VAR and VECM models were used to investigate the factors that influence of Indian securities market performance, including the period of Covid 19's financial crises

Lotica Surana
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Abstract

Using a dummy variable, we evaluate commodities in addition to macroeconomic considerations of the Indian securities market from 2010 to 2021, which includes the era of the Covid 19 crises. We used the Bombay Stock Exchange (BSE) (Sensex) for securities market performance and developed a Vector Auto regressive models that combines the short- and long-run model of economics. On stock price indexes, we discovered that the Indian securities market reflects both macroeconomic indicators and prices of commodity. Growth in the economy, inflation, interest, rates, currency rates, crude oil prices, and gold prices are all factors to consider were all used in this study to see how they affected BSE (Sensex) prices during the Covid 19 crises. In their first difference, all series were judged to be stationary. We discovered that shocks to all eight factors had both positive and negative effects on BSE (Sensex) prices in the short and long term, including Covid 19 crises. Each securities market index’s most significant impulse is its own shock, decreasing from short to long-term. We also used the Joint Co-Integration Test to detect and confirm the lack of a long-term equilibrium link (cointegration) between all eight variables, resulting in four cointegration equations with an estimated error correction term at the 0.05 level (speed of adjustment towards equilibrium) of 0.007362. Vector Error Correction Mode (VECM), on the other hand, suggests that the BSE (Sensex) has a significant value with its lagged values of.007362 and 0.517952. We created Vector Auto regressive (VAR) models for the BSE (Sensex) using eight independent variables, including Dummy variables, but their statistics were not significant, despite the fact that the lagged value of crude oil, gold prices, the rupee, and the BSE (Sensex) lagged value were all significant. We proceed to estimate VECM. We proved the short and long-term effects of lagged BSE(Sensex) prices, crude oil prices, gold prices, and the currency on the BSE (SENSEX) using several robustness tests. Dummy factors have also been included to see how the Covid 19 crisis affected the BSE (Sensex) prices. We discovered that crude oil prices followed value, gold prices lagged value, and rupees based on the dollar had a significant impact on BSE(Sensex) pricing over the study period, including the Covid 19 crises from March 2020 to June 2021.
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使用 VAR 和 VECM 模型研究了影响印度证券市场表现的因素,包括科维德 19 年金融危机期间的影响因素。
利用虚拟变量,我们对 2010 年至 2021 年印度证券市场的宏观经济考虑因素之外的商品进行了评估,其中包括科维德 19 危机时代。我们使用孟买证券交易所(BSE)(Sensex)来衡量证券市场的表现,并建立了一个结合短期和长期经济学模型的向量自回归模型。在股价指数方面,我们发现印度证券市场同时反映了宏观经济指标和商品价格。经济增长、通货膨胀、利率、货币汇率、原油价格和黄金价格都是需要考虑的因素,在本研究中都使用了这些因素,以了解它们在科维德 19 危机期间对上证指数(Sensex)价格的影响。在第一次差分中,所有序列都被判定为静态。我们发现,从短期和长期来看,包括 Covid 19 危机在内,所有八个因素的冲击都会对上证指数(Sensex)价格产生正面和负面影响。每个证券市场指数最显著的脉冲是其自身的冲击,从短期到长期依次递减。我们还使用联合协整检验来检测并确认所有八个变量之间缺乏长期均衡联系(协整),结果得出四个协整方程,在 0.05 水平(向均衡调整的速度)上估计的误差修正项为 0.007362。另一方面,向量误差修正模式(VECM)表明,上证指数(Sensex)的滞后值为.007362 和 0.517952,具有显著价值。我们使用包括虚拟变量在内的八个自变量为上证指数建立了向量自回归(VAR)模型,但尽管原油、黄金价格、卢比和上证指数的滞后值都很显著,但它们的统计量并不显著。我们继续估计 VECM。我们通过几种稳健性检验证明了上证指数(SENSEX)的滞后价格、原油价格、黄金价格和货币对上证指数(SENSEX)的短期和长期影响。我们还加入了虚拟因子,以了解 Covid 19 危机对上证指数(SENSEX)价格的影响。我们发现,在研究期间(包括 2020 年 3 月至 2021 年 6 月的 Covid 19 危机),原油价格跟随价值、黄金价格滞后价值以及以美元为基准的卢比对上证指数(SENSEX)的定价有重大影响。
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