Paper: arXiv 2609.38765

Authors: Balaji Ramachandran, Srikanth Iyer, Shashi Jain

Abstract

Bank treasury portfolios must balance yield, liquidity, and interest-rate risk across bonds of different maturities. Static allocation rules are ill-suited to this task: portfolios concentrated in long-duration securities with no dynamic adjust- ment mechanism can accumulate large mark-to-market losses and liquidity stress under rising interest rates, as illustrated by the failure of Silicon Valley Bank in 2023. We develop a tractable simulation-based framework for multi-period bond port- folio optimization under interest-rate risk and proportional transaction costs. Yield-curve dynamics are modeled using the Dynamic Nelson-Siegel parameter- ization with Vector Autoregressive factor dynamics, from which we construct a time-inhomogeneous discrete-state Markov chain approximating the joint yield process across bond maturities. This chain forms the state space of a finite- horizon Markov Decision Process in which the investor maximizes expected terminal wealth subject to proportional rebalancing costs. The optimal portfolio policy is obtained by backward induction. We also quantify the approximation error introduced by truncating the transition kernel, and show that it leaves mean terminal wealth almost unchanged while substantially distorting drawdown and tail statistics.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a sophisticated mathematical framework using MDPs and Dynamic Nelson-Siegel models. It demonstrates strong empirical rigor through simulation-based backtesting and comparison with benchmarks, while offering a novel approach to multi-period bond optimization.

Research Flowchart

  flowchart TD
    A[Research Goal: Multiperiod Bond Portfolio Optimization] --> B{Key Methodology Steps};
    B --> C[Data/Inputs: Yield Curve Dynamics (Dynamic Nelson-Siegel, VAR)];
    C --> D{Computational Process: Markov Decision Process (MDP) + Backward Induction};
    D --> E[Outcomes: Optimal Portfolio Policy];
    E --> F[Key Finding: Approximation Error Effects on Drawdown & Tail Statistics];