代写ACFI3308 Financial Econometrics Assessment 2代做留学生Matlab编程

Faculty of Business and Law

ACFI3308 Assessment 2

(Capstone Project)

Module Title	Financial Econometrics	Assignment Number	2
Module Code	ACFI3308	Assignment Title	Capstone Project

Assessment Information – What you need to do

BACKGROUND

Following on from the equity research you carried out in Assessment 1 (Report), you are asked by your division to carry out a range of time series analyses to augment your earlier report. Specifically, the firm would like to know if alternative time series models (ARMA and ES) are better at pricing and predicting stock return. In addition, you are also expected to estimate volatility using the ARCH/GARCH family of models.

The terms of reference are detailed below.

PART ONE: STOCK RETURN PREDICTABILITY

UNIVARIATE TIME SERIES MODELS

1. Using the equal-weighted portfolio of the twenty (20) equities from Assessment One, update your portfolio this time to a daily return for the period 1 January 2018 to 29 February 2024.

2. Utilise the Box-Jenkins Approach to estimate an appropriate Autoregressive Moving Average (ARMA) for your portfolio's monthly return from 1 January 2018 to 31 January 2022.

3. Discuss the diagnostic results (residual analysis) of the ARMA model you estimated.

4. Produce an out-of-sample forecast for 1 February 2022 to 29 February 2024.

5. Compare your forecasts to the return series for the forecast window using the following measures:

• Mean Square Error (MSE)

• Root Mean Square Error (RMSE) and

• Mean Absolute Percentage Error (MAPE).   [20 MARKS]

EXPONENTIAL SMOOTHING & TECHNICAL ANALYSIS

(i) Estimate an appropriate exponential smoothing (ETS) model for the daily price series of one stock in your portfolio from January 2018 to January 2022. Your model should incorporate a trend term and a dampening factor if required.

(ii) Produce an out-of-sample forecast from February 2022 to February 2024 and discuss the forecasts’ accuracy measures.

(iii) Construct a moving average convergence divergence (MACD) chart for the daily price series of the stock selected in (i) above from your portfolio for the period February 2022 to February 2024. The parameters for the MACD are s = 8, l = 30 and k = 9.

(iv) Analyse the predictability of the price series’ trend and momentum from the MACD plots and highlight when the chart correctly predicts (or misses) the trend and momentum in the series.   [20 MARKS]

DISCUSSION OF RESULTS

Referring to your ARMA, ETS, and MACD, discuss the predictability of stock prices. Relate your discussion to the notion that financial markets are informationally efficient.   [10 MARKS]

[TOTAL 50 MARKS]

PART TWO: VOLATILITY MODELLING WITH GARCH

In addition to the return on the fund, it is important to understand how volatile the fund isrelative to the market.

(i) Using the daily returns (in percentages) from 1 January 2018 to 29 February 2024, estimate the GARCH (1, 1) and the GJR GARCH (1, 1) process for;

• The S&P 500 Index

• The equity fund you formed in Part 1

• Any two (2) stocks from the equity fund in Part 1.

(ii) Plot your volatility estimates, summarise the coefficients from your GARCH (1, 1) and GJR GARCH (1,1) models in an appropriate table, and comment upon the results.  [6 MARKS]

(iii) What do the results for GARCH (1, 1) suggest about the riskiness of your fund and stocks relative to the market portfolio? Discuss.  [7 MARKS]

(iv) Discuss whether your volatility estimates are symmetrical Using the GJR GARCH (1,1) results. Is this result in line with the economic theory of investor reaction to good and bad news?  [7 MARKS]

[TOTAL 20 MARKS]

PART THREE: VECTOR AUTOREGRESSIVE MODELS/COINTEGRATION

CHOOSE ONLY ONE QUESTION FROM THIS SECTION

QUESTION ONE: VECTOR AUTOREGRESSIVE MODELS(VAR)

Using the relevant quarterly series from January 2010 to December 2023:

(i) Estimate a vector autoregressive model (VAR) using the following series.

• The equity fund’s return series (from Part One)

• Real Gross Domestic Product [GDPC1]

• Industrial Output [INDPRO]

• Consumer price index [USACPIALLMINMEI]

• Federal fund effective rate [FEDFUNDS]

• Yield spreads (difference between the US 3 months T-Bill and 10-Year Treasury Constant Maturity Rate) [T10Y3M]

• SP 500 Index [^GSPC]

Note: the labels in the square bracket [ ] are the variable names in the Federal Reserve Economic Database (FRED), except for SP500, the variable in Yahoo Finance.

(ii) Report the following results using appropriate tables and charts:

• Granger causality between your fund’s return and all the variables in the model [ignoring all other causality results]

• Impulse response function from all other factors to your fund’s return series [ignoring all other impulse response function results].

(iii) Discuss only the Granger causality results and impulse response function on the fund return series. [10 MARKS]

(iv) Estimate a structural VAR (SVAR) using the following ordering and restrictions.

• Variable order Real Gross Domestic Product [GDPC1], SP 500 Index [^GSPC], The equity fund’s return series (from Part One).

• Restriction

Note: In the estimation, the ordering above Real GDP, SP 500 Index, and the fund’s return series must be maintained.

(v) Estimate the Granger Causality between the fund’s returns, Real GDP, and SP 500 Index.

(vi) Conduct an impulse response using Real GDP, SP 500, and the lag of the fund’s return series. [10 MARKS]

[TOTAL 20 MARKS]

QUESTION TWO: COINTEGRATION AND ERROR CORRECTION MODEL

Using daily series on the following stock indices between 1 January 2010 and December 2023.

(i) Pair the following five major stock indices and test for cointegration among the pairs of variables by applying the Engle-Granger (EG) approach. Discuss whether any of the markets are cointegrated.

• FTSE 100 index

• SP 500 index

• DAX 40 index

• CAC 40 index

• Nikkei 225 index [10 marks]

(ii) Estimate an error correction model for any cointegrated pair of variables and discuss your results. [10 marks]

[TOTAL 20 MARKS]

FORMAT OF THE REPORT

An R script. showing the codes used in estimating your results should be submitted for all estimations.

Link for submitting the R Script.

Link for Submitting the R-Script.

A well-structured report with clarity of discussion, logical presentation, and proficient level of depth (references).

[TOTAL 10 MARKS]

Criteria for Assessment - How you will be marked

PART ONE: STOCK RETURN PREDICTABILITY

• Univariate time series models [20 marks]

• Exponential Smoothing & Technical analysis [20 marks]

• Discussion of Results [10 marks]

[50 MARKS]

PART TWO: VOLATILITY MODELLING WITH GARCH

• Estimation and summary of GARCH (1, 1) and GJR GARCH (1, 1) [6 marks]

• Discussion of GARCH (1, 1) results [7 marks]

• Discussion of GJR GARCH (1, 1) [7 marks]

[20 MARKS]

PART THREE: VECTOR AUTOREGRESSIVE MODELS/COINTEGRATION

• Estimation and Discussion of VAR results [10 marks]

• Estimation and Discussion of SVAR results [10 marks]

[20 MARKS]

PART FOUR: COINTEGRATION AND ERROR CORRECTION MODELS

• Test of cointegration and discussion of results [10 marks]

• Error Correction model and discussion of results [10 marks]

[20 MARKS]

FORMAT OF THE REPORT

• R script. [5 marks]

• Structure of report and references [5 marks]

[10 MARKS]

There should be an emphasis on your report's logical flow and clarity. Every step should be explained to engage the reader.

Students who paste output without providing any context will get the minimum score.

ASSESSMENT AND MARKING SCHEME

The marks will be given to students who produce evidence of:

• Good understanding of the underlying theory and concepts.

• Good understanding of econometric analysis.

• Ability to link properly the first two points above.

• Critical thinking in the interpretation of the findings.

A marking rubric which provides more details on the marking scheme is also provided.

This assignment is designed to assess the following learning outcomes:

(i) Appraise the problems of non-stationarity in the data series and how these problems can be detected using unit roots and cointegration tests.

(ii) Produce forecasts or ARMA and exponential smoothing models and evaluate the usefulness, relative advantages and disadvantages of VAR, ARCH and GARCH modelling using econometrics packages.

(iii) Perform. and appraise business and economic forecasts using various econometrics techniques.