nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒12‒20
eight papers chosen by

  1. Employing Bayesian Forecasting of Value-at-Risk to Determine an Appropriate Model for Risk Management By CHEN, Cathy W.S.; WENG, Monica M.C.; WATANABE, Toshiaki
  2. Down-side Risk Metrics as Portfolio Diversification Strategies across the GFC By Allen, D.E.; McAleer, M.J.; Powell, R.J.; Singh, A.K.
  3. Distress propagation in complex networks: the case of non-linear DebtRank By Marco Bardoscia; Fabio Caccioli; Juan Ignacio Perotti; Gianna Vivaldo; Guido Caldarelli
  4. A generalized intensity based framework for single-name credit risk By Frank Gehmlich; Thorsten Schmidt
  5. Optimal form of retention for securitized loans under moral hazard By Dionne, Georges; Malekan, Sara
  6. The Determinants of Systemic Banking Crises A Regulatory Perspective By Michael Wosser
  7. Optimal Control of Conditional Value-at-Risk in Continuous Time By Christopher W. Miller; Insoon Yang
  8. Identifying Highly Correlated Stocks Using the Last Few Principal Components By Libin Yang; William Rea; Alethea Rea

  1. By: CHEN, Cathy W.S.; WENG, Monica M.C.; WATANABE, Toshiaki
    Abstract: To allow for a higher degree of flexibility in model parameters, we propose a general and time-varying nonlinear smooth transition (ST) heteroskedastic model with a second-order logistic function of varying speed in the mean and variance. This paper evaluates the performance of Value-at-Risk (VaR) measures in a class of risk models, specially focusing on three distinct ST functions with GARCH structures: first- and second-order logistic functions, and the exponential function. The likelihood function is non-differentiable in terms of the threshold values and delay parameter. We employ Bayesian Markov chain Monte Carlo sampling methods to update the estimates and quantile forecasts. The proposed methods are illustrated using simulated data and an empirical study. We estimate VaR forecasts for the proposed models alongside some competing asymmetric models with skew and fat-tailed error probability distributions, including realized volatility models. To evaluate the accuracy of VaR estimates, we implement two loss functions and three backtests. The results show that the ST model with a second-order logistic function and skew Student’s t error is a worthy choice at the 1% level, when compared to a range of existing alternatives.
    Keywords: Second-order logistic transition function, Backtesting, Markov chain Monte Carlo methods, Value-at-Risk, Volatility forecasting, Realized volatility models
    Date: 2015–12–08
  2. By: Allen, D.E.; McAleer, M.J.; Powell, R.J.; Singh, A.K.
    Abstract: This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically compounded returns on a set of ten market indices representing the major European markets for a nine year period from the beginning of 2005 to the end of 2013. The sample period, which incorporates the periods of both the Global Financial Crisis (GFC) and subsequent European Debt Crisis (EDC), is challenging one for the application of portfolio investment strategies. The analysis is undertaken via the examination of multiple investment strategies and a variety of hold-out periods and back-tests. We commence by using four two year estimation periods and subsequent one year investment hold out period, to analyse a naive 1/N diversification strategy, and to contrast its effectiveness with Markowitz mean variance analysis with positive weights. Markowitz optimisation is then compared with various down- side investment opimisation strategies. We begin by comparing Markowitz with CVaR, and then proceed to evaluate the relative e effctiveness of Markowitz with various draw-down strategies, utilising a series of backtests. Our results suggest that none of the more sophisticated optimisation strategies appear to dominate naive diversification.
    Keywords: portfolio diversification, Markowitz analysis, downside risk, CVaR, draw-down
    JEL: G11 C61
    Date: 2015–11–01
  3. By: Marco Bardoscia; Fabio Caccioli; Juan Ignacio Perotti; Gianna Vivaldo; Guido Caldarelli
    Abstract: We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall the stability of the system increases between 2008 and 2013.
    Date: 2015–12
  4. By: Frank Gehmlich; Thorsten Schmidt
    Abstract: The intensity of a default time is obtained by assuming that the default indicator process has an absolutely continuous compensator. Here we drop the assumption of absolute continuity with respect to the Lebesgue measure and only assume that the compensator is absolutely continuous with respect to a general $\sigma$-finite measure. This allows for example to incorporate the Merton-model in the generalized intensity based framework. An extension of the Black-Cox model is also considered. We propose a class of generalized Merton models and study absence of arbitrage by a suitable modification of the forward rate approach of Heath-Jarrow-Morton (1992). Finally, we study affine term structure models which fit in this class. They exhibit stochastic discontinuities in contrast to the affine models previously studied in the literature.
    Date: 2015–12
  5. By: Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Malekan, Sara (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We address the moral hazard problem of securitization using a principal-agent model where the investor is the principal and the lender is the agent. Our model considers structured asset-backed securitization with a credit enhancement procedure. We assume that the originator can affect the default probability and the conditional loss distribution. We show that the optimal form of retention must be proportional to the pool default loss even in the absence of systemic risk when the originator can affect the conditional distribution of portfolio losses, yet the current regulations propose a constant retention rate.
    Keywords: Securitization; optimal retention; moral hazard; principal-agent model; tranching; credit enhancement; conditional loss distribution.
    JEL: D80 D82 D86 G18 G21 G23
    Date: 2015–11–27
  6. By: Michael Wosser (Department of Economics, Finance and Accounting, Maynooth University.)
    Abstract: Using a sample of 75 developed and emerging economies covering the period 1998-2011 we show that the enhanced Basel III Accord variables Tier-1 capital and the new liquidity measure known as the Net Stable Funding Ratio (NSFR), when measured in levels, do not feature as systemic banking crisis determinants. Neither does distance from the minimum standard, in either direction, matter. However the compound annual growth rate of Tier-1 capital is shown to be significantly associated with overall financial-services stability. Certain aspects of the regulatory environment are shown to contribute positively towards systemic risk mitigation whereas others do not. For example by restricting the breadth of trading activities permitted to banks, banking sectors are made stable. However regimes where capital adequacy standards are rigorously enforced are no more robust than their less strictly-enforced counterparts.
    Keywords: Systemic Banking Crises; Determinants; Basel III Accord; Regulations; Regulatory Framework; Stability; Early Warning System
    JEL: G21 G28
    Date: 2015
  7. By: Christopher W. Miller; Insoon Yang
    Abstract: We consider continuous-time stochastic optimal control problems featuring Conditional Value-at-Risk (CVaR) in the objective. The major difficulty in these problems arises from time-inconsistency, which prevents us from directly using dynamic programming. To resolve this challenge, we convert to an equivalent bilevel optimization problem in which the inner optimization problem is standard stochastic control. Furthermore, we provide conditions under which the outer objective function is convex and differentiable. We compute the outer objective's value via a Hamilton-Jacobi-Bellman equation and its gradient via the viscosity solution of a linear parabolic equation, which allows us to perform gradient descent. The significance of this result is that we provide an efficient dynamic programming-based algorithm for optimal control of CVaR without lifting the state-space. To broaden the applicability of the proposed algorithm, we provide convergent approximation schemes in cases where our key assumptions do not hold and characterize relevant suboptimality bounds. In addition, we extend our method to a more general class of risk metrics, which includes mean-variance and median-deviation. We also demonstrate a concrete application to portfolio optimization under CVaR constraints. Our results contribute an efficient framework for solving time-inconsistent CVaR-based dynamic optimization.
    Date: 2015–12
  8. By: Libin Yang; William Rea; Alethea Rea
    Abstract: We show that the last few components in principal component analysis of the correlation matrix of a group of stocks may contain useful financial information by identifying highly correlated pairs or larger groups of stocks. The results of this type of analysis can easily be included in the information an investor uses to manage their portfolio.
    Date: 2015–12

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