Cryptocurrencies and traditional assets: the empirical study in dynamic linkages and portfolio optimization
| dc.contributor.advisor | Sorasart Sukcharoensin | th |
| dc.contributor.author | Watcharaporn Kantaphayao | th |
| dc.date.accessioned | 2022-02-28T07:17:10Z | |
| dc.date.available | 2022-02-28T07:17:10Z | |
| dc.date.issued | 2021 | th |
| dc.date.issuedBE | 2564 | th |
| dc.description | Thesis (Ph.D. (Economics))--National Institute of Development Administration, 2021 | th |
| dc.description.abstract | The paper generates the top 5-coin index and the top 5-token index and treats them as the cryptocurrency price to investigate for three main objectives. First, it has to investigate the short-term dynamic spillover between cryptocurrency and main traditional assets. Second, it has to examine the fundamental characters of both coin and token by long-term relationships with other traditional assets, as well as forecasting price ability. Third, it has to analyze the asset allocation for the portfolio optimization approach by applying the dynamic conditional correlations, calculated by the Dynamic Conditional Correlation (DCC)-GARCH (1,1) model. The results reveal that coin and token are positively long-term related to each other. The developed stock market also has a negative long-term relationship with coin, while it has a positive long-term relationship with token. The fixed income asset has a positive long-term relationship with coin and token. Meanwhile, the commodity asset has a positive long-term relationship with token. For the short-term spillover, coin return causes token, stock and gold returns. Meanwhile, stock return causes token return. Coin and token return immediately highest responded by the shock of each other by the first period. The responses of coin and token returns from the shock of other markets are not significant. Besides, the shock of main traditional assets does not affect the cryptocurrency volatility. Therefore, the cryptocurrencies might be of benefit to portfolio diversification due to their minor linkages with other financial assets. Due to the long-term cointegration relationship and some short-term dynamic spillover among coin, token, and developed stock market through MSCI international world price market, these assets will be acted as an exogenous variable for price forecasting. Using data from January 2017 To December 2019, the results reveal that the ARIMAX model with the developed stock market as an exogenous variable and the VAR model depended on lagged variable of developed stock market are relatively reliable to forecast coin and token comparing to RW and ARIMA models using out-of-sample forecasting period over 20 days horizon ahead. However, longer forecasting horizon has provided higher estimation errors; therefore, the forecasting ability of cryptocurrency would be effective in the short-term period. Then, this paper has focused on the discussion of the portfolio optimization and diversification benefits by investment in coin and token. The static correlation and dynamic conditional correlation have been applied to form portfolio optimization. The dynamic conditional correlation has been calculated from DCC-GARCH (1,1) model. As the results of the static and dynamic conditional correlations, this paper finds that coin and token are moderately positive correlated with each other. Meanwhile, they have extremely low correlations with the other traditional assets. Therefore, the cryptocurrency might be an alternative asset class to benefit for portfolio diversification. The optimal portfolios formed by the DCC-GARCH (1,1) model have provided the huge Sharp Ratio rather than the static portfolios. Furthermore, this paper also finds that the actively adjusted weight portfolios provide a huge average annual return and higher standard deviation rather than fixed weights of the investment portfolio. The higher standard deviation is tiny when comparing to the huge increase of the average return. So, the Sharpe Ratio of actively adjusted weight portfolios then is better than the fixed weight portfolio. The portfolios performed well, which presented in terms of average annual return, Sharpe Ratio, and Compound Annual Growth Rate (CAGR), always have either coin or token in there. Nonetheless, due to the high-risk level of cryptocurrencies, including coin and token, the investors should be careful to invest. They should have always re-considered about the weights of investment in either coin or token. The optimal weights of investment in coin and token of each period should be quite low rather than other traditional assets, which are well-known about fundamental movement. | th |
| dc.description.abstract | The paper generates the top 5-coin index and the top 5-token index and treats them as the cryptocurrency price to investigate for three main objectives. First, it has to investigate the short-term dynamic spillover between cryptocurrency and main traditional assets. Second, it has to examine the fundamental characters of both coin and token by long-term relationships with other traditional assets, as well as forecasting price ability. Third, it has to analyze the asset allocation for the portfolio optimization approach by applying the dynamic conditional correlations, calculated by the Dynamic Conditional Correlation (DCC)-GARCH (1,1) model. The results reveal that coin and token are positively long-term related to each other. The developed stock market also has a negative long-term relationship with coin, while it has a positive long-term relationship with token. The fixed income asset has a positive long-term relationship with coin and token. Meanwhile, the commodity asset has a positive long-term relationship with token. For the short-term spillover, coin return causes token, stock and gold returns. Meanwhile, stock return causes token return. Coin and token return immediately highest responded by the shock of each other by the first period. The responses of coin and token returns from the shock of other markets are not significant. Besides, the shock of main traditional assets does not affect the cryptocurrency volatility. Therefore, the cryptocurrencies might be of benefit to portfolio diversification due to their minor linkages with other financial assets. Due to the long-term cointegration relationship and some short-term dynamic spillover among coin, token, and developed stock market through MSCI international world price market, these assets will be acted as an exogenous variable for price forecasting. Using data from January 2017 To December 2019, the results reveal that the ARIMAX model with the developed stock market as an exogenous variable and the VAR model depended on lagged variable of developed stock market are relatively reliable to forecast coin and token comparing to RW and ARIMA models using out-of-sample forecasting period over 20 days horizon ahead. However, longer forecasting horizon has provided higher estimation errors; therefore, the forecasting ability of cryptocurrency would be effective in the short-term period. Then, this paper has focused on the discussion of the portfolio optimization and diversification benefits by investment in coin and token. The static correlation and dynamic conditional correlation have been applied to form portfolio optimization. The dynamic conditional correlation has been calculated from DCC-GARCH (1,1) model. As the results of the static and dynamic conditional correlations, this paper finds that coin and token are moderately positive correlated with each other. Meanwhile, they have extremely low correlations with the other traditional assets. Therefore, the cryptocurrency might be an alternative asset class to benefit for portfolio diversification. The optimal portfolios formed by the DCC-GARCH (1,1) model have provided the huge Sharp Ratio rather than the static portfolios. Furthermore, this paper also finds that the actively adjusted weight portfolios provide a huge average annual return and higher standard deviation rather than fixed weights of the investment portfolio. The higher standard deviation is tiny when comparing to the huge increase of the average return. So, the Sharpe Ratio of actively adjusted weight portfolios then is better than the fixed weight portfolio. The portfolios performed well, which presented in terms of average annual return, Sharpe Ratio, and Compound Annual Growth Rate (CAGR), always have either coin or token in there. Nonetheless, due to the high-risk level of cryptocurrencies, including coin and token, the investors should be careful to invest. They should have always re-considered about the weights of investment in either coin or token. The optimal weights of investment in coin and token of each period should be quite low rather than other traditional assets, which are well-known about fundamental movement. | th |
| dc.format.extent | 180 leaves | th |
| dc.format.mimetype | application/pdf | th |
| dc.identifier.doi | 10.14457/NIDA.the.2021.40 | |
| dc.identifier.other | b212247 | th |
| dc.identifier.uri | https://repository.nida.ac.th/handle/662723737/5525 | th |
| dc.language.iso | eng | th |
| dc.publisher | National Institute of Development Administration | th |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | th |
| dc.subject | e-Thesis | th |
| dc.subject | Traditional assets | th |
| dc.subject | Dynamic linkages | th |
| dc.subject | Portfolio optimization | th |
| dc.subject.other | Cryptocurrencies | th |
| dc.title | Cryptocurrencies and traditional assets: the empirical study in dynamic linkages and portfolio optimization | th |
| dc.type | text--thesis--doctoral thesis | |
| mods.genre | Dissertation | |
| mods.physicalLocation | National Institute of Development Administration. Library and Information Center | th |
| thesis.degree.department | School of Development Economics | th |
| thesis.degree.discipline | Economics | th |
| thesis.degree.grantor | National Institute of Development Administration | th |
| thesis.degree.level | Doctoral | th |
| thesis.degree.name | Doctor of Philosophy | th |