Asset Portfolio Investment Strategies Based on Econometric Models

Xiting Wang
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Abstract

In order to provide traders with a daily trading strategy for gold and bitcoin, a price forecasting model was first developed. After performing ADF stationarity tests and LM tests on returns, the ARMA (3,7) and ARMA (4,5) models are estimated using THE PERTUR (3,7) and ALM (5) models with perturbation terms GARCH (1,1) and vt following a t-distribution. The second step is to build a risk judgement model that considers the impact of the special bull and bear market quotes, sets the bull score and determines its weight as 0.333. The third step is to build a decision model that introduces a purchase score and builds a planning equation based on the fact that gold is only traded on trading days to obtain the final decision. With this model, a principal of $1,000 could end up with an asset of $164,080. MCM/ICM: Procedures and techniques for a great experience. Proving that the model used is the optimal strategy is done in two parts: an accuracy test and an analysis of investment behavior. The accuracy test introduces the regression evaluation metrics MSE and RMSE and the results show that the portfolio investment model is the most accurate. The analysis of investment behavior demonstrates the failure of one-way investments by comparing complete bitcoin investments or gold investments with the information reviewed.
基于计量经济模型的资产组合投资策略
为了给交易者提供黄金和比特币的日常交易策略,首先开发了一个价格预测模型。在对收益进行ADF平稳性检验和LM检验后,使用PERTUR(3,7)和ALM(5)模型估计ARMA(3,7)和ARMA(4,5)模型,扰动项GARCH(1,1)和vt服从t分布。第二步是建立考虑牛熊特殊行情影响的风险判断模型,设定牛分并确定其权重为0.333。第三步是建立决策模型,引入购买分数,并根据黄金只在交易日交易的事实建立规划方程,以获得最终决策。在这个模型下,1000美元的本金最终可以得到164080美元的资产。MCM/ICM:获得良好体验的程序和技巧。通过准确性检验和投资行为分析两部分来证明所使用的模型是最优策略。准确性检验引入回归评价指标MSE和RMSE,结果表明组合投资模型最准确。通过比较完整的比特币投资或黄金投资与审查的信息,对投资行为的分析表明单向投资的失败。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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