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Download the daily adjusted close price data from the Yahoo finance website for any one company (of your choice) from the table given below, for

Download the daily adjusted close price data from the Yahoo finance website for any one company (of your choice) from the table given below, for the period from January 1, 2022, to September 30, 2023. You need to mention the rationale for choosing the company as well, in brief. [5*10=50 Marks]

Granules India Ltd. Latent View Analytics Ltd.
Graphite India Ltd. Laxmi Organic Industries Ltd.
Great Eastern Shipping Co. Ltd. Lemon Tree Hotels Ltd.
Gujarat Alkalies & Chemicals Ltd. Lux Industries Ltd.
Gujarat Ambuja Exports Ltd. MMTC Ltd.
Gujarat Narmada Valley Fertilizers and Chemicals Ltd. MTAR Technologies Ltd.
Gujarat Pipavav Port Ltd. Mahanagar Gas Ltd.
Gujarat State Fertilizers & Chemicals Ltd. Manappuram Finance Ltd.
Gujarat State Petronet Ltd. Mangalore Refinery & Petrochemicals Ltd.
H.E.G. Ltd. Mastek Ltd.
HFCL Ltd. Metropolis Healthcare Ltd.
Happiest Minds Technologies Ltd. Minda Corporation Ltd.
Hindustan Copper Ltd. Motilal Oswal Financial Services Ltd.
Hitachi Energy India Ltd. Multi Commodity Exchange of India Ltd.
Home First Finance Company India Ltd. NMDC Steel Ltd.
Housing & Urban Development Corporation Ltd. Narayana Hrudayalaya Ltd.
IDBI Bank Ltd. National Aluminium Co. Ltd.
IDFC Ltd. Nazara Technologies Ltd.
IIFL Finance Ltd. Network18 Media & Investments Ltd.
IRB Infrastructure Developers Ltd. Nippon Life India Asset Management Ltd.
IRCON International Ltd. Nuvoco Vistas Corporation Ltd.
ITI Ltd. PCBL Ltd.
India Cements Ltd. PNB Housing Finance Ltd.

Compute the log returns and estimate the appropriate ARIMA model using the Box-Jenkins methodology. Please show these computations and output:

  1. Keep the last 5 observations of the returns series as the out-of-sample forecast. Fit a suitable ARIMA model on the remaining observations (in-sample) of the log returns (N-5).
  2. You are supposed to show the entire procedure (Box-Jenkins) involved in fitting the suitable ARIMA model (along with diagnostics), forecasting the model, and checking the accuracy of the model.
  3. If you observe that the return series of the chosen company exhibits characteristics of a white noise process, undertake the fitting of ARIMA (1,0,1) on the return series. This step aims to elucidate the comprehensive procedure involved in the entire ARIMA modelling process.
  4. After identifying the suitable ARIMA model, do forecasting for the last 5 observations and report the accuracy of the model (out-of-sample only).
  5. Please report all important output/results of R (Such as line chart of price and return series, correlograms, stationarity test results, Ljung Box test results, among others) as screenshots/images in the assignment.

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