Search results for "value.at.Risk"
showing 10 items of 36 documents
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…
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.
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.
Incorporating stand level risk management options into forest decision support systems
2018
Aim of study: To examine methods of incorporating risk and uncertainty to stand level forest decisions. Area of study: A case study examines a small forest holding from Jonkoping, Sweden. Material and methods: We incorporate empirically estimated uncertainty into the simulation through a Monte Carlo approach when simulating the forest stands for the next 100 years. For the iterations of the Monte Carlo approach, errors were incorporated into the input data which was simulated according to the Heureka decision support system. Both the Value at Risk and the Conditional Value at Risk of the net present value are evaluated for each simulated stand. Main results: Visual representation of the er…
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 …
Risk management optimization for sovereign debt restructuring
2015
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, multiple…
Data-Based Forest Management with Uncertainties and Multiple Objectives
2016
In this paper, we present an approach of employing multiobjective optimization to support decision making in forest management planning. The planning is based on data representing so-called stands, each consisting of homogeneous parts of the forest, and simulations of how the trees grow in the stands under different treatment options. Forest planning concerns future decisions to be made that include uncertainty. We employ as objective functions both the expected values of incomes and biodiversity as well as the value at risk for both of these objectives. In addition, we minimize the risk level for both the income value and the biodiversity value. There is a tradeoff between the expected val…
An Empirical Investigation of Heavy Tails in Emerging Markets and Robust Estimation of the Pareto Tail Index
2021
In this work we analyze and compare the performances of VaR-based estimatorswith respect to three different classes of distributions, i.e., Gaussian, Stable and Pareto, and to different emerging markets, i.e., Egypt, Qatar and Mexico. This is motivated by the evidence that there are points of distinction between emerging and developed markets mainly relating to the speed and reliability of information available to investors.We propose a computational Threshold Accepting-VaR based algorithm (TAVaR) for optimally estimating the Pareto tail index. A Monte Carlo bias estimation analysis is also carried out by comparing our proposed methodology with the Hill estimator and a variant of it.
Comment on “A simple way to incorporate uncertainty and risk into forest harvest scheduling”
2017
In a recent research article, Robinson et al. (2016) described a method of estimating uncertainty of harvesting outcomes by analyzing the historical yield to the associated prediction for a large number of harvest operations. We agree with this analysis, and consider it a useful tool to integrate estimates of uncertainty into the optimization process. The authors attempt to manage the risk using two different methods, based on deterministic integer linear programming. The first method focused on maximizing the 10th quantile of the distribution of predicted volume subject to area constraint, while the second method focused on minimizing the variation of total quantity of volume harvested sub…