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import the data into eviews (you may want to delete the series for the commodity you will not be using to avoid confusion).

INSTRUCTIONS TO CANDIDATES
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coursework data the data for the coursework can be found on moodle in the file named ‘coursework 2 data.xlsx’. this dataset contains weekly commodity price data for the spot and futures prices for crude oil and corn.

if your student registration number ends with a zero or an even number, you will use the data on oil spot and futures prices, whereas if your registration number ends with an odd number you will use the data on corn spot and futures prices.

use your assigned data to answer the questions below. your answers should all be contained in a single word or pdf file, which should include all required figures and tables exported from eviews. you do not need to upload your eviews workfile as a separate file. ensure in all cases that your answers are clearly written and, where relevant, any conclusions are supported by empirical evidence from your analysis.

question 1 import the data into eviews (you may want to delete the series for the commodity you will not be using to avoid confusion). generate new series containing the natural logarithms of the spot price and futures price for your allocated commodity. below we will denote the log price series for spot and futures prices by s_t and f_t respectively. plot the two log price series s_t and f_t on the same graph and briefly describe their properties. using augmented dickey-fuller tests, determine the order of integration of the two log price series. for each test you perform, clearly state the form of the adf test regression used, why that form was chosen, and what the test outcome implies. allow eviews to automatically select the relevant lag length for the adf test regressions using the sbic. using the engle-granger 2-step testing method, assess whether s_t and f_t are cointegrated. what does the test outcome imply about the validity or invalidity of a regression of s_t on f_t? discuss the economic rationale behind the result in part (d), explaining terms like unbiasedness and market efficiency. also briefly comment on how this result compares with the relevant economic literature. some useful references (which can be downloaded electronically from the library) include: chow, y-f. (2001), “arbitrage, risk premium, and cointegration tests of the efficiency of futures markets”, journal of business finance and accounting, 28: 693- 713. kellard, n., newbold, p., rayner, a. and ennew, c. (1999), “the relative efficiency of commodity futures markets”, journal of futures markets, 19: 413-432. assuming that the series, are cointegrated, formulate and estimate an ecm (error correction model) of the form: δs_t=β_0+β_1 δf_t+β_2 (u ̂_(t-1) )+v_t where u ̂_t is the residual from the possible cointegrating regression s_t=α_0+α_1 f_t+u_t. comment on the estimation results you obtain, in particular the interpretation of the estimated coefficient values and their significance. question 2 create a log return series from the spot price (not futures price) series for your allocated commodity. below we will denote the log return series by r_t. plot the return series r_t, and based on this plot, briefly describe its properties. estimate an ar(1) model for the return series. test the residuals of the model for arch effects, including 5 lags when you perform the arch test. what does the outcome of the test imply? estimate both an arch(1) and a garch(1,1) volatility model for the returns, using an ar(1) mean/return equation in both cases. briefly interpret the signs and statistical significance of the estimated coefficients. using arch tests (again, with 5 lags included), do either of the two volatility models estimated in part (c) appear to be adequate for modelling the autoregressive conditional heteroskedasticity present in the return series? finally, estimate a threshold garch model (also known as gjr garch) with 1 arch, 1 garch and 1 threshold term included (use the same mean/return equation as above). what do your results imply about the presence or absence of leverage effects in the return series? based on your answers to the previous questions and any other relevant parts of the estimation output or statistic tests, which of the three volatility models you have estimated seems most appropriate for modelling this return series? clearly justify your answer. question 3 outline the potential advantages of exploiting the structure present in panel datasets and summarise the standard methods available for econometric analysis of panel data.

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