Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions

• URL: http://arxiv.org/abs/2409.18970v1
• Date: Thu, 12 Sep 2024 03:20:25 GMT
• Title: Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions
• Authors: Krishan Mohan Nagpal,
• Abstract summary: This paper proposes an approach for incorporating prevailing market conditions in stress scenario design and estimation of VaR. The proposed approach is based on historical data where historical observations of market changes are given more weight if a certain period in history is "more similar" to the prevailing market conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Value at Risk (VaR) and stress testing are two of the most widely used approaches in portfolio risk management to estimate potential market value losses under adverse market moves. VaR quantifies potential loss in value over a specified horizon (such as one day or ten days) at a desired confidence level (such as 95'th percentile). In scenario design and stress testing, the goal is to construct extreme market scenarios such as those involving severe recession or a specific event of concern (such as a rapid increase in rates or a geopolitical event), and quantify potential impact of such scenarios on the portfolio. The goal of this paper is to propose an approach for incorporating prevailing market conditions in stress scenario design and estimation of VaR so that they provide more accurate and realistic insights about portfolio risk over the near term. The proposed approach is based on historical data where historical observations of market changes are given more weight if a certain period in history is "more similar" to the prevailing market conditions. Clusters of market conditions are identified using a Machine Learning approach called Variational Inference (VI) where for each cluster future changes in portfolio value are similar. VI based algorithm uses optimization techniques to obtain analytical approximations of the posterior probability density of cluster assignments (market regimes) and probabilities of different outcomes for changes in portfolio value. Covid related volatile period around the year 2020 is used to illustrate the performance of the proposed approach and in particular show how VaR and stress scenarios adapt quickly to changing market conditions. Another advantage of the proposed approach is that classification of market conditions into clusters can provide useful insights about portfolio performance under different market conditions.

Related papers

• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Price Interpretability of Prediction Markets: A Convergence Analysis [6.623653707600985]
  We show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. We also show that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth.
  arXiv  Detail & Related papers  (2022-05-18T13:15:11Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities [12.101446195463591]
  We describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. A key contribution of this work is the development of a novel similarity metric for comparing historical pricing data.
  arXiv  Detail & Related papers  (2021-07-07T17:26:32Z)
• Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model [0.0]
  We construct a regime-switching model independent of asset classes for risk-adjusted return predictions based on hidden Markov models. An investigation of our metric for risk-adjusted return predictions is conducted by analyzing daily financial market changes for almost twenty years.
  arXiv  Detail & Related papers  (2021-07-07T10:23:59Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• Learning Risk Preferences from Investment Portfolios Using Inverse Optimization [25.19470942583387]
  This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings.
  arXiv  Detail & Related papers  (2020-10-04T21:29:29Z)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)
• Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning [2.741266294612776]
  We apply deep learning to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile markets. Our findings show that accuracies, while remaining significant, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers.
  arXiv  Detail & Related papers  (2020-02-24T17:19:54Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.