On the shortfall risk control: A refinement of the quantile hedging method

IF 1.3 Q2 STATISTICS & PROBABILITY
M. Barski
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引用次数: 3

Abstract

Abstract The issue of constructing a risk minimizing hedge under an additional almost-surely type constraint on the shortfall profile is examined. Several classical risk minimizing problems are adapted to the new setting and solved. In particular, the bankruptcy threat of optimal strategies appearing in the classical risk minimizing setting is ruled out. The existence and concrete forms of optimal strategies in a general semimartingale market model with the use of conditional statistical tests are proven. The quantile hedging method applied in [Finance Stoch. 3 (1999), 251–273; Finance Stoch. 4 (2000), 117–146] as well as the classical Neyman–Pearson lemma are generalized. Optimal hedging strategies with shortfall constraints in the Black–Scholes and exponential Poisson model are explicitly determined.
短缺风险控制:分位数套期保值方法的改进
摘要研究了在一个附加的几乎确定型约束条件下的风险最小化套期保值问题。将几个经典的风险最小化问题适应于新的环境并加以解决。特别地,排除了经典风险最小化设置中出现的最优策略的破产威胁。利用条件统计检验证明了一般半鞅市场模型中最优策略的存在性及其具体形式。分位数套期保值方法在财务管理中的应用[j] .财务管理。3 (1999),251-273;金融学杂志,4(2000),117-146]以及经典的Neyman-Pearson引理被推广。明确确定了Black-Scholes模型和指数泊松模型中具有短缺约束的最优对冲策略。
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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