ST2020代做、STATISTICS代做、R程序设计调试、R代写代写

日期：2020-05-27 11:00

LSE ST2020/ST436 Page 1 of 4

Summer Assessment 2020

Assessment paper and instructions to

candidates: ST436 – FINANCIAL STATISTICS

Suitable for all candidates

Instructions to candidates

This paper contains 3 questions.

Answer 3 questions. Question 1 is worth 34%. Questions 2 and 3 are worth 33%

each.

Answers should be justified by showing work.

Time Allowed: until 12pm on 27th May

Upper word limit: 4000-5000 (this is an approximate limit and there are no

penalties for exceeding it)

You may use: any sources that are available to you; please reference all

sources in a bibliography.

? Specify the question numbers that you answered in the boxes provided at the

end of the paper.

? You will need to include a bibliography or a list of references at the end of

your submission. Whenever you use the words or thoughts of another, you

need to reference it in (Name, Year) format in the text of your answer, and the

corresponding full reference needs to appear in the bibliography at the end.

When inserting a section of text (of any size) from someone else's work in to

your own, you must use quotation marks and a reference to the source in

(Name, Year: Page) format to make clear that you are citing verbatim. Failure

to do so may result in allegations of plagiarism.

? This online assessment has an approximate upper word limit of 1500 words

per question (so that is 4000-5000 words overall). This is in addition to any

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tables, figures, bibliography, computer code. There is no penalty for

exceeding this upper limit; however, please fit your entire assessment

(including everything) on 25 pages A4 maximum. Anything from page 26

onwards will not be read or assessed. If you use MS Word, please use font

Roboto size 11. If you use Latex, please use double spacing and a font of size

11. Please use margins of about one inch (2.54cm) on all four sides of the

page.

? Any code included in your report must be in R, and must be executable on a

clean install of R. Please keep your code listings to an absolute minimum as

they take up precious writing space, and are usually very hard to read. Any

code submitted without thorough and detailed comments will not be read or

assessed.

? We expect you to spend approximately up to a day per question to prepare

and revise, and another day per question to write up. Organise your time

well, and avoid working all night. Sleep and a social life are good for the

quality of your thinking.

Assessment questions

1. Read the paper by Catalin Starica entitled “Is GARCH(1,1) as good a model as the

Nobel Prize accolades would imply?” The paper is available on the Moodle page for

ST436, or please download it from

https://pdfs.semanticscholar.org/d5f7/b07d931274e7c9fbae10c647021696971731.pd

f. If neither of these methods of obtaining the paper work, please email

p.fryzlewicz@lse.ac.uk for a copy. Having read the paper, please perform the

following tasks.

(a) In your own words, say what the paper is about. What is its main message? What

is the paper trying to argue?

(b) Describe in detail the two methods for volatility forecasting presented in the

paper: (i) based on the GARCH(1,1) model and (ii) based on the time-varying

unconditional variance approach. Your description must be detailed enough for

someone who has completed ST436 but has no prior knowledge of Starica’s article

to be able to re-code both predictors in a computer programming language based on

your description. However, please answer this part in your own words and using

mathematical notation only; do not include any code.

(c) In your experience, which approach is better in what circumstances? To answer

this question, implement both approaches in R. Design and carry out a numerical

study to compare the empirical performance of both approaches in forecasting

volatility. In executing this task, please bear in mind the following points.

- You may wish to base your argument on a diverse selection of financial time series

(e.g. foreign exchange, individual share prices, stock indices from various regions of

the world). For each dataset you use, please say precisely how you have obtained it;

your reference must be precise enough for your reader to be able to obtain exactly

the same dataset. For this project, it is enough to focus on daily data.

- As of April 2020, many financial markets are experiencing huge fluctuations; it

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would be of great interest how the two approaches compare in particular during this

extraordinary period. You may want to include the period January-April 2020 in your

test sets.

- Please remember to base your findings on a variety of forecasting horizons; not

only “one day ahead”. What error measure are you going to use? How are you going

to measure the “true” volatility to be forecast?

- Please include your R code in the appendix; it must be commented and executable

on a clean install of R. With the help of the code, your reader must be able to

reproduce exactly any tables and/or figures included in your answer to this question.

- Important: your answer to this question must be in prose that is easy to understand

for your peers. (You can imagine, for example, that you are writing a blog post for a

specialised “financial statistics” blog started recently by your former classmates from

your undergraduate university.)

2. In the recent weeks, markets in several western countries experienced very high

levels of volatility. Several popular press articles commented that the recent market

swings were “the biggest since . . .”, with different articles making different versions

of this argument: “the biggest since 1929”, “the biggest since 1987”, or “the biggest

since 2008”, amongst others.

Use your knowledge and skills gained in ST436 to develop and convey your own

understanding of how the magnitude of the recent market movements compares to

the biggest moves seen in the 20th and 21st centuries, listed above. Have the recent

market movements really been “the biggest since. . .”? If so, since when? In your

analysis, you may want to pay attention to the points below.

(a) What markets are you going to use to draw your argument on? It is a good idea to

look at a diverse selection of markets from different continents, including Asia,

Europe and the Americas.

(b) How are you going to measure the magnitude of market movements? Examine

the lecture notes to look for ideas. In particular, you may want to review the content

of the following chapters: “Exploratory analysis of financial data”, “ARCH-type models

for low-frequency asset returns”, “Value at Risk”.

As in question 1,

- For each dataset you use, please say precisely how you have obtained it; your

reference must be precise enough for your reader to be able to obtain exactly the

same dataset. For this project, it is enough to focus on daily data.

- Please include your R code in the appendix; it must be commented and executable

on a clean install of R. With the help of the code, your reader must be able to

reproduce exactly any tables and/or figures included in your answer to this question.

- Important: your answer to this question must be in prose that is easy to understand

for your peers. (You can imagine, for example, that you are writing a blog post for a

specialised “financial statistics” blog started recently by your former classmates from

your undergraduate university.)

LSE ST2020/ST436 Page 4 of 4

3. In this project, you will re-visit the case study of Chapter 4 of the lecture notes.

Instead of basing it on linear regression as was done in the lectures, you will base it

on two other classification and prediction technologies discussed in the course:

nearest neighbours, and CART. Here is a possible workflow to guide you.

(a) Code, in R, your own implementation of nearest neighbours in a form that will

make it convenient for you to use it in the case study. This should be done from first

principles, i.e. you should not be using any specialised R packages (see item (b)

below).

(b) Locate any existing R packages that implement nearest neighbours classification.

Write alternative code for nearest-neighbour classification that uses one of these

packages.

(c) Code, in R, your own implementation of CART in a form that will make it

convenient for you to use it in the case study. This should be done from first

principles, i.e. you should not be using any specialised R packages (see item (d)

below).

(b) Locate any existing R packages that implement CART. Write alternative code for

CART that uses one of these packages.

(e) Re-do the case study, i.e. re-do Exercises 7 from the lecture course, but replacing

the linear regression method of prediction with your code from parts (a), (b), (c) and

(d).

(f) In addition to the questions contained in Exercises 7, answer one additional

question: if your Sharpe ratios obtained on the test set are positive, are they

“significantly” positive? For a hint on how to answer this question, please see the

very final part of Chapter 6 of the lecture notes. If your Sharpe ratios are not positive,

are you able to idenfity what causes this performance?

As in the two previous questions,

- Please include your R code in the appendix; it must be commented and executable

on a clean install of R. With the help of the code, your reader must be able to

reproduce exactly any tables and/or figures included in your answer to this question.

- It would be a good idea to include the period January-April 2020 in your test set.

- Important: your answer to this question must be in prose that is easy to understand

for your peers. (You can imagine, for example, that you are writing a blog post for a

specialised “financial statistics” blog started recently by your former classmates from

your undergraduate university.)