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首页> 外文期刊>The journal of risk finance >Modeling risk for long and short trading positions
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Modeling risk for long and short trading positions

机译：建模长交易职位的风险

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Purpose - Aims to investigate the accuracy of parametric, nonparametric, and semiparametric methods in predicting the one-day-ahead value-at-risk (VaR) measure in three types of markets (stock exchanges, commodities, and exchange rates), both for long and short trading positions. Design/methodology/approach - The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance, and power transformation of conditional variance. Findings - Based on back-testing measures and a loss function evaluation method, finds that the modeling of the main characteristics of asset returns produces the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions. Practical implications - Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns. Originality/value - The behavior of the risk management techniques is examined for both long and short VaR trading positions; to the best of one's knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, a two-stage model selection is implemented in contrast with the most commonly used back-testing procedures to identify a unique model. Finally, parametric, nonparametric, and semiparametric techniques are employed to investigate their performance in a unified environment.
机译：目的 - 旨在调查参数，非参数和半参数方法的准确性，以预测三种类型的市场（证券交易所，商品和汇率）中的一日预先价值风险（VAR）衡量标准，均为长交易职位。设计/方法/方法 - 风险管理技术旨在捕获资产回报的主要特征，例如链球菌病和不对称分布，波动率群集，股票回报与条件差异之间的不对称关系以及条件差异的功率转化。调查结果 - 基于反测试措施和损失函数评估方法，发现资产回报的主要特征的建模会产生最准确的VAR预测。特别是对于高置信度，风险经理必须采用不同的波动率技术，以便准确预测两个交易头寸的VAR。实际含义 - 不同的模型实现了长期和短交易位置的准确VAR预测，这表明投资组合经理分别建模左侧和右侧的回报分配的右侧。独创性/价值 - 长和短量交易头寸都检查了风险管理技术的行为;据一个人的知识，这是第一项同时研究三个不同金融市场的风险特征的研究。此外，与最常用的背景过程相比，实现了两阶段的模型选择，以识别唯一的模型。最后，采用了参数，非参数和半参数技术在统一环境中研究其性能。

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