Paper: arXiv 2407.10175

Abstract

We employ a Bayesian modelling technique for high dimensional cointegration estimation to construct low volatility portfolios from a large number of stocks. The proposed Bayesian framework effectively identifies sparse and important cointegration relationships amongst large baskets of stocks across various asset spaces, resulting in portfolios with reduced volatility. Such cointegration relationships persist well over the out-of-sample testing time, providing practical benefits in portfolio construction and optimization. Further studies on drawdown and volatility minimization also highlight the benefits of including cointegrated portfolios as risk management instruments.

Complexity vs Empirical Score

  • Math Complexity: 9.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper uses advanced high-dimensional Bayesian statistics and complex matrix decompositions (Spike-and-Slab Lasso, VECM) but also demonstrates out-of-sample testing on large stock universes (400-1000 stocks) with specific performance metrics like volatility reduction and Sharpe ratio.

Research Flowchart

  flowchart TD
  A["Research Goal: Construct Low Volatility Portfolio<br>from High-Dimensional Stock Data"] --> B["Data Inputs: Time-series<br>of N Stocks (High-Dimensional)"]
  B --> C["Methodology: Bayesian Sparse<br>Cointegration Estimation"]
  C --> D["Computational Process:<br>Posterior Sampling & Model Fitting"]
  D --> E["Key Outcome 1: Identified<br>Persistent Cointegration Relationships"]
  E --> F["Key Outcome 2: Constructed<br>Low Volatility Portfolio"]
  F --> G["Outcome: Reduced Volatility &<br>Effective Risk Management (Drawdown)"]

Cited References (13)

  1. Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)
  2. Bayesian Analysis of High Dimensional Vector Error Correction Model — → 2312.17061
  3. Determination of vector error correction models in high dimensions (2019)
  4. Spike-and-slab meets lasso: A review of the spike-and-slab lasso (2021)
  5. Pairs trading (2005)
  6. Sequential asset ranking in nonstationary time series (2022)
  7. Portfolio Selection: A Statistical Learning Approach (2022)
  8. Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction (2022)
and 5 more
  1. Graph Denoising Networks: A Deep Learning Framework for Equity Portfolio Construction (2023)
  2. Sparse and stable Markowitz portfolios (2009)
  3. The Spike-and-Slab LASSO (2018)
  4. Bayesian inference with the l 1-ball prior: solving combinatorial problems with exact zeros (2023)
  5. Portfolio Selection (1952)

1 of 13 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.