Estimation of Value-at-Risk for bank portfolio by using Markov Chain Monte Carlo method

• ZAVIALOV Igor

  Nara Institute of Science and Technology

• IKEDA Kazushi

  Nara Institute of Science and Technology

抄録

Value-at-Risk models (VaR) are widely used in bank risk management practice. VaR models come from the field of “worst statistics” and help to understand the worst loss with a certain probability. After the last major financial crisis of 2007-2008, the regulators tried to modify existing models for risk assessment. VaR models were criticized by many researchers and new modified models, such as conditional VaR, Expected Shortfalls (ES), were proposed. One of the major drawbacks of VaR models is low sensitivity to the tails of the returns’ distribution. In this work, we propose Gibbs sampling for efficient sampling from the joint distribution of returns. Gibbs sampling belongs to the family of Markov Chain Monte Carlo (MCMC) methods which use Markov chains to create random walks while sampling from the target distribution. We considered multivariate distribution of returns for a portfolio consisting of several instruments. For this purpose, we simulated a situation of 3 instruments-portfolio with known Covariance matrix under the normality assumption of the marginal distributions. After calculating the conditional probabilities, we ran Gibbs sampler and succeeded to achieve marginal distributions allowing further VaR calculation.

収録刊行物

• 人工知能学会全国大会論文集

  人工知能学会全国大会論文集 JSAI2020 (0), 1K4ES203-1K4ES203, 2020

  一般社団法人 人工知能学会

キーワード

• Value-at-Risk
• Markov Chain Monte Carlo
• percentile estimation

詳細情報

• CRID: 1390003825189200384
• NII論文ID: 130007856746
• DOI: 10.11517/pjsai.jsai2020.0_1k4es203
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可