Search results for "value.at.Risk"

Non-Gaussian Distribution for Var Calculation: an Assessment for the Italian Market

2001

Abstract In this paper we compare different approaches to computing VaR (Value-at-Risk) for heavy tailed return series. Using data from the Italian market, we show that almost all the return series present statistically significant skewness and kurtosis. We implement (i) the stable models proposed by Rachev et al . (2000), (ii) an alternative to the Gaussian distributions based on a Generalized Error Distribution and (iii) a non-parametric model proposed by Li (1999). All the models are then submitted to backtest on out-of-sample data in order to assess their forecasting power. We observe that when the percentiles are low, all the models tested produce results that are dominant compared to …

A computational proposal for a robust estimation of the Pareto tail index: An application to emerging markets

2022

Abstract In this work, we backtest and compare, under the VaR risk measure, the fitting performances of three classes of density distributions (Gaussian, Stable and Pareto) with respect to three different types of emerging markets: Egypt, Qatar and Mexico. We also propose a new technique for the estimation of the Pareto tail index by means of the Threshold Accepting (TAVaR) and the Hybrid Particle Swarm Optimization algorithm (H-PSOVaR). Furthermore, we test the accuracy and robustness of our estimates demonstrating the effectiveness of the proposed approach.

The impact of systemic and illiquidity risk on financing with risky collateral

2015

Abstract Repurchase agreements (repos) are one of the most important sources of funding liquidity for many financial investors and intermediaries. In a repo, some assets are given by a borrower as collateral in exchange of funding. The capital given to the borrower is the market value of the collateral, reduced by an amount termed as haircut (or margin). The haircut protects the capital lender from loss of value of the collateral contingent on the borrower׳s default. For this reason, the haircut is typically calculated with a simple Value at Risk estimation of the collateral for the purpose of preventing the risk associated to volatility. However, other risk factors should be included in th…

High Frequency Data Analysis in an Emerging and a Developed Market

2002

We compare distributional properties of high frequency (tick by tick) returns of stocks traded at the NASDAQ, NYSE, and BSE (Budapest Stock Exchange). In particular, we model returns with a mixture of a degenerate (zero) and a symmetric stable distribution. We measure time with the number of successive price changes on the market and study the convergence of the index of stability on increasing time horizons. We apply results to calculate expected waiting times to reach given levels of value at risk.

Implicit Public Debt Thresholds: An Empirical Exercise for the Case of Spain

2017

We extend previous work that combines the Value at Risk approach with estimation of the correlation pattern of the macroeconomic determinants of public debt dynamics by means of Vector Auto Regressions (VARs). These estimated models are used to compute the probability that the public debt ratio exceeds a given threshold, by means of Monte Carlo simulations. We apply this methodology to Spanish data and compute time-series probabilities to analyse the possible correlation with market risk assessment, measured by the spread over the German bond. Taking into account the high correlation between the probability of crossing a pre-specified debt threshold and the spread, we go a step further and …

Minimising value-at-risk in a portfolio optimisation problem using a multi-objective genetic algorithm

2011

[EN] In this paper, we develop a general framework for market risk optimisation that focuses on VaR. The reason for this choice is the complexity and problems associated with risk return optimisation (non-convex and non-differential objective function). Our purpose is to obtain VaR efficient frontiers using a multi-objective genetic algorithm (GA) and to show the potential utility of the algorithm to obtain efficient portfolios when the risk measure does not allow calculating an optimal solution. Furthermore, we measure differences between VaR efficient frontiers and variance efficient frontiers in VaR-return space and we evaluate out-sample capacity of portfolios on both bullish and bearis…

A Conditional Value–at–Risk Model for Insurance Products with Guarantee

2009

We propose a model to select the optimal portfolio which underlies insurance policies with a guarantee. The objective function is defined in order to minimise the conditional value at-risk (CVaR) of the distribution of the losses with respect to a target return. We add operational and regulatory constraints to make the model as flexible as possible when used for real applications. We show that the integration of the asset and liability side yields superior performances with respect to naive fixed-mix portfolios and asset based strategies. We validate the model on out-of-sample scenarios and provide insights on policy design.

Value at risk -laskennan soveltuvuus lentoyhtiölle : case: Finnair oyj

2001

Hedging foreign exchange rate risk: Multi-currency diversification

2016

Abstract This article proposes a multi-currency cross-hedging strategy that minimizes the exchange risk. The use of derivatives in small and medium-sized enterprises (SMEs) is not common but, despite its complexity, can be interesting for those with international activities. In particular, the reduction in the exchange risk borne through the use of natural multi-currency cross-hedging is measured, considering Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) for measuring market risk instead of the variance. CVaR is minimized using linear programmes, while a multiobjective genetic algorithm is designed for minimizing VaR, considering two scenarios for each currency. The results obtai…

Risk Management Optimization for Sovereign Debt Restructuring

2015

Abstract Debt restructuring is one of the policy tools available for resolving sovereign debt crises and, while unorthodox, it is not uncommon. We propose a scenario analysis for debt sustainability and integrate it with scenario optimization for risk management in restructuring sovereign debt. The scenario dynamics of debt-to-GDP ratio are used to define a tail risk measure, termed conditional Debt-at-Risk. A multi-period stochastic programming model minimizes the expected cost of debt financing subject to risk limits. It provides an operational model to handle significant aspects of debt restructuring: it collects all debt issues in a common framework, and can include contingent claims, m…