The Implementation of Asset Allocation Approaches: Theory and Evidence

Su, Jung-Bin

doi:10.3390/su12177162

Cited by 2 publications

(4 citation statements)

References 30 publications

(70 reference statements)

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“…In this subsection, we explore the issue of optimal asset allocation for the ten paired markets or 23 pairs of assets during the pre- and post-COVID-19 subperiods to examine the variation of performance of asset allocation in the crisis era of the COVID-19 pandemic. The optimal portfolio is the minimum variance portfolio (MVP) obtained from the MDWI approach of Su ( 41 ) or Kroner and Ng ( 42 ). The performance of asset allocation is measured by the risk-adjusted return.…”

Section: Resultsmentioning

confidence: 99%

“…2. ρ denotes the average value of correlation for a pair of assets during a specific period. w 1 is the average weight forecast of the first component asset of the minimum variance portfolio (MVP) based on the MDWI approach of Su ( 41 ) or Kroner and Ng ( 42 ). 3.…”

Section: Resultsmentioning

confidence: 99%

“…This study used the Mean Dynamic Weight for the In-sample period (MDWI) approach of Su ( 41 ) or Kroner and Ng ( 42 ) to find the weight forecasts of the minimum variance portfolio (MVP) and then allocated the capital to the component assets to construct an optimal portfolio based on the minimum risk. 15 Thus, the following technique of mathematical programming is used to determine the weights of the bivariate MVP.…”

Section: Methodsmentioning

confidence: 99%

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How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?

Kao

2022

Front. Public Health

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This study examines how the COVID-19 pandemic crisis affects the interactions between the stock, oil, gold, currency, and cryptocurrency markets. The impacts of the COVID-19 pandemic crisis on the optimal asset allocation and optimal hedged strategy are also discussed. Empirical results show that the volatility spillover significantly exists in most of the ten paired markets whereas the return spillover and correlation are significant only for the few paired markets. Moreover, the impact of the COVID-19 pandemic on the return spillover is the greatest followed by the correlation whereas the volatility spillover is not affected by the COVID-19 pandemic. Furthermore, the Quantitative easing (QE) implemented after the COVID-19 pandemic crisis increases the risk-adjusted return for each asset and minimum variance portfolio (MVP) and raises the correlation between two assets. In addition, most of the pairs of assets are not suitable to hedge each other except for a few pairs of assets. Regarding these few pairs of assets, the optimal hedge asset with the fewer hedge cost is accompanied by less risk reduction and vice versa. Finally, the investors should choose the euro to construct a portfolio to achieve risk diversification and to hedge gold or WTI to get the risk reduction. The above findings can help investors and fund managers make a useful investment strategy, optimal asset allocation, and effective hedged strategy. For example, the investors can use the volatility of one market to predict the volatility of another market and they can take a long position during the post-COVID-19 period but they should withdraw capital from the market when the QE tapering is executed.JEL classificationC52; C53; G15.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?

Kao

2022

Front. Public Health

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“…Hence, volatility can be used to measure the amount of uncertainty or risk related to the size of changes in an asset’s price, and it obeys the criteria: ‘the higher the volatility and then the riskier the asset’. Because of the above property for volatility, volatility is usually used in asset allocation [ 1 , 2 , 3 ], option pricing [ 4 , 5 ], risk management [ 6 , 7 , 8 , 9 ] and hedge strategy [ 10 , 11 ]. Thus, how to accurately predict the volatility of an asset is a very important issue in the actual investment process in the financial field.…”

Section: Introductionmentioning

confidence: 99%

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

2021

Entropy

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This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect and non-normal return distribution can promote the performance of volatility forecasting, and which one of the sixteen models possesses the best volatility forecasting performance. The eight parametric volatility forecasts models are composed of the generalized autoregressive conditional heteroskedasticity (GARCH) or GJR-GARCH volatility specification combining with the normal, Student’s t, skewed Student’s t, and generalized skewed Student’s t distributions. Empirical results show that, the performance for the composed volatility forecasting approach is significantly superior to that for the parametric volatility forecasting approach. Furthermore, the GJR-GARCH volatility specification has better performance than the GARCH one. In addition, the non-normal distribution does not have better forecasting performance than the normal distribution. In addition, the GJR-GARCH model combined with both the normal distribution and a neural network approach has the best performance of volatility forecasting among sixteen models. Thus, a neural network approach significantly promotes the performance of volatility forecasting. On the other hand, the setting of leverage effect can encourage the performance of volatility forecasting whereas the setting of non-normal distribution cannot.

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The Implementation of Asset Allocation Approaches: Theory and Evidence

Cited by 2 publications

References 30 publications

How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?

How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

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