Questions and Answers of Econometric Analysis

Construct a workfile, import data and accomplish simple tasks in EViews AppendixLO1
Calculate asset price returns AppendixLO1
Describe the steps involved in building an econometric model AppendixLO1
Distinguish between different types of data AppendixLO1
Using EViews, estimate a multivariate GARCH model for the spot and futures returns series in ‘sandphedge.wf1’. Note that these series are somewhat short for multivariate GARCH model estimation.
(a) What is a news impact curve? Using a spreadsheet or otherwise, construct the news impact curve for the following estimated EGARCH and GARCH models, setting the lagged conditional variance to the
Suppose that a researcher is interested in modelling the correlation between the returns of the NYSE and LSE markets.(a) Write down a simple diagonal VECH model for this problem. Discuss the values
(a) Distinguish between the terms ‘conditional variance’ and‘unconditional variance’. Which of the two is more likely to be relevant for producing:i. 1-step-ahead volatility forecasts ii.
(a) Discuss briefly the principles behind maximum likelihood.(b) Describe briefly the three hypothesis testing procedures that are available under maximum likelihood estimation. Which is likely to be
(a) What stylised features of financial data cannot be explained using linear time series models?(b) Which of these features could be modelled using a GARCH(1,1)process?(c) Why, in recent empirical
Estimate univariate and multivariate GARCH models in EViews AppendixLO1
Construct multivariate conditional volatility models and compare between alternative specifications AppendixLO1
Discuss the three hypothesis testing procedures available under maximum likelihood estimation AppendixLO1
Contrast various models from the GARCH family AppendixLO1
Produce forecasts from GARCH models AppendixLO1
Test for ‘ARCH-effects’ in time series data AppendixLO1
Explain how conditional volatility models are estimated AppendixLO1
Discuss the features of data that motivate the use of GARCH models AppendixLO1
Define carefully the following terms● Simultaneous equations system● Exogenous variables● Endogenous variables● Structural form model● Reduced form model AppendixLO1
Consider the following vector autoregressive model yt = β0 +k i=1βi yt−i + ut (6.99)where yt is a p × 1 vector of variables determined by k lags of all p variables in the system, ut is a p×1
Explain, using an example if you consider it appropriate, what you understand by the equivalent terms ‘recursive equations’ and ‘triangular system’. Can a triangular system be validly
Consider the following system of two equations y1t = α0 + α1 y2t + α2X1t + α3X2t + u1t (6.97)y2t = β0 + β1 y1t + β2X1t + u2t (6.98)(a) Explain, with reference to these equations, the
Consider the following simultaneous equations system y1t = α0 + α1 y2t + α2 y3t + α3X1t + α4X2t + u1t (6.94)y2t = β0 + β1 y3t + β2X1t + β3X3t + u2t (6.95)y3t = γ0 + γ1 y1t + γ2X2t +
Construct simultaneous equations models and VARs in EViews AppendixLO1
Conduct Granger causality tests AppendixLO1
Estimate optimal lag lengths, impulse responses and variance decompositions AppendixLO1
Determine whether an equation from a system is identified AppendixLO1
Explain the relative advantages and disadvantages of VAR modelling AppendixLO1
Describe several methods for estimating simultaneous equations models AppendixLO1
Derive the reduced form equations from a structural model AppendixLO1
Discuss the cause, consequence and solution to simultaneous equations bias AppendixLO1
Compare and contrast single equation and systems-based approaches to building models AppendixLO1
Select two of the stock series from the ‘CAPM.XLS’ Excel file, construct a set of continuously compounded returns, and then perform a time-series analysis of these returns. The analysis should
(a) Briefly explain any difference you perceive between the characteristics of macroeconomic and financial data. Which of these features suggest the use of different econometric tools for each class
(a) Explain what stylised shapes would be expected for the autocorrelation and partial autocorrelation functions for the following stochastic processes:● white noise● an AR(2)● an MA(1)● an
You have estimated the following ARMA(1,1) model for some time series data yt = 0.036 + 0.69yt−1 + 0.42ut−1 + ut Suppose that you have data for time to t−1, i.e. you know that yt−1 = 3.4, and
(a) You obtain the following sample autocorrelations and partial autocorrelations for a sample of 100 observations from actual data:Lag 1 2 3 4 5 6 7 8 acf 0.420 0.104 0.032 −0.206 −0.138 0.042
‘Given that the objective of any econometric modelling exercise is to find the model that most closely ‘fits’ the data, then adding more lags to an ARMA model will almost invariably lead to a
How could you determine whether the order you suggested for question 6 was in fact appropriate?AppendixLO1
A researcher is trying to determine the appropriate order of an ARMA model to describe some actual data, with 200 observations available.She has the following figures for the log of the estimated
You obtain the following estimates for an AR(2) model of some returns data yt = 0.803yt−1 + 0.682yt−2 + ut where ut is a white noise error process. By examining the characteristic equation, check
(a) Describe the steps that Box and Jenkins (1976) suggested should be involved in constructing an ARMA model.(b) What particular aspect of this methodology has been the subject of criticism and
Consider the following three models that a researcher suggests might be a reasonable model of stock market prices yt = yt−1 + ut (5.190)yt = 0.5yt−1 + ut (5.191)yt = 0.8ut−1 + ut (5.192)(a)
Why might ARMA models be considered particularly useful for financial time series? Explain, without using any equations or mathematical notation, the difference between AR, MA and ARMA
What are the differences between autoregressive and moving average models?AppendixLO1
Estimate time series models and produce forecasts from them in EViews AppendixLO1
Evaluate the accuracy of predictions using various metrics AppendixLO1
Produce forecasts for ARMA and exponential smoothing models AppendixLO1
Identify the appropriate time series model for a given data series AppendixLO1
Explain the defining characteristics of various types of stochastic processes AppendixLO1
Find a further example of where panel regression models have been used in the academic finance literature and do the following:● Explain why the panel approach was used.● Was a fixed effects or
(a) Explain how fixed effects models are equivalent to an ordinary least squares regression with dummy variables.(b) How does the random effects model capture cross-sectional heterogeneity in the
(a) What are the advantages of constructing a panel of data, if one is available, rather than using pooled data?(b) What is meant by the term ‘seemingly unrelated regression’? Give examples from
Construct and estimate panel models in EViews AppendixLO1
Contrast the fixed effect and random effect approaches to panel model specification, determining which is the more appropriate in particular cases AppendixLO1
Explain the intuition behind seemingly unrelated regressions and propose examples of where they may be usefully employed AppendixLO1
Describe the key features of panel data and outline the advantages and disadvantages of working with panels rather than other structures AppendixLO1
Determine a sensible structure for the dissertation AppendixLO1
Find appropriate sources of literature and data AppendixLO1
Choose a suitable topic for an empirical research project in finance AppendixLO1
A barrier option is a path-dependent option whose payoff depends on whether the underlying asset price traverses a barrier. A knock-out call is a call option that ceases to exist when the underlying
(a) Consider the following AR(1) model yt = φyt−1 + ut (12.31)Design a simulation experiment (with code for EViews) to determine the effect of increasing the value of φ from 0 to 1 on the
A researcher tells you that she thinks the properties of the Ljung–Box test (i.e. the size and power) will be adversely affected by ARCH in the data. Design a simulations experiment to test this
(a) Present two examples in finance and two in econometrics (ideally other than those listed in this chapter!) of situations where a simulation approach would be desirable. Explain in each case why
Implement a simulation analysis in EViews AppendixLO1
Describe the various techniques available for reducing Monte Carlo sampling variability AppendixLO1
Explain the difference between pure simulation and bootstrapping AppendixLO1
Design simulation frameworks to solve a variety of problems in finance AppendixLO1
Re-open the ‘fail xls’ spreadsheet for modelling the probability of MSc failure and do the following:(a) Take the country code series and construct separate dummy variables for each country.
(a) Explain the difference between a censored variable and a truncated variable as the terms are used in econometrics.(b) Give examples from finance (other than those already described in this book)
(a) Describe the intuition behind the maximum likelihood estimation technique used for limited dependent variable models.(b) Why do we need to exercise caution when interpreting the coefficients of a
Compare and contrast the probit and logit specifications for binary choice variables.AppendixLO1
Explain why the linear probability model is inadequate as a specification for limited dependent variable estimation.AppendixLO1
Estimate limited dependent variable models using maximum likelihood in EViews AppendixLO1
Deal appropriately with censored and truncated dependent variables AppendixLO1
Distinguish between the binomial and multinomial cases AppendixLO1
Interpret and evaluate logit and probit models AppendixLO1
Compare between different types of limited dependent variables and select the appropriate model AppendixLO1
(a) Re-open the exchange rate returns series and test them for day-of-the-week effects.(b) Re-open the house price changes series and determine whether there is any evidence of seasonality.AppendixLO1
A researcher suggests that the volatility dynamics of a set of daily equity returns are different:● on Mondays relative to other days of the week● if the previous day’s return volatility was
(a) What is a switching model? Describe briefly and distinguish between threshold autoregressive models and Markov switching models. How would you decide which of the two model classes is more
A researcher is attempting to form an econometric model to explain daily movements of stock returns. A colleague suggests that she might want to see whether her data are influenced by daily
Describe the intuition behind the estimation of regime switching models AppendixLO1
Compare and contrast Markov switching and threshold autoregressive models AppendixLO1
Specify and explain the logic behind Markov switching models AppendixLO1
Motivate the use of regime switching models in financial econometrics AppendixLO1
Use intercept and slope dummy variables to allow for seasonal behaviour in time series AppendixLO1
In EViews, open the ‘currencies.wf1’ file that will be discussed in detail in the following chapter. Determine whether the exchange rate series (in their raw levels forms) are non-stationary. If
Compare and contrast the Engle–Granger and Johansen methodologies for testing for cointegration and modelling cointegrated systems. Which, in your view, represents the superior approach and
(a) Suppose that a researcher has a set of three variables, yt (t = 1, . . . , T ), i.e. yt denotes a p-variate, or p × 1 vector, that she wishes to test for the existence of cointegrating
(a) Briefly outline Johansen’s methodology for testing for cointegration between a set of variables in the context of a VAR.(b) A researcher uses the Johansen procedure and obtains the following
(a) Consider a series of values for the spot and futures prices of a given commodity. In the context of these series, explain the concept of cointegration. Discuss how a researcher might test for
Using the same regression as for question 2, but on a different set of data, the researcher now obtains the estimate ˆψ = −0.52 with standard error = 0.16.(a) Perform the test.(b) What is the
A researcher wants to test the order of integration of some time series data. He decides to use the DF test. He estimates a regression of the formyt = μ + ψyt−1 + ut and obtains the estimate
(a) What kinds of variables are likely to be non-stationary? How can such variables be made stationary?(b) Why is it in general important to test for non-stationarity in time series data before
Construct models for long-run relationships between variables in EViews AppendixLO1
Describe how to test hypotheses in the Johansen framework AppendixLO1
Explain the intuition behind Johansen’s test for cointegration AppendixLO1
Estimate error correction and vector error correction models AppendixLO1
Examine whether systems of variables are cointegrated AppendixLO1

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