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on Risk Management |
| By: | Jozef Barunik (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic); Lucie Kraicova (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic) |
| Abstract: | We study the relationship between conditional quantiles of returns and the long-, medium- and short-term volatility in a portfolio of financial assets. We argue that the combination of quantile panel regression and wavelet decomposition of the volatility time series provides us with new insights into the pricing of risk and increases the accuracy of our estimates of re-turn quantiles. Our results contribute to the literature on the risk-return relationship with an emphasis on portfolio management under various investment horizons. Moreover, the analytical framework that we introduce should be applicable to a wide range of problems outside of our research area. |
| Keywords: | Return predictability, Quantiles, Wavelets, Panel data |
| JEL: | C14 C21 C58 G17 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2017_19&r=rmg |
| By: | Frantisek Cech (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic); Jozef Barunik (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic) |
| Abstract: | This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogeneity among financial assets. Direct benefits of the proposed approach are revealed in the portfolio Value-at-Risk forecasting application, where our modeling strategy performs significantly better than several benchmark models according to both statistical and economic comparison. In particular Panel Quantile Regression Model for Returns consistently outperforms all the competitors in the 5% and 10% quantiles. Sound statistical performance translates directly into economic gains which is demonstrated in the Global Minimum Value-at-Risk Portfolio and Markowitz-like comparison. Overall results of our research are important for correct identification of the sources of systemic risk, and are particularly attractive for high dimensional applications. |
| Keywords: | panel quantile regression, realized measures, Value-at-Risk |
| JEL: | C14 C23 G17 G32 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2017_20&r=rmg |
| By: | Dietmar Pfeifer; Andreas M\"andle; Olena Ragulina |
| Abstract: | We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature. In particular, we consider negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data in arbitrary dimensions. |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1709.07682&r=rmg |
| By: | Martin D. Gould; Nikolaus Hautsch; Sam D. Howison; Mason A. Porter |
| Abstract: | A counterparty credit limit (CCL) is a limit imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures, their implementation restricts the liquidity that institutions can access in an otherwise centralized pool. We address the question of how this mechanism impacts trade prices and volatility, both empirically and via a new model of trading with CCLs. We find empirically that CCLs cause little impact on trade. However, our model highlights that in extreme situations, CCLs could serve to destabilize prices and thereby influence systemic risk. |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1709.08238&r=rmg |
| By: | Rajae Aboulaich (Mohammadia School of Engineering, Université Mohamed V - Mohammadia School of Engineering, Université Mohamed V, LERMA - Laboratoire d'Etudes et Recherche en Mathématiques Appliquées - Ecole Mohammadia d'Ingénieurs); Rachid Ellaia (Mohammadia School of Engineering, Université Mohamed V - Mohammadia School of Engineering, Université Mohamed V, LERMA - Laboratoire d'Etudes et Recherche en Mathématiques Appliquées - Ecole Mohammadia d'Ingénieurs); Samira El Moumen (LERMA - Laboratoire d'Etudes et Recherche en Mathématiques Appliquées - Ecole Mohammadia d'Ingénieurs); Abderahmane Habbal (Acumes - Analysis and Control of Unsteady Models for Engineering Sciences - CRISAM - Inria Sophia Antipolis - Méditerranée - Inria - Institut National de Recherche en Informatique et en Automatique); Noureddine Moussaid (LERMA - Laboratoire d'Etudes et Recherche en Mathématiques Appliquées - Ecole Mohammadia d'Ingénieurs) |
| Abstract: | The purpose of this work is to present a model for portfolio multi-optimization, in which distributions are compared on the basis of tow statistics: the expected value and the Conditional Value-at-Risk (CVaR), to solve such a problem many authors have developed several algorithms, in this work we propose to find the efficient boundary by using the Normal Boundary Intersection approach (NBI) based on our proposed hybrid method SASP, since the considered problem is multi-objective, then we find the Kalai-smorodinsky solution. |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-01575730&r=rmg |
| By: | Tan, A.C.; McAleer, M.J. |
| Abstract: | The purpose of the paper is to explore the relative biases in the estimation of the Full BEKK model as compared with the Diagonal BEKK model, which is used as a theoretical and empirical benchmark. Chang and McAleer [4] show that univariate GARCH is not a special case of multivariate GARCH, specically, the Full BEKK model, and demonstrate that Full BEKK which, in practice, is estimated almost exclusively, has no underlying stochastic process, regularity conditions, or asymptotic properties. Diagonal BEKK (DBEKK) does not suf- fer from these limitations, and hence provides a suitable benchmark. We use simulated nancial returns series to contrast estimates of the conditional vari- ances and covariances from DBEKK and BEKK. The results of non-parametric tests suggest evidence of considerable bias in the Full BEKK estimates. The results of quantile regression analysis show there is a systematic relationship between the two sets of estimates as we move across the quantiles. Estimates of conditional variances from Full BEKK, relative to those from DBEKK, are lower in the left tail and higher in the right tail. |
| Keywords: | DBEKK, BEKK, Regularity Conditions, Asymptotic Properties, Non-Parametric, Bias, Qantile regression |
| JEL: | C13 C21 C58 |
| Date: | 2017–07–28 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:101765&r=rmg |
| By: | S. Ali Pourmousavi; Mahdi Behrangrad; Ali Jahanbani Ardakani; M. Hashem Nehrir |
| Abstract: | Ownership cost calculation plays an important role in optimal operation of distributed energy resources (DERs) and microgrids (MGs) in the future power system, known as smart grid. In this paper, a general framework for ownership cost calculation is proposed using uncertainty and risk analyses. Four ownership cost calculation approaches are introduced and compared based on their associated risk values. Finally, the best method is chosen based on a series of simulation results, performed for a typical diesel generator (DiG). Although simulation results are given for a DiG (as commonly used in MGs), the proposed approaches can be applied to other MG components, such as batteries, with slight modifications, as presented in this paper. The analyses and proposed approaches can be useful in MG optimal design, optimal power flow, and market-based operation of the smart grid for accurate operational cost calculations. |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1709.08023&r=rmg |
| By: | William N. Goetzmann; Dasol Kim |
| Abstract: | We study crashes using data from 101 global stock markets from 1698 to 2015. Extremely large, annual stock market declines are typically followed by positive returns. This is not true for smaller declines. This pattern does not appear to be driven by financial market disruptions, macroeconomic shocks, political conflicts, or survivorship issues. |
| JEL: | G02 G11 G15 G17 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23830&r=rmg |
| By: | Anil K. Kashyap; Dimitrios P. Tsomocos; Alexandros Vardoulakis |
| Abstract: | We modify the Diamond and Dybvig (1983) model of banking to jointly study various regulations in the presence of credit and run risk. Banks choose between liquid and illiquid assets on the asset side, and between deposits and equity on the liability side. The endogenously determined asset portfolio and capital structure interact to support credit extension, as well as to provide liquidity and risk-sharing services to the real economy. Our modifications create wedges in the asset and liability mix between the private equilibrium and a social planner's equilibrium. Correcting these distortions requires the joint implementation of a capital and a liquidity regulation. |
| Keywords: | Bank runs ; Capital ; Credit risk ; Limited liability ; Liquidity ; Regulation |
| JEL: | E44 G01 G21 G28 |
| Date: | 2017–09–22 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2017-97&r=rmg |
| By: | Trivikram Dokka Venkata Satyanaraya; Fabrice Talla Nobibon; Sonali Sen Gupta; Alain Zemkoho |
| Abstract: | We study a robust toll pricing problem where toll setters and users have different level of information when taking their decisions. Toll setters do not have full information on the costs of the network and rely on historical information when determining toll rates, whereas users decide on the path to use from origin to destination knowing toll rates and having, in addition, more accurate traffic data. In this work, we first consider a single origin-destination parallel network and formulate the robust toll pricing problem as a distributionally robust optimization problem, for which we develop an exact algorithm based on a mixed-integer programming formulation and a heuristic based on two-point support distribution. We further extend our formulations to more general networks and show how our algorithms can be adapted for the general networks. Finally, we illustrate the usefulness of our approach by means of numerical experiments both on randomly generated networks and on the road network of the city of Chicago. |
| Keywords: | Toll-pricing, Conditional value at risk, Robust optimization |
| JEL: | C61 C63 D80 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:194217799&r=rmg |