Multi‐period mean variance portfolio selection under incomplete information

Zhang, Ling; Li, Zhongfei; Xu, Yunhui; Li, Yongwu

doi:10.1002/asmb.2191

Cited by 15 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HMM proposed in Baum and Petrie [20] has been widely used in various problems [21][22][23]. It consists of two stochastic processes and , where is a Markov chain and not directly visible (hidden), but the output of another variable whose distribution depends on the hidden process , is visible.…”

Section: The Particular Triplet Markov Model Architecturementioning

confidence: 99%

Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach

Chen

Jiang

2020

Complexity

View full text Add to dashboard Cite

With a view to develop a more realistic model for credit risk analysis in consumer loan, our paper addresses the problem of how to incorporate business cycles into a repayment behavior model of consumer loan in portfolio. A particular Triplet Markov Model (TMM) is presented and introduced to describe the dynamic repayment behavior of consumers. The particular TMM can simultaneously capture the phases of business cycles, transition of systematic credit risk of a loan portfolio, and Markov repayment behavior of consumers. The corresponding Markov chain Monte Carlo algorithms of the particular TMM are also developed for estimating the model parameters. We show how the transition of consumers’ repayment states and systematic credit risk of a loan portfolio are affected by the phases of business cycles through simulations.

show abstract

Section: The Particular Triplet Markov Model Architecturementioning

confidence: 99%

Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach

Chen

Jiang

2020

Complexity

View full text Add to dashboard Cite

show abstract

“…The robust estimation of the correlation matrix achieved better returns from the investment measured in terms of the Sharpe ratio. A similar strategy was used in the work of Zhang et al, where the Sharpe ratio assesses the impact of the unobservable market state on the efficient frontier.…”

Section: Literature Review For Extreme Observations In Portfolio Optimentioning

confidence: 99%

Robust asset allocation with conditional value at risk using the forward search

Grossi

Laurini

2019

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the effective risk. Additionally, the Markowitz approach is very sensitive to small changes in either the expected returns or their correlation, often leading to irrelevant portfolio allocations. More recent allocation techniques are based on alternative risk measures, such as value at risk (VaR) and conditional VaR (CVaR), which are believed to be more accurate measures of risk for fat‐tailed distributions. Nevertheless, both VaR and CVaR estimates can be influenced by the presence of extreme returns. In this paper, we discuss sensitivity to the presence of extreme returns and outliers when optimizing the allocation, under the constraint of keeping CVaR to a minimum. A robust and efficient approach, based on the forward search, is suggested. A Monte Carlo simulation study shows the advantages of the proposed approach, which outperforms both robust and nonrobust alternatives under a variety of specifications. The performance of the method is also thoroughly evaluated with an application to a set of US stocks.

show abstract

“…A similar assumption is made in[2] and[15], but none of them takes into account the inflation risk and stochastic salary. In addition,[22] use a hidden Markov chain to describe the market dynamics under the discrete-time settings, in which the stock returns depend on both the observable and unobservable market states.…”

mentioning

confidence: 99%

Asset allocation for a DC pension plan with learning about stock return predictability

Wang

Zhang

2022

JIMO

Self Cite

View full text Add to dashboard Cite

<p style='text-indent:20px;'>This paper investigates an optimal investment problem for a defined contribution pension plan member who receives a stochastic salary, and considers inflation risk and stock return predictability. The member aims to maximize the expected power utility from her terminal real wealth by investing her pension account wealth in a financial market consisting of a risk-free asset, an inflation-indexed bond and a stock. The expected excess return on the stock can be predicted by both an observable predictor and an unobservable predictor, and the member has to estimate the unobservable predictor by learning the history information. By using the filtering techniques and dynamic programming approach, the closed-form optimal investment strategy and the corresponding value function are derived. Finally, with the help of numerical analysis, we explore the impact of model parameters on the optimal investment strategy, and analyze the welfare benefits from leaning and using inflation-indexed bond to hedge the stock return predictors.</p>

show abstract

Multi‐period mean variance portfolio selection under incomplete information

Cited by 15 publications

References 29 publications

Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach

Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach

Robust asset allocation with conditional value at risk using the forward search

Asset allocation for a DC pension plan with learning about stock return predictability

Contact Info

Product

Resources

About