Dependence Modeling of Joint Extremes Via Copulas: A Dynamic Portfolio Allocation Perspective

Despite the overwhelming evidence of time-varying risk aversion documented in recent literature, standard dynamic portfolio theories often adopt an assumption of constant (relative) risk aversion due to analytical tractability. In “Time Varying Risk Aversion and Dynamic Portfolio Allocation,” Li et al. explicitly consider the implications of time-varying risk aversion for dynamic portfolio allocation under the framework of regime-switching models. An investor with regime-dependent utility exhibits a decreasing relative risk aversion (DRRA) and has higher risk aversion when a bear market regime is more likely in the future. They develop an efficient dynamic programming algorithm that overcomes the challenges imposed by regime-dependent preference in obtaining time-consistent portfolio policies. The empirical results show that VIX is an important predictor of regime shifts and that investors with regime-dependent risk aversion achieve better investment performance than those with constant risk aversion.

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Dynamic portfolio allocation in goals-based wealth management

Computational Management Science ◽

10.1007/s10287-019-00351-7 ◽

2019 ◽

Author(s):

Sanjiv R. Das ◽

Daniel Ostrov ◽

Anand Radhakrishnan ◽

Deep Srivastav

Keyword(s):

Portfolio Allocation ◽

Wealth Management ◽

Dynamic Portfolio

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Variable payout annuities and dynamic portfolio choice in retirement

Journal of Pension Economics and Finance ◽

10.1017/s1474747208003880 ◽

2009 ◽

Vol 9 (2) ◽

pp. 163-183 ◽

Cited By ~ 49

Author(s):

WOLFRAM J. HORNEFF ◽

RAIMOND H. MAURER ◽

OLIVIA S. MITCHELL ◽

MICHAEL Z. STAMOS

Keyword(s):

Portfolio Choice ◽

Capital Market ◽

Portfolio Allocation ◽

Welfare Gains ◽

Variable Annuities ◽

Financial Wealth ◽

Dynamic Portfolio ◽

Dynamic Portfolio Choice ◽

Risk Parameters ◽

Over Time

AbstractMany retirees hope to continue earning capital market rewards on their saving while avoiding outliving their funds during retirement. We model a dynamic utility maximizing investor who seeks to benefit from holding both equity and longevity insurance. She is free to adjust her portfolio allocation of her financial wealth as well as of the annuity over time, and she can purchase variable payout annuities any time and incrementally. In this setting, we show that the retiree will not fully annuitize even without bequests; rather, she will combine variable annuities with withdrawals from her liquid financial wealth so as to match her desired consumption profile. Optimal stock exposures decrease over time, both within the variable annuity and the withdrawal plan. Welfare gains from this strategy can amount to 40% of financial wealth, depending on risk parameters and other resources; additionally, many retirees will do almost as well as the fully optimized outcome if they hold variable annuities invested 60/40 in stocks/bonds.

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Analysis Of Dynamic Portfolio Allocation Of Indonesian LQ45 During 2005 – 2011 Following The Markowitz Theowry

The Winners ◽

10.21512/tw.v17i2.1969 ◽

2016 ◽

Vol 17 (2) ◽

pp. 91

Author(s):

Agustini Hamid

Keyword(s):

Portfolio Management ◽

Stock Return ◽

Banking Sector ◽

Management Strategies ◽

Portfolio Allocation ◽

Return Volatility ◽

Stock Return Volatility ◽

Manufacture Sector ◽

Turbulent Times ◽

Dynamic Portfolio

The research observed that equity portfolio and investment managers were facing challenges in determining the optimum portfolio, especially during the turbulent times. As a result, they needed to implement portfolio management strategies to overcome the risk associated with stock return volatility in turbulence periods. This research focused on selecting stocks from the LQ-45 index during 2005-2011 using The Markowitz theory combining the Solver Linear Programming. The portfolio selection method which has been introduced by Markowitz (1952) used variance or standard deviation as a risk measurement. The result of this research proves that the composition of the portfolio is not the same in the different period. In the bearish period, the composition of the optimum portfolio is dominated by the banking sector and manufacture sector. In the bullish period, the optimum portfolio is dominated by the commodity stocks.

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