Question: Overview The task you are given is to estimate the market risk for a holding of 10,000 BHP shares (BHP.ax) and 10,000 CBA shares (CBA.ax),

Overview The task you are given is to estimate
Overview The task you are given is to estimate the market risk for a holding of 10,000 BHP shares (BHP.ax) and 10,000 CBA shares (CBA.ax), held on March 1, 2023 (you are working out the risk position assuming that you own these shares before the open of trading that day). You will do this by estimating the ValueatRisk for the stock portfolio. This will require you to choose the best VaR model by backtesting several methods to determine the most reliable for the task at hand. Description You will be asked to calculate the Mg; . M VaR for the portfolio of shares at a confidence level of 99%. Note: This risk estimate applies to the next 10 trading days from March 1, 2023 until March 14, 2023 (Le. it should be a forecast of risk}. Based on what you have learnt from EFB344, you are considering several options for how to compute this risk measure, a) the normal distribution using the EWMA, b] the normal distribution using a rolling window, or C] historical simulation based on a rolling window. All methods require choosing parameters to assign weight to past data, for the EWMA and the window length for the other two. You will consider the following choices for E Normal EWMA lambda = 0.94 for both volatilities and the covariance. Normal Rolling Window Rolling window with 252 trading days. Historical Simulation Rolling window with 252 trading days. This leaves you with 3 possible models that could be used to provide the VaR measure asked for above. You must choose the most appropriate model and report the associated Maa To inform your decision of which to use, you are going to consider the recent historical performance of the three models in calculating :l gay VaR at the confidence level of 99%. You will do so by first examining the frequency of instances when the VaR was exceeded by the observed return over the past five years. You will then evaluate the appropriateness of these frequencies over time relative to the Basel traffic light levels discussed in lectures. Based on this performance, select the best model and report the required Q10, 99%) for March 1, 2023

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