Robust Growth-Optimal Portfolios

Rujeerapaiboon, Napat; Kühn, Daniel; Wiesemann, Wolfram

doi:10.1287/mnsc.2015.2228

Cited by 50 publications

(52 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[27] considered an ambiguity set similar to D 2 and derived an SOC representation of DRCCs in portfolio optimization under an assumption of weak sense white noise, i.e., the uncertainty is stationary and mutually uncorrelated over time (see Definition 4 and Theorem 5 in [27]). In contrast, the SOC representation in Theorem 3.2 holds for general covariance matrices.…”

Section: Soc Representations Of the Drccmentioning

confidence: 99%

Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information

Zhang

Jiang²,

Shen

2018

SIAM J. Optim.

View full text Add to dashboard Cite

We consider chance-constrained binary programs, where each row of the inequalities that involve uncertainty needs to be satisfied probabilistically. Only the information of the mean and covariance matrix is available, and we solve distributionally robust chance-constrained binary programs (DCBP). Using two different ambiguity sets, we equivalently reformulate the DCBPs as 0-1 second-order cone (SOC) programs. We further exploit the submodularity of 0-1 SOC constraints under special and general covariance matrices, and utilize the submodularity as well as lifting to derive extended polymatroid inequalities to strengthen the 0-1 SOC formulations. We incorporate the valid inequalities in a branch-and-cut algorithm for efficiently solving DCBPs. We demonstrate the computational efficacy and solution performance using diverse instances of a chance-constrained bin packing problem.

show abstract

Section: Soc Representations Of the Drccmentioning

confidence: 99%

Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information

Zhang

Jiang²,

Shen

2018

SIAM J. Optim.

View full text Add to dashboard Cite

show abstract

“…Delbaen [48] and Follmer and Schied [49] provide a comprehensive presentation of the theory of static coherent and convex risk measures. The most recent comprehensive application of this approach to portfolio selection is provided by Rujeerapaiboon et al [50].…”

Section: Optimal Draw-down Portfoliosmentioning

confidence: 99%

Down-Side Risk Metrics as Portfolio Diversification Strategies across the Global Financial Crisis

Allen

McAleer

Powell

et al. 2016

JRFM

View full text Add to dashboard Cite

Abstract:This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically-compounded returns, in U.S. dollar terms, on a set of ten market indices representing the major European markets for a nine-year period from the beginning of 2005 to the end of 2013. The sample period, which incorporates the periods of both the Global Financial Crisis (GFC) and the subsequent European Debt Crisis (EDC), is a challenging one for the application of portfolio investment strategies. The analysis is undertaken via the examination of multiple investment strategies and a variety of hold-out periods and backtests. We commence by using four two-year estimation periods and a subsequent one-year investment hold out period, to analyse a naive 1/N diversification strategy and to contrast its effectiveness with Markowitz mean variance analysis with positive weights. Markowitz optimisation is then compared to various down-side investment optimisation strategies. We begin by comparing Markowitz with CVaR, and then proceed to evaluate the relative effectiveness of Markowitz with various draw-down strategies, utilising a series of backtests. Our results suggest that none of the more sophisticated optimisation strategies appear to dominate naive diversification.

show abstract

“…A more convincing approach to maximizing wealth with risk management over finite horizons was proposed in [Rujeerapaiboon et al, 2015] for portfolio construction. The authors consider the optimization of a minimum bound for finite-horizon growth,…”

Section: Other Approaches To Optimization Under Finite Horizon and Riskmentioning

confidence: 99%

Adaptive Bet-Hedging Revisited: Considerations of Risk and Time Horizon

Tal

Tran

2020

Bull Math Biol

View full text Add to dashboard Cite

Models of adaptive bet-hedging commonly adopt insights from Kelly's famous work on optimal gambling strategies and the financial value of information. In particular, such models seek evolutionary solutions that maximize long term average growth rate of lineages, even in the face of highly stochastic growth trajectories. Here, we argue for extensive departures from the standard approach to better account for evolutionary contingencies. Crucially, we incorporate considerations of volatility minimization, motivated by interim extinction risk in finite populations, within a finite time horizon approach to growth maximization. We find that a game-theoretic competitive-optimality approach best captures these additional constraints, and derive the equilibria solutions under straightforward fitness payoff functions and extinction risks. We show that for both maximal growth and minimal time relative payoffs the log-optimal strategy is a unique pure-strategy symmetric equilibrium, invariant with evolutionary time horizon and robust to low extinction risks.

show abstract

Robust Growth-Optimal Portfolios

Cited by 50 publications

References 71 publications

Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information

Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information

Down-Side Risk Metrics as Portfolio Diversification Strategies across the Global Financial Crisis

Adaptive Bet-Hedging Revisited: Considerations of Risk and Time Horizon

Contact Info

Product

Resources

About