# SMM622 Advanced Financial Modelling and Forecasting

SMM622 Advanced Financial Modelling and ForecastingIndividual Coursework
From either https://uk.finance.yahoo.com/ or Datastream or
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html or any alternative data provider
a) fifty price series of financial asset (say ππππππ with ππ = 1, β¦ . , 50 and π‘π‘ = 1, β¦ . , ππ)
b) one of the major stock indices (say ππππππ with M being either SP500 or FTSE100 or DW or Nasdaq or
β¦.) at daily frequency for the last ten years from 1 January 2010 to the 31 March 2021.
1. Aggregate both ππππππ and ππππππ at annual frequency and take the log-returns.
a. Estimate the model ππππππ = ππππ + π΅π΅π΅π΅π΅π΅π΅π΅ β ππππππ + πππ‘π‘ + πππ½π½ + π£π£ππππ using pooled, least-squares dummy
variable (LSDV) and within-groups (WG) estimators and evaluate. Explain the difference
between random and fixed effects and describe the way in which you can test whether the
individual effects (ππππ) are random or fixed
b. Estimate an AR(1) dynamic model ππππππ = ππππ + π΄π΄π΄π΄π΄π΄π΄π΄π΄π΄ β ππππππβ1 + πππ‘π‘ + πππ½π½ + π£π£ππππ considering the
last only the first 5 years (2010-2014) and implement the Anderson and Hsiao (1981, 1982) and
the Arellano and Bond (1991) estimator where the number of valid instruments to exploit are
(t-2), (t-3).
c. Contrast the results in b. with those that you obtain considering the last 5 years (2015-2019)
2. From the data set above, consider only five the log-returns series at daily frequency:
a. Assuming that the conditional mean follows an ARMA(0,0), estimate the conditional variance
of each log returns series by identifying the appropriate univariate (G)ARCH specification by
considering alternative GARCH models and alternative distributions of the innovations as
available in [email protected]
b. Estimate an ARMA (1,1)-GARCH representation and compare it with the case of a simple
ARMA(0,0)-GARCH model identified above.
c. Estimate and comment the results from the implementation of three MGARCH models. In
particular, focus on the time pattern of the conditional correlations you get from the DCC model
highlighting links with stylized facts you know.
3. Evaluate the forecasting capability of three alternative volatility models following guidelines provided
in Brownlees et al (2011) and Hansen and Lunde (2015).
4. Following the Shu and Zhang (2013) paper, where authors used daily S&P 500 index option prices over
the period between January 1995 and December 1999, report and comment a novel empirical application
evaluating the relationship between alternative measures of volatility. Alternatively, you may replicate
a similar empirical application as reported in Zhang, Ma and Liao (2020).
5. Elaborate an essay on systemic risk measures on the basis of your reading of the Benoit et al (2017)
paper and references therein. In particular highlight pros and cons of the MES of Acharya et al (2017),
of the SRIK of Brownlees & Engle (2016), and of the CoVaR of Adrian & Brunnermeier (2016) with
respect the alternative source-specific and global approaches in Benoit et al (2017).
The deadline for the coursework to be submitted via Moodle is Friday, 9/07/ 2021 by 4pm.
Giovanni Urga, Tuesday, 06 April 2021

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