Search arXiv⌕ Search

arXiv · 0706.0051

Optimal consumption from investment and random endowment in incomplete semimartingale markets

Abstract

We consider the problem of maximizing expected utility from consumption in a constrained incomplete semimartingale market with a random endowment process, and establish a general existence and uniqueness result using techniques from convex duality. The notion of asymptotic elasticity of Kramkov and Schachermayer is extended to the time-dependent case. By imposing no smoothness requirements on the utility function in the temporal argument, we can treat both pure consumption and combined consumption/terminal wealth problems, in a common framework. To make the duality approach possible, we provide a detailed characterization of the enlarged dual domain which is reminiscent of the enlargement of $L^1$ to its topological bidual $(L^{\infty})^*$, a space of finitely-additive measures. As an application, we treat the case of a constrained It\^ o-process market-model.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ioannis Karatzas, Gordan Zitkovic. 2007-06-01. Optimal consumption from investment and random endowment in incomplete semimartingale markets. https://arxiv.org/abs/0706.0051

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Directionally Correct Views in Portfolio Optimization

Portfolio optimization techniques often use historical data to estimate parameters for Markowitz-type or ML models. Effective allocation requires a way to align the available data with conditions expected over the investment horizon. Inspired by the Black-Litterman framework, we ask whether incorporating expert views leads to better portfolio allocations. In a simple Markowitz setting, we show that even a small amount of directionally correct information can materially improve investment decisions. We also discuss how expert views can be incorporated in end-to-end portfolio models using entropy-based ideas. Somewhat surprisingly, our empirical results indicate that simpler Markowitz-type model perform as well as an end-to-end model, once directionally correct experts are incorporated.

q-fin.PM↗

Market Regime-Based Multi-Asset Portfolio Optimization Using Nested Clustered Optimization for Tactical Asset Allocation

We propose a regime-aware tactical asset allocation (TAA) framework integrating market regime detection with Nested Clustered Optimization (NCO) for multi-asset portfolio construction. The central hypothesis is that incorporating market regime information into the NCO pipeline, specifically at the stages of covariance estimation, hierarchical clustering, and weight optimization, yields portfolios with improved risk-adjusted performance and reduced downside risk relative to regime-agnostic baselines. Market regimes (Bullish, Sideways/Neutral, Bearish) are detected using a Gaussian Hidden Markov Model (HMM) fitted exclusively on information available up to each decision point, thereby preventing look-ahead bias. Regime-conditional covariance matrices and clustering structures parameterize NCO at each rebalancing event. An adaptive rebalancing rule, triggered by regime transitions or material changes in optimal weights, replaces fixed-calendar rebalancing. The framework is evaluated on a heterogeneous monthly multi-asset universe comprising Indonesian government bonds (SBN), the LQ45 equity index, gold (IDR-denominated), foreign exchange (CNY/IDR), and Bitcoin/IDR (BTC). Out-of-sample performance is assessed via rolling walk-forward backtesting. Benchmarks include equal-weight (1/N), buy-and-hold, standard NCO without regime awareness, minimum variance, hierarchical risk parity (HRP), and a rule-based regime strategy. Performance metrics include cumulative return, annualized Sharpe ratio, Sortino ratio, maximum drawdown, Calmar ratio, turnover, and net-of-cost returns. Statistical significance is assessed using the Jobson-Korkie test. The empirical analysis indicates that the proposed regime-aware TAA framework successfully limits tail risk, achieving a maximum drawdown of 4.44% and demonstrating the utility of HMM-based regime detection in multi-asset risk management.

q-fin.PM↗

When Values-Based Screens Disagree: Continuous Shariah Compliance and Classification Dynamics

Does the design of a values-based screening rule generate eligibility changes when reported accounting inputs remain unchanged but market valuations move? We apply seven Shariah financial-screening rulebooks to 1.34 million U.S. security-months from 1999 to 2024 on a common, threshold-anchored scale. Applied retrospectively, the full emulations disagree on 35.9% of complete permissible-sector observations (38.8% from 2015, when all were in force), and the financial-ratio screens alone on 47.3%. Under AAOIFI's spot market-capitalisation denominator, market-capitalisation movement alone is sufficient for 65.6% of breaches, and half of denominator-driven breaches reverse within three months; numerator movement dominates under asset-based rules. Holding the threshold and sample fixed, a 24-month average denominator cuts debt-screen transitions from 2.51% to 1.03% a month, whereas raising the threshold barely matters. Among breaches of similar severity the reversal gap narrows but persists. Portfolio and fund-holdings evidence qualifies these findings: benefits are modest under a binding single-rule mandate.

q-fin.PM↗