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程序代写案例-MSIN0209

时间：2021-01-29

MSIN0209: Finance Research Project – Research proposal example

Volatility in the FTSE 100 in bull markets vs bear markets: Applications for investors

1. Introduction

The state of the market, a bull or a bear market, has an important role in financial

decision-making. The terms bull and bear mean extended intervals of time over which prices

have broadly increased or broadly decreased respectively (Chauvet and Potter, 2000).

To identify and forecast bull and bear markets, semi-parametric and parametric methods

are often employed. Pagan and Sossounov (2003) developed a semi-parametric method that

uses a range of rules to determine bear and bull markets in equity prices. Hamilton (1988,

1989, 1990) developed a parametric method, the Markov regime-switching model, that is

used to forecast prices based on switching regimes that take the volatility of returns into

account. An advantage of the semi-parametric methods is that they use transparent rules to

determine the peaks and throughs. Nevertheless, the rules require some subjective settings,

while also predictor variables that are relevant for forecasting the market prices are not used.

The regime switching models specify one data-generating process, allowing a complete

density of returns at each point in time. However, the method is vulnerable to

misspecification, because of the need to specify the data-generating process.

Even though both these methods have been used in various market (such as the S&P500),

no study has assessed the efficacy of these two methods in the FTSE100 index. Motivated by

the unique features of the FTSE100 (e.g. time-varying volatility), the aim of this study is two-

fold: First, to compare between semi-parametric and parametric forecasting methods for the

UK market proxied by high-frequency FTSE100 data over the period 1980 – 2019. To enhance

the robustness and appropriateness of the models several macroeconomic and financial

variables are used to predict switches between bull and bear markets. Second, the study

attempts to identify which model provides the best measures of conditional time-varying

variance of the FTSE100 in bull and bear markets.

2. Theoretical Background and Hypotheses development

The market state is monitored because of its relation to (a) the credit supply (Rigobon and

Sack, 2003, Bohl et al., 2007), (b) time-variation in risk premia (Gordon and St-Amour, 2000,

Ang et al. 2006) and (c) forecast macroeconomic variables and predict business cycle

(Marcellino, 2006). Therefore, I hypothesize that:

H1: Parametric models are more suitable to forecast bear and bull market compared to semi-

parametric models in high frequency and extremely volatile data, such as the FTSE100.

H2: GARCH (p,q) fits better the FTSE100 compared to EGARCH(p,q) and TGARCH(p,q) and

H3: EGARCH (p,q) fits better the FTSE100 compared to TGARCH(p,q).

3. Possible data sources

I will use the FTSE 100 data of the period 1980 to 2019, found on Yahoo Finance.

4. Empirical Analysis

First, I will identify which periods from 1980 – 2019 were bullish or bearish. As a semi-

parametric method, this study uses the Pagan and Sossounov (2003) method to determine the

past states of the market. Hamilton’s (1990) method is used as a parametric method.

Additionally, due to the extremely volatile nature of the FTSE 100 I will attempt to identify the

best model to measure conditional variance of the FTSE100. The approach is suitable

when a series exhibits volatility clustering and serial correlation, suggesting that past variances

might be predictive of the current variance. Thus, I will use the GARCH(p,q), EGARCH(p,q) and

the TGARCH(p,q) to identify the best model to capture volatility during bear and bull periods.

4. Applications

Investors and economists need robust information about the future state of the market. The

results of this project could support investment decisions during bear and bull periods.

5. Reference list

Ang, A. and Timmermann, A. (2012). Regime Changes and Financial Markets, Annual Review of

Financial Economics, 4, pp. 313 – 337.

Ang, A., Chen, J., and Xing, Y. (2006). Downside risk. Review of Financial Studies, 19(4), pp.

1191 – 1239.

Bohl, M.T., Siklos, P.L. and Werner, T. (2007). Do central banks react to the stock market? The

case of the Bundesbank, Journal of Banking and Finance, 31(3), 719 – 733.

Chauvet, M. and Potter, S. (2000). Coincident and leading indicators of the stock market,

Journal of Empirical Finance, 7(1), pp. 87 – 111.

Gordon, S. and St-Amour, P. (2000). A preference regime model of bull and bear markets,

American Economic Review, 90(4), pp. 1019 – 1033.

Hamilton, J.D. (1988). Rational-Expectations Econometric Analysis of Changes in Regime: An

Investigation of the Term Structure of Interest Rates, Journal of Economic Dynamics and

Control, 12(2-3), pp. 385 – 423.

Hamilton, J.D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series

and the Business Cycle, Econometrics, 57(2), pp. 357 – 384.

Hamilton, J.D. (1990). Analysis of Time Series Subject to Changes in Regime, Journal of

Econometrics, 45(1-2), pp. 39 – 70.

Marcellino, M. (2006). Leading indicators: What have we learned? In Elliot, G., Granger, C.W.

and Timmermann, A., editors, Handbook of Economic Forecasting, pp. 879 – 960,

Elsevier, Amsterdam, Netherlands.

Pagan, A.R. and Sossounov, K.A. (2003). A simple framework for analysing bull and bear

markets, Journal of Applied Econometrics, 18(1), pp. 23 – 46.

Rigobon, R. and Sack, B. (2003). Measuring the reaction of monetary policy to the stock

market, Quarterly Journal of Economics, 118(2), pp. 639 – 669.

Feedback

Dear Student,

I like your dissertation topic and think that it is very relevant. Your introduction, data sources,

empirical analysis and application sections are good. The time frame you chose is long enough to

cover bull and bear markets and I see that you have done proper reading to identify models.

Pay attention to your similarity mark, 34% here, mostly related to references but this is very high and

see a few bits copied from a published paper. Make sure you use your own words to explain things in

your dissertation.

In addition, notice that in Section 2, your justification (the paragraph I underlined) explains why your

hypotheses are important, but not why you think that they are correct. In the theoretical background

and hypotheses development section, students are required to explain how the existing literature led

them to formulate their hypotheses the way they did (e.g. why you think that EGARCH (p,q) fits

better the FTSE100 compared to TGARCH(p,q)), and not the applications of the hypotheses.

Good work so far, keep it up! Please contact a relevant consultant before you continue your work.

Good luck with your dissertation!

LPC and DS

学霸联盟

Volatility in the FTSE 100 in bull markets vs bear markets: Applications for investors

1. Introduction

The state of the market, a bull or a bear market, has an important role in financial

decision-making. The terms bull and bear mean extended intervals of time over which prices

have broadly increased or broadly decreased respectively (Chauvet and Potter, 2000).

To identify and forecast bull and bear markets, semi-parametric and parametric methods

are often employed. Pagan and Sossounov (2003) developed a semi-parametric method that

uses a range of rules to determine bear and bull markets in equity prices. Hamilton (1988,

1989, 1990) developed a parametric method, the Markov regime-switching model, that is

used to forecast prices based on switching regimes that take the volatility of returns into

account. An advantage of the semi-parametric methods is that they use transparent rules to

determine the peaks and throughs. Nevertheless, the rules require some subjective settings,

while also predictor variables that are relevant for forecasting the market prices are not used.

The regime switching models specify one data-generating process, allowing a complete

density of returns at each point in time. However, the method is vulnerable to

misspecification, because of the need to specify the data-generating process.

Even though both these methods have been used in various market (such as the S&P500),

no study has assessed the efficacy of these two methods in the FTSE100 index. Motivated by

the unique features of the FTSE100 (e.g. time-varying volatility), the aim of this study is two-

fold: First, to compare between semi-parametric and parametric forecasting methods for the

UK market proxied by high-frequency FTSE100 data over the period 1980 – 2019. To enhance

the robustness and appropriateness of the models several macroeconomic and financial

variables are used to predict switches between bull and bear markets. Second, the study

attempts to identify which model provides the best measures of conditional time-varying

variance of the FTSE100 in bull and bear markets.

2. Theoretical Background and Hypotheses development

The market state is monitored because of its relation to (a) the credit supply (Rigobon and

Sack, 2003, Bohl et al., 2007), (b) time-variation in risk premia (Gordon and St-Amour, 2000,

Ang et al. 2006) and (c) forecast macroeconomic variables and predict business cycle

(Marcellino, 2006). Therefore, I hypothesize that:

H1: Parametric models are more suitable to forecast bear and bull market compared to semi-

parametric models in high frequency and extremely volatile data, such as the FTSE100.

H2: GARCH (p,q) fits better the FTSE100 compared to EGARCH(p,q) and TGARCH(p,q) and

H3: EGARCH (p,q) fits better the FTSE100 compared to TGARCH(p,q).

3. Possible data sources

I will use the FTSE 100 data of the period 1980 to 2019, found on Yahoo Finance.

4. Empirical Analysis

First, I will identify which periods from 1980 – 2019 were bullish or bearish. As a semi-

parametric method, this study uses the Pagan and Sossounov (2003) method to determine the

past states of the market. Hamilton’s (1990) method is used as a parametric method.

Additionally, due to the extremely volatile nature of the FTSE 100 I will attempt to identify the

best model to measure conditional variance of the FTSE100. The approach is suitable

when a series exhibits volatility clustering and serial correlation, suggesting that past variances

might be predictive of the current variance. Thus, I will use the GARCH(p,q), EGARCH(p,q) and

the TGARCH(p,q) to identify the best model to capture volatility during bear and bull periods.

4. Applications

Investors and economists need robust information about the future state of the market. The

results of this project could support investment decisions during bear and bull periods.

5. Reference list

Ang, A. and Timmermann, A. (2012). Regime Changes and Financial Markets, Annual Review of

Financial Economics, 4, pp. 313 – 337.

Ang, A., Chen, J., and Xing, Y. (2006). Downside risk. Review of Financial Studies, 19(4), pp.

1191 – 1239.

Bohl, M.T., Siklos, P.L. and Werner, T. (2007). Do central banks react to the stock market? The

case of the Bundesbank, Journal of Banking and Finance, 31(3), 719 – 733.

Chauvet, M. and Potter, S. (2000). Coincident and leading indicators of the stock market,

Journal of Empirical Finance, 7(1), pp. 87 – 111.

Gordon, S. and St-Amour, P. (2000). A preference regime model of bull and bear markets,

American Economic Review, 90(4), pp. 1019 – 1033.

Hamilton, J.D. (1988). Rational-Expectations Econometric Analysis of Changes in Regime: An

Investigation of the Term Structure of Interest Rates, Journal of Economic Dynamics and

Control, 12(2-3), pp. 385 – 423.

Hamilton, J.D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series

and the Business Cycle, Econometrics, 57(2), pp. 357 – 384.

Hamilton, J.D. (1990). Analysis of Time Series Subject to Changes in Regime, Journal of

Econometrics, 45(1-2), pp. 39 – 70.

Marcellino, M. (2006). Leading indicators: What have we learned? In Elliot, G., Granger, C.W.

and Timmermann, A., editors, Handbook of Economic Forecasting, pp. 879 – 960,

Elsevier, Amsterdam, Netherlands.

Pagan, A.R. and Sossounov, K.A. (2003). A simple framework for analysing bull and bear

markets, Journal of Applied Econometrics, 18(1), pp. 23 – 46.

Rigobon, R. and Sack, B. (2003). Measuring the reaction of monetary policy to the stock

market, Quarterly Journal of Economics, 118(2), pp. 639 – 669.

Feedback

Dear Student,

I like your dissertation topic and think that it is very relevant. Your introduction, data sources,

empirical analysis and application sections are good. The time frame you chose is long enough to

cover bull and bear markets and I see that you have done proper reading to identify models.

Pay attention to your similarity mark, 34% here, mostly related to references but this is very high and

see a few bits copied from a published paper. Make sure you use your own words to explain things in

your dissertation.

In addition, notice that in Section 2, your justification (the paragraph I underlined) explains why your

hypotheses are important, but not why you think that they are correct. In the theoretical background

and hypotheses development section, students are required to explain how the existing literature led

them to formulate their hypotheses the way they did (e.g. why you think that EGARCH (p,q) fits

better the FTSE100 compared to TGARCH(p,q)), and not the applications of the hypotheses.

Good work so far, keep it up! Please contact a relevant consultant before you continue your work.

Good luck with your dissertation!

LPC and DS

学霸联盟