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Journal articles on the topic 'Asset Allocation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Phan, Hieu V., and Shantaram P. Hegde. "Corporate Governance and Risk Taking in Pension Plans: Evidence from Defined Benefit Asset Allocations." Journal of Financial and Quantitative Analysis 48, no. 3 (May 3, 2013): 919–46. http://dx.doi.org/10.1017/s0022109013000227.

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AbstractBased on theoretical advice and empirical evidence suggesting that risk taking in asset allocation enhances pension returns, we evaluate empirically whether good corporate governance leads to a larger allocation of pension assets to risky securities as compared to safe investments. Our findings suggest that firms with good external and internal corporate governance take more risk by investing heavily in equities and allocating a smaller share of the plan assets to cash, government debt, and insurance company accounts. The main underlying mechanisms appear to be higher investment returns and better pension funding status associated with higher equity and lower safe asset allocations.

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2

Heroux, Marcel. "Asset Allocation with Shadow Assets." CFA Digest 43, no. 1 (February 2013): 87–88. http://dx.doi.org/10.2469/dig.v43.n1.33.

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3

Scherer, Bernd. "Asset Allocation with Shadow Assets." Journal of Wealth Management 15, no. 3 (October 31, 2012): 30–35. http://dx.doi.org/10.3905/jwm.2012.15.3.030.

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4

Idzorek, Thomas M., and Maciej Kowara. "Factor-Based Asset Allocation vs. Asset-Class-Based Asset Allocation." Financial Analysts Journal 69, no. 3 (May 2013): 19–29. http://dx.doi.org/10.2469/faj.v69.n3.7.

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5

Cao, Dan, and Jérôme Teïletche. "Reconsidering asset allocation involving illiquid assets." Journal of Asset Management 8, no. 4 (October 15, 2007): 267–82. http://dx.doi.org/10.1057/palgrave.jam.2250077.

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6

Sharpe, William F. "Asset allocation." Journal of Portfolio Management 18, no. 2 (January 31, 1992): 7–19. http://dx.doi.org/10.3905/jpm.1992.409394.

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7

Jones, Charles P., and Jack W. Wilson. "Asset Allocation." Journal of Wealth Management 6, no. 3 (October 31, 2003): 26–34. http://dx.doi.org/10.3905/jwm.2003.320487.

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8

Hin/David Ho, Kim, Seow Eng Ong, and Tien Foo Sing. "Asset allocation." Journal of Property Investment & Finance 24, no. 4 (July 2006): 324–42. http://dx.doi.org/10.1108/14635780610674516.

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9

Wachter, Jessica A. "Asset Allocation." Annual Review of Financial Economics 2, no. 1 (December 2010): 175–206. http://dx.doi.org/10.1146/annurev-financial-073009-104026.

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10

Black, Fischer, and Robert B. Litterman. "Asset Allocation." Journal of Fixed Income 1, no. 2 (September 30, 1991): 7–18. http://dx.doi.org/10.3905/jfi.1991.408013.

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11

Hielscher, Udo. "Asset Allocation." Credit and Capital Markets – Kredit und Kapital 24, no. 2 (February 1, 1991): 254–70. http://dx.doi.org/10.3790/ccm.24.2.254.

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12

Huang, Zhanbing, and Yu Lu. "Analysis of asset risk and household Financial Asset Allocation structure—Empirical analysis from a nonlinear model." E3S Web of Conferences 235 (2021): 01039. http://dx.doi.org/10.1051/e3sconf/202123501039.

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Chinese households now have a good understanding of finance and their asset allocation choices are increasingly skewed towards financial products. At present, most domestic and foreign researches on the structure and choice of household asset allocation mainly analyze the influence of residents’ characteristics or financial literacy on household asset allocation, while few researches on the internal relationship between household risk, asset structure and allocation choice. Based on CHFS data and the theory of asset investment behavior, this paper systematically analyzes and risk assets and family financial asset allocation structure of mutual influence and role, an empirical analysis of the influencing factors of residents in our country family financial asset allocation structure by using Probit model and Tobit model, pay attention to risk assets, family income and other factors, a deeper understanding of family financial asset investment circumstances.

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13

Horvitz, Jeffrey E. "Asset Classes and Asset Allocation." Journal of Wealth Management 2, no. 4 (January 31, 2000): 27–32. http://dx.doi.org/10.3905/jwm.2000.320371.

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14

Clarke, Roger G., Harindra de Silva, and Brett H. Wander. "Risk Allocation versus Asset Allocation." Journal of Portfolio Management 29, no. 1 (October 31, 2002): 9–30. http://dx.doi.org/10.3905/jpm.2002.319860.

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15

Fisher, Gregg S., and Michael B. McDonald. "Factor Allocation and Asset Allocation." Journal of Wealth Management 21, no. 2 (July 31, 2018): 10–20. http://dx.doi.org/10.3905/jwm.2018.21.2.010.

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16

Bt Abdul Halima, Nurfadhlina, Dwi Susanti, Alit Kartiwa, and Endang Soeryana Hasbullah. "Abnormal Portfolio Asset Allocation Model: Review." International Journal of Business, Economics, and Social Development 1, no. 1 (June 12, 2020): 46–54. http://dx.doi.org/10.46336/ijbesd.v1i1.18.

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It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

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17

ALESTALO, NOORA, and VESA PUTTONEN. "Asset allocation in Finnish pension funds." Journal of Pension Economics and Finance 5, no. 1 (February 8, 2006): 27–44. http://dx.doi.org/10.1017/s1474747205002295.

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This paper empirically examines the strategic asset allocation and the asset/liability issues in the Finnish defined benefit pension funds. The results indicate that there is a relationship between the liability structure and the asset allocation. While pension funds with younger participants have more equity exposure, more mature pension funds have more fixed income investments.Wide dispersion in asset allocations is also found between the funds. One fund holds its entire portfolio in fixed income securities, whereas other funds have none or only few fixed income holdings. Equity investments also vary dramatically, ranging from 0% to over 70% of the asset allocation. The same applies to investments in a sponsor, real estate investment, and money market investments. A portion of these different asset allocations is explained by the liability structure, but another part remains unexplained. The other variables affecting strategic asset allocation of a pension fund are not obvious, but they could include factors such as regulatory environment, historical reasons, mean-variance optimization instead of ALM, sponsor's own preferences or pension fund's irrationality. Analyzing these factors would be a fruitful topic for further research. Additionally, international comparisons would be a fruitful topic for further investigation.

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18

Choi, James J., David Laibson, and Brigitte C. Madrian. "Mental Accounting in Portfolio Choice: Evidence from a Flypaper Effect." American Economic Review 99, no. 5 (December 1, 2009): 2085–95. http://dx.doi.org/10.1257/aer.99.5.2085.

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Consistent with mental accounting, we document that investors sometimes choose the asset allocation for one account without considering the asset allocation of their other accounts. The setting is a firm that changed its 401(k) matching rules. Initially, 401(k) enrollees chose the allocation of their own contributions, but the firm chose the match allocation. These enrollees ignored the match allocation when choosing their own-contribution allocation. In the second regime, enrollees selected both accounts' allocations, leading them to integrate the two. Own-contribution allocations before the rule change equal the combined own- and match-contribution allocations afterward, whereas combined allocations differ sharply across regimes. (JEL G11, J32)

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19

Jahnke, William W. "”Digestible” Asset Allocation." Journal of Investing 7, no. 1 (February 28, 1998): 9–11. http://dx.doi.org/10.3905/joi.1998.408445.

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20

Ibbotson, Roger G., and Charles H. Wang. "Global Asset Allocation." Journal of Investing 9, no. 1 (February 29, 2000): 39–51. http://dx.doi.org/10.3905/joi.2000.319398.

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21

Singh, Ishmeet. "Dynamic asset allocation." International Journal of Recent Scientific Research 08, no. 05 (May 28, 2017): 17204–8. http://dx.doi.org/10.24327/ijrsr.2017.0805.0304.

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22

Sharpe, William F. "Integrated Asset Allocation." Financial Analysts Journal 43, no. 5 (September 1987): 25–32. http://dx.doi.org/10.2469/faj.v43.n5.25.

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23

Trammell, Susan. "Adaptive Asset Allocation." CFA Institute Magazine 22, no. 6 (November 2011): 47–49. http://dx.doi.org/10.2469/cfm.v22.n6.10.

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24

Fong, H. Gifford. "Dynamic Asset Allocation." ICFA Continuing Education Series 1987, no. 1 (January 1987): 82–85. http://dx.doi.org/10.2469/cp.v1987.n1.12.

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25

Goodsall, William A. R. "Tactical Asset Allocation." AIMR Conference Proceedings 1998, no. 6 (December 1998): 102–10. http://dx.doi.org/10.2469/cp.v1998.n6.10.

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26

Merciai, Patrizio. "Global Asset Allocation." AIMR Conference Proceedings 1998, no. 6 (December 1998): 31–45. http://dx.doi.org/10.2469/cp.v1998.n6.4.

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27

Philips, Thomas K., Greg T. Rogers, and Robert E. Capaldi. "Tactical Asset Allocation." Journal of Portfolio Management 23, no. 1 (October 31, 1996): 57–64. http://dx.doi.org/10.3905/jpm.1996.409576.

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28

Ennis, Richard M. "Parsimonious Asset Allocation." Financial Analysts Journal 65, no. 3 (May 2009): 6–10. http://dx.doi.org/10.2469/faj.v65.n3.2.

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29

Brennan, Michael J., Eduardo S. Schwartz, and Ronald Lagnado. "Strategic asset allocation." Journal of Economic Dynamics and Control 21, no. 8-9 (June 1997): 1377–403. http://dx.doi.org/10.1016/s0165-1889(97)00031-6.

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30

Tütüncü, R. H., and M. Koenig. "Robust Asset Allocation." Annals of Operations Research 132, no. 1-4 (November 2004): 157–87. http://dx.doi.org/10.1023/b:anor.0000045281.41041.ed.

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31

Estrada, Javier. "GHAUS asset allocation." Journal of Asset Management 17, no. 1 (December 28, 2015): 1–9. http://dx.doi.org/10.1057/jam.2015.28.

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32

Madhogarhia, Pawan K., and Marco Lam. "Dynamic asset allocation." Journal of Asset Management 16, no. 5 (August 26, 2015): 293–302. http://dx.doi.org/10.1057/jam.2015.4.

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33

Rajyaguru, Jyoti Umesh. "A study on balancing risk and return through asset allocation." RESEARCH REVIEW International Journal of Multidisciplinary 8, no. 11 (November 14, 2023): 151–54. http://dx.doi.org/10.31305/rrijm.2023.v08.n11.023.

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Increased awareness of asset allocation is a positive development in the world of investing. Asset allocation is a crucial investment strategy that involves dividing an investor's portfolio among different asset classes, such as stocks, bonds, cash, real estate, and commodities. The goal of asset allocation is to balance the risk and return of an investment portfolio by diversifying across different types of assets with varying levels of risk and return potential.

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34

Sun, Yuqin, Yungao Wu, and Gejirifu De. "A Novel Black-Litterman Model with Time-Varying Covariance for Optimal Asset Allocation of Pension Funds." Mathematics 11, no. 6 (March 17, 2023): 1476. http://dx.doi.org/10.3390/math11061476.

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The allocation of pension funds has important theoretical value and practical significance, which improves the level of pension investment income, achieves the maintenance and appreciation of pension funds, and resolves the pension payment risk caused by population aging. The asset allocation of pension funds is a long-term asset allocation problem. Thus, the long-term risk and return of the assets need to be estimated. The covariance matrix is usually adopted to measure the risk of the assets, while calculating the long-term covariance matrix is extremely difficult. Direct calculations suffer from the insufficiency of historical data, and indirect calculations accumulate short-term covariance, which suffers from the dynamic changes of the covariance matrix. Since the returns of main assets are highly autocorrelated, the covariance matrix of main asset returns is time-varying with dramatic dynamic changes, and the errors of indirect calculation cannot be ignored. In this paper, we propose a novel Black–Litterman model with time-varying covariance (TVC-BL) for the optimal asset allocation of pension funds to address the time-varying nature of asset returns and risks. Firstly, the return on assets (ROA) and the covariance of ROA are modeled by VARMA and GARCH, respectively. Secondly, the time-varying covariance estimation of ROA is obtained by introducing an effective transformation of the covariance matrix from short-term to long-term. Finally, the asset allocation decision of pension funds is achieved by the TVC-BL model. The results indicate that the proposed TVC-BL pension asset allocation model outperforms the traditional BL model. When the risk aversion coefficient is 1, 1.5, and 3, the Sharp ratio of pension asset allocation through the TVC-BL pension asset allocation model is 13.0%, 10.5%, and 12.8% higher than that of the traditional BL model. It helps to improve the long-term investment returns of pension funds, realize the preservation and appreciation of pension funds, and resolve the pension payment risks caused by the aging of the population.

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35

Zhu, Junyang. "Industry Asset Allocation Portfolio Analysis." BCP Business & Management 26 (September 19, 2022): 150–58. http://dx.doi.org/10.54691/bcpbm.v26i.1871.

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Since the beginning of the 21st century, there have been many excellent fund managers, but excellent asset portfolios are rare. Therefore, it is of great interests to implement in-depth investigations on this issue. In this paper, Markowitz model and Index model are selected to measure the returns and volatility of ten different excellent assets to get a better result. At the same time, according to the preferences of different customers, five different constraints are selected to meet the characteristics of more groups. Based on this, this paper optimizes the asset portfolio. This result shows that: First, when the Markowitz model and the Index model are combined with the capital market line and the efficient frontier, it is very effective to apply it to the risk asset portfolio. Second, Alaska Air Group and Hawaiian Holdings tend to be short under various constraints, while Wells Fargo is the opposite. It is of great significance to the research on the optimal allocation of financial assets in the industry.

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36

Duan, Jun, Tingting Liu, Xiaoran Yang, Hua Yang, and Yunwei Gao. "Financial asset allocation and green innovation." Green Finance 5, no. 4 (2023): 512–37. http://dx.doi.org/10.3934/gf.2023020.

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<abstract> <p>Sustainable development is a key issue of global concern, and countries around the world are striving to promote green development. From the perspective of financial asset allocation motivation, this paper explores the impact of financial asset allocation on green innovation based on the data of A-share listed non-financial companies from 2011 to 2021. First, there is an inverted U-shaped relationship between the proportion of financial asset allocation and the green innovation of physical enterprises, that is, as the proportion of financial asset allocation increases, the green innovation output of enterprises first increases and then decreases. After robustness testing, the conclusion still holds. Second, further testing of the intermediary mechanism shows that the moderate holding of short-term financial assets by real enterprises can increase the output of green innovation by alleviating financing constraints, which is manifested as the "reservoir" effect. The "crowding out" effect plays a leading role when overallocation of financial assets reduces liquidity supply and capital expenditure, which in turn reduces green innovation output. Third, in the test of financial asset allocation preference, it is found that the short-term financial assets held by enterprises mainly play a "reservoir" effect, that is, they tend to be "preventive" motives. Holding long-term financial assets mainly exerts a "crowding out" effect, that is, tends to "seek profits" motives. Finally, there are differences in the impact of financial asset allocation on green innovation output among enterprises with different property rights, different monetary policies and different social responsibilities.</p> </abstract>

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37

Duan, Jun, Tingting Liu, Xiaoran Yang, Hua Yang, and Yunwei Gao. "Financial asset allocation and green innovation." Green Finance 5, no. 4 (2023): 512–37. http://dx.doi.org/10.3934/gf.2023021.

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<abstract> <p>Sustainable development is a key issue of global concern, and countries around the world are striving to promote green development. From the perspective of financial asset allocation motivation, this paper explores the impact of financial asset allocation on green innovation based on the data of A-share listed non-financial companies from 2011 to 2021. First, there is an inverted U-shaped relationship between the proportion of financial asset allocation and the green innovation of physical enterprises, that is, as the proportion of financial asset allocation increases, the green innovation output of enterprises first increases and then decreases. After robustness testing, the conclusion still holds. Second, further testing of the intermediary mechanism shows that the moderate holding of short-term financial assets by real enterprises can increase the output of green innovation by alleviating financing constraints, which is manifested as the "reservoir" effect. The "crowding out" effect plays a leading role when overallocation of financial assets reduces liquidity supply and capital expenditure, which in turn reduces green innovation output. Third, in the test of financial asset allocation preference, it is found that the short-term financial assets held by enterprises mainly play a "reservoir" effect, that is, they tend to be "preventive" motives. Holding long-term financial assets mainly exerts a "crowding out" effect, that is, tends to "seek profits" motives. Finally, there are differences in the impact of financial asset allocation on green innovation output among enterprises with different property rights, different monetary policies and different social responsibilities.</p> </abstract>

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38

Konno, Hiroshi, and Jing Li. "Internationally Diversified Investment Using an Integrated Portfolio Model." International Journal of Theoretical and Applied Finance 01, no. 01 (January 1998): 145–60. http://dx.doi.org/10.1142/s0219024998000072.

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In this paper, we use a new integrated portfolio model which takes care of stocks and bonds of several countries to construct an internationally diversified portfolio. This serves as an alternative to the popular asset allocation strategy, in which the fund is first allocated to indices corresponding to diverse asset classes and then allocated to individual assets using appropriate models for each asset class. Our model, on the other hand, determines the allocation of the fund to individual assets in one stage by solving a large scale mean-variance or mean-absolute deviation model. Another important feature of this article is a newly developed strategy for hedging the exchange rate risk by using forward contracts on currencies. Computational experiments using historical data collected in the capital market show that the new approach can serve as a more reliable and less expensive method for allocating the fund to diverse classes of assets.

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39

Li, Yanqi. "Research on Household Financial Asset Allocation Based on Population Aging." Modern Economics & Management Forum 5, no. 3 (July 17, 2024): 438. http://dx.doi.org/10.32629/memf.v5i3.2357.

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With the intensification of population aging, household financial asset allocation is facing new challenges. This paper analyzes the trend of population aging and its impact on the financial market, and discusses the relative changes of high risk assets and low risk assets in household financial asset allocation, and how to allocate assets according to households' risk appetite and investment goals. This paper reviews and analyzes relevant literature at home and abroad, and uses the data of China Household Finance Survey (CHFS) from 2015 to 2019 to explore the impact of population aging on household financial asset allocation.

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40

Layard-Liesching, Ronald G. "Risk Allocation Instead of Asset Allocation." AIMR Conference Proceedings 1998, no. 6 (December 1998): 126–35. http://dx.doi.org/10.2469/cp.v1998.n6.13.

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41

Huang, Kun, Qiuge Yao, and Chong Li. "Impacts of Financial Market Shock on Bank Asset Allocation from the Perspective of Financial Characteristics of Banks." International Journal of Financial Studies 7, no. 2 (June 12, 2019): 29. http://dx.doi.org/10.3390/ijfs7020029.

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Given ongoing financial disintermediation and the need for central banks to establish interest rate corridors, commercial banks have increasingly enriched their asset allocation choices, forming an allocation pattern that combines traditional credit assets (loans) and financial assets (interbank and securities investment). Due to the long-standing dual interest rate system in China, the yields of credit assets and financial assets have differed, which means the latter has greater volatility. Using the quarterly panel data of 23 listed commercial banks in China from 2002 to 2017, the empirical results of this paper show that the fluctuation of the return rate of the two types of assets will affect the asset allocation of banks. Specifically, on the one hand, when the price of financial assets falls, which leads to the narrowing of the credit spread between the two types of assets, banks reduce transaction demand to prevent loss and reduce their holdings of financial assets, thus increasing the ratio of their credit assets to financial assets. On the other hand, rising benchmark lending rates leads to the increase in the credit financing cost of demanders, reducing the willingness of demanders to lend, forcing the demander to obtain funds through other channels. This results in the decrease in the ratio of credit assets to financial assets. Furthermore, the financial characteristics of banks also influence the dynamic adjustment range of asset allocation. That is, the lower the reserve ratio and capital adequacy ratio, the smaller the impact of financial asset yield volatility on bank asset allocation.

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42

Ling, Yunting, Zihan Jiang, and Shiyu Liu. "Optimal Asset Allocation Model during the Economic Recession." BCP Business & Management 46 (June 8, 2023): 159–71. http://dx.doi.org/10.54691/bcpbm.v46i.5092.

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Due to the impact of the COVID-19 pandemic, the economic situation of various industries around the world has been affected to varying degrees. Different asset types are affected in different ways. As most non-institutional investors lack sufficient professional skills, investors tend to invest in low-risk assets. Some investors only invest in a single asset without a reasonable portfolio allocation of multiple assets, resulting in the risk of one asset being equal to or higher than the portfolio investment of multiple assets. This report mainly uses the CAPM model, based on financial data from China and America from 2020 to 2022, to analyze the correlation coefficients, alpha, beta, and excess returns between different time series and different variables to get a Sharpe ratio, aiming to find the optimal model in various asset allocation combinations (stock indexes, bonds, commodity futures, and cryptocurrency). This paper mainly analyzes the forecasting ability of future returns of different asset types through a time series model and risk premium scatter diagram.

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43

Wu, Wen-Lin. "Where to make an investment? If home political risk occurs." Corporate Ownership and Control 12, no. 1 (2014): 589–98. http://dx.doi.org/10.22495/cocv12i1c6p6.

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n this paper, we put political risk into the model of international asset allocation to analyze international investors’ decisions. We assume that when home investors have perceived home political risks, they override other factors of their portfolio decision and move to hold more foreign assets to hedge those risks. To model political risk, we use a stochastic differential equation with a Poisson jump diffusion process to simulate international asset allocation. The numerical result confirms our hypothesis, i.e., foreign bias exists. That is, home investors would prefer to hold more foreign assets than the optimal asset allocation to hedge against home political risk

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44

Wang, Xiaowei, Rui Wang, and Yichun Zhang. "Cross-asset momentum and the hybrid fund transmission mechanism in China’s stock and bond markets." PLOS ONE 19, no. 3 (March 21, 2024): e0300781. http://dx.doi.org/10.1371/journal.pone.0300781.

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The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China’s financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market’s return, while the momentum in the bond market positively influences the stock market’s return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China’s financial market.

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45

Cloutier, Richard, Arsen Djatej, and Dean Kiefer. "A tactical asset allocation strategy that exploits variations in VIX." Investment Management and Financial Innovations 14, no. 1 (March 31, 2017): 27–34. http://dx.doi.org/10.21511/imfi.14(1).2017.03.

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Buy and hold strategies make staying disciplined difficult for investors, especially given the variability of returns for different asset classes/strategies during divergent market conditions. Market timing strategies, on the other hand, present significant theoretical benefits, but in reality these benefits are difficult to obtain. Tactical asset allocation, where limited deviations from the strategic allocation are allowed permits the portfolio manager to take advantage of market conditions fits between these two extremes. The authors correlate daily returns for each of eighteen separate asset classes typically used in diversified institutional portfolios and daily closing values of the VIX (the ticker symbol for the Chicago Board Options Exchange Volatility Index). This information is used to select those classes whose returns are most responsive to the level of the VIX. Portfolio allocations for eight selected asset classes are revised depending on the level of the VIX at the daily close of the market. The portfolio is rebalanced on the business day following the day the VIX hits the trigger value. The VIX tactical allocation overlay yields an increase in return over the buy and hold portfolio of approximately 38 basis points. The authors conclude that the tactical asset allocation strategy based on the level of VIX provides a higher return than the neutral buy and hold allocation with a higher Sharpe ratio and lower volatility.

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46

Reichenstein, William R. "Asset Allocation and Asset Location Decisions Revisited." Journal of Wealth Management 4, no. 1 (April 30, 2001): 16–26. http://dx.doi.org/10.3905/jwm.2001.320399.

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47

Jayakody, J. A. N. N., M. C. M. Nasvi, D. J. Robert, S. K. Navaratnarajah, L. C. Kurukulasuriya, F. Giustozzi, C. Gunasekara, and S. Setunge. "Development of a Cross-Asset Model for the Maintenance of Road and Water Pipe Assets using AHP Method." Civil Engineering Journal 10, no. 2 (February 1, 2024): 336–61. http://dx.doi.org/10.28991/cej-2024-010-02-01.

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Roads and water pipe assets undergo various deterioration processes due to the high demand for their services. Maintenance of these assets is often planned as individual assets, and the interdependency among different assets is neglected. An integrated framework for cross-asset maintenance is required for optimum utilization of the available funds for asset maintenance. To date, there are very few studies focusing on the use of the analytical hierarchy process (AHP) for cross-asset maintenance of roads and water pipe assets. Therefore, this research aims to develop an integrated fund allocation model for the maintenance of road and water pipe assets. A model was developed using AHP analysis based on expert opinions captured through a questionnaire in order to obtain optimum maintenance fund allocation for the cross-assets, roads, and water pipes. Then, a case study corridor segment with the considered cross-assets was selected, and a trade-off analysis was conducted for the intervention alternatives considering different levels of service (LOS) of the asset elements. The results of the trade-off analysis can be used to identify the optimum intervention alternative that satisfies the budget requirement and results in the maximum benefit. Overall, asset managers can use the approach presented in the present study to develop a cross-asset fund allocation model when multiple assets are involved in maintenance. Doi: 10.28991/CEJ-2024-010-02-01 Full Text: PDF

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Wei, Pei. "Long-term General Asset Allocation for individual investors in Chinese securities market." BCP Business & Management 20 (June 28, 2022): 1207–16. http://dx.doi.org/10.54691/bcpbm.v20i.1120.

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Facing the boom of online transaction applications in Chinese securities market, individual investors in China vacillate between different assets while allocating assets. On account of the individual investors’ inferior ability to take risk, portfolio will be their best choices. However due to their unacquaintance to modern portfolio theory, individual investors need some instructions. As proved by Brinson in 1986, over 90% of the success of a portfolio owe to general asset selection. Hence, this paper primarily focused on providing suggestions to individual investors on general asset selection to help them to construct a portfolio in China securities market at present. Initially, this paper utilized the Mean-Variance model and CML approach to depict the market and analyzed the long-term asset allocation of multi-asset for individual investors. Based on the results, this paper suggested that individual investors should focus more on bond asset: with a portfolio of 97.37% of bond asset and 2.63% of stock asset, investors will be able to acquire a yield of 4.54% (annualized). And through this study, this paper found that Chinese securities market is not mature enough and teemed with regulations, making it to be competent than other mature market.

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Sun, Jin, Dan Zhu, and Eckhard Platen. "DYNAMIC ASSET ALLOCATION FOR TARGET DATE FUNDS UNDER THE BENCHMARK APPROACH." ASTIN Bulletin 51, no. 2 (March 29, 2021): 449–74. http://dx.doi.org/10.1017/asb.2021.6.

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ABSTRACTTarget date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

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Cairns, Andrew. "Some Notes on the Dynamics and Optimal Control of Stochastic Pension Fund Models in Continuous Time." ASTIN Bulletin 30, no. 1 (May 2000): 19–55. http://dx.doi.org/10.2143/ast.30.1.504625.

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AbstractThis paper discusses the modelling and control of pension funds.A continuous-time stochastic pension fund model is proposed in which there are n risky assets plus the risk-free asset as well as randomness in the level of benefit outgo. We consider Markov control strategies which optimise over the contribution rate and over the range of possible asset-allocation strategies.For a general (not necessarily quadratic) loss function it is shown that the optimal proportions of the fund invested in each of the risky assets remain constant relative to one another. Furthermore, the asset allocation strategy always lies on the capital market line familiar from modern portfolio theory.A general quadratic loss function is proposed which provides an explicit solution for the optimal contribution and asset-allocation strategies. It is noted that these solutions are not dependent on the level of uncertainty in the level of benefit outgo, suggesting that small schemes should operate in the same way as large ones. The optimal asset-allocation strategy, however, is found to be counterintuitive leading to some discussion of the form of the loss function. Power and exponential loss functions are then investigated and related problems discussed.The stationary distribution of the process is considered and optimal strategies compared with dynamic control strategies.Finally there is some discussion of the effects of constraints on contribution and asset-allocation strategies.

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