I (arXiv:1710.04579 [q-fin.PM]), this allows us to calculate efficient portfolios based on a drawdown risk measure constraint.
    Date: 2017–10
  4. By: Valeria Bignozzi; Claudio Macci; Lea Petrella
    Abstract: Due to their heterogeneity, insurance risks can be properly described as a mixture of different fixed models, where the weights assigned to each model may be estimated empirically from a sample of available data. If a risk measure is evaluated on the estimated mixture instead of the (unknown) true one, then it is important to investigate the committed error. In this paper we study the asymptotic behaviour of estimated risk measures, as the data sample size tends to infinity, in the fashion of large deviations. We obtain large deviation results by applying the contraction principle, and the rate functions are given by a suitable variational formula; explicit expressions are available for mixtures of two models. Finally, our results are applied to the most common risk measures, namely the quantiles, the Expected Shortfall and the entropic risk measure.
    Date: 2017–10
  5. By: Chris Kenyon; Mourad Berrahoui; Benjamin Poncet
    Abstract: The utility of Potential Future Exposure (PFE) for counterparty trading limits is being challenged by new market developments, notably widespread regulatory Initial Margin (using 99% 10-day exposure), and netting of trade and collateral flows. However PFE has pre-existing challenges w.r.t. portfolios/distributions, collateralization, netting set seniority, and overlaps with CVA. We introduce Potential Future Loss (PFL) which combines expected shortfall (ES) and loss given default (LGD) as a replacement for PFE. With two additional variants Adjusted PFL (aPFL) and Protected Adjusted PFL (paPFL) these deal with both new and pre-existing challenges. We provide a theoretical background and numerical examples.
    Date: 2017–10
  6. By: Sterzel, André; Neyer, Ulrike
    Abstract: Banks hold relatively large amounts of government bonds. Large sovereign exposures reinforce possible financial contagion effects from sovereigns to banks and are a risk for financial stability. Using a theoretical model, we find that the introduction of capital requirements for government bonds induce banks to decrease their investment in government bonds and to increase their investment in high yield assets. This implies that banks' balance sheets become more resilient.
    JEL: G28 G21 G01
    Date: 2017
  7. By: Amanda Michaud; David Wiczer
    Abstract: Using retrospective data, we introduce evidence that occupational exposure significantly affects disability risk. Incorporating this into a general equilibrium model, social disability insurance (SDI) affects welfare through (i) the classic, risk-sharing channel and (ii) a new channel of occupational reallocation. Both channels can increase welfare, but at the optimal SDI they are at odds. Welfare gains from additional risk-sharing are reduced by overly incentivizing workers to choose risky occupations. In a calibration, optimal SDI increases welfare by 2.3% relative to actuarially fair insurance, mostly due to risk sharing.
    Date: 2017
  8. By: Bommier, Antoine; Harenberg, Daniel; Le Grand, François
    Abstract: We analyze life-cycle saving strategies with a recursive model that is designed to provide reasonable positive values for the value of a statistical life. With a positive value of life, risk aversion amplifies the impact of uncertain survival on the discount rate, and thus reduces savings. Our model also predicts that risk aversion lowers stock market participation and leads to choose more conservative portfolios.
    JEL: D91 G11 J17
    Date: 2017

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