26
Financial Risk Management Student Name Student ID Tutor Name Ali Taqi 201000556 Ghassan Alsoud Mohammed Haji 201100040 Fatima Mohammed 201000393 Marwa Malik 201000472

Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Embed Size (px)

Citation preview

Page 1: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Financial Risk Management

Student Name Student ID Tutor NameAli Taqi 201000556

Ghassan AlsoudMohammed Haji 201100040Fatima Mohammed 201000393Marwa Malik 201000472

Page 2: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

DJIA Consists of 30 “Blue-Chip” US stocks Best-known market indicator in the world

We have invested $1m in ETF (-2x) the daily average of DJIA

Introduction

Page 3: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472
Page 4: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Investment Strategy: - Short Selling - Inverse ETF Short Position - (-2X) the DJIA - Invest $1,000,000

Main Characteristics

Page 5: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Portfolio Chart - Using excel (using sum product

of ‘quantity’)

- Fluctuating but generally increasing

- Investor will profit at “low points”

Main Characteristics (continued)

1/2/

2013

2/1/

2013

3/3/

2013

4/2/

2013

5/2/

2013

6/1/

2013

7/1/

2013

7/31

/201

3

8/30

/201

3

9/29

/201

3

10/2

9/20

13

11/2

8/20

13

12/2

8/20

130

500000

1000000

1500000

2000000

2500000

3000000

Performance Chart

Portfolio

Page 6: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Basis Price: - It is the Actual Invested Amount 1) Capital x Weight (in decimal) to get

invested amount 2) Invested amount / Stock Price to

get the number of stocks 3) Round up the number of stocks 4) Number of Stocks (rounded up) x

Stock Price to get the Actual Invested Amount

Main Characteristics (continued)

Page 7: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Risks in US Stock market Monetary Policy by the Federal Reserve

Federal Reserve tightens the Monetary Policy and raise interest rates

Restrictive Monetary policy = weak stock performance

Expansionary Monetary Policy= strong stock Performance

The US dollar Wage stagnation

Page 8: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Market risk of inverse ETFs

Risky in long-term

Leverage issues Asset management responsibilitiesCounterparty risk Costlier than traditional ETFsLess tax-efficient

Page 9: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Confidence Level: 5%, Holding Period: 1-Day 1) Calculate Profit/Loss (Daily Returns were

calculated for the Returns VaR) 2) Put them in order (highest losses to highest

profits) 3) Multiply total observations (251) by 5%, we’ll

get 12.55 4) [(Observation 13 - Observation 12) x 0.55] +

Observation 12 5) VaR = 319.01, (9833.179 for daily return VaR) 6) Use PERCENTILE for confirmation

Historical VaR

Page 10: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Confidence Level: 5%, Holding Period: 1-Day, Z-Score (5%): 1.96

1) Find Mu (Average) of Profit/Loss (Daily Returns for daily return VaR)

2) Find Sigma (Standard Deviation) of Profit/ Loss (Daily Returns for daily return VaR)

3) Z-Score x Sigma – Mu 4) VaR = 394.7 , (11929.17 for daily return

VaR)

Linear VaR

Page 11: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Historical VaR is more accurate because: - linear VaR cannot give the correct

estimation when it comes to extreme market events

- linear VaR does not take into account the skews, return distribution, and kurtosis

Comments on VaR

Page 12: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Inflation Interest rate Unemployment

The world economy

Page 13: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

The relationship between Bonds & Interest rate

Page 14: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472
Page 15: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Investment portfolio of $100,000 is invested in US treasury securities.

30 years treasury Bond “CUSIP 912810QN1” 50% weight.

10 years treasury note “CUSIP 912828PX2” 50% weight.

Measuring Bond Risk

Page 16: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Duration.- It measures number of years needed to

refund the bond price by it internal cash flows

Dv01. - Measures how much the bond value will

change if the interest rate increased or decreased by 1 basis point.

Measuring Bond Risk

Page 17: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

30 years treasury Bond “CUSIP 912810QN1”

Measuring Bond Risk

Description DataBond CUSIP 912810QN1Settlement date 15-Feb-11Maturity date 15-Feb-41coupon rate 4.75%Percent yield 4.75%Redemption value 100frequency ( 1 = annual, 2 = semiannual, 4 = quarterly) 2Basis (1 = Actual/actual, 2 = Actual/360, 3 = Actual/365, 4 = European 30/360) 1price 100Quantity 500

Bond Duration DV01 DV01912810QN1 16.28196651 79.52120397 0.15904257

Duration: 16.28 - The time required to refund the internal cash flow of the bond is 16 years and 3 months.- After 16 years and three months the fluctuations in interest rates doesn’t affect the portfolio value.Dv01: $79.52- The bond value will be changed by $79.52 if yield increased or decreased by 1 basis point.

Page 18: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

10 years treasury note “CUSIP 912828PX2”

Measuring Bond Risk

Duration: 8.47- The time required to refund the internal cash flow of the bond is 8 years and 5 months.- After 8 years and 5 months the fluctuations in interest rates doesn’t affect the portfolio value.

Dv01: $41.45- The bond value will be changed by $41.45 if yield increased or decreased by 1 basis point.

Description DataBond CUSIP 912828PX2Settlement date 15-Feb-11Maturity date 15-Feb-21coupon rate 3.6250%Percent yield 3.6650%Redemption value 99.667623

frequency ( 1 = annual, 2 = semiannual, 4 = quarterly) 2

Basis (1 = Actual/actual, 2 = Actual/360, 3 = Actual/365, 4 = European 30/360) 1

Price 99.43646822Quantity 501

Bond Duration DV01 DV01912828PX2 8.473410655 41.45293307 0.082705747

Page 19: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Bond VaR

Value at Risk “VaR” Measures the worst possible of losses in

dollars that a portfolio expected to loss over period of time.

Var for the period from April 1, 2013 - March 31, 2014 with significant level of 5%.

Two type of VaR: Historical VaR Variance-Covariance

Page 20: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Returns are re-arranged from the worst to the best and suggest that the history it will show itself again.

Historical VaR

Assumes that portfolio returns are distributed normally.

Factors affect Variance-Covariance VaR :- Expected returns.- Standard deviation.

Variance-Covariance

Page 21: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Number of observation 250

250*5%= 12.512= ($891.06)13= ($889.03)($891.06)- ($889.03)= ($2.03)($2.03)*0.5=($1.01)($891.06)+ ($1.01)= ($892.08)

Significance Level 5%Confidence Level 95%

Historical VaR ($892.08)

The confident level is 95%.losses will not exceed 5%.The historical Var calculation is and var value is ($892.08)

Historical VaR

Page 22: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Variance-Covariance

  R1 R2R 0.03463 0.02234Slandered Deviation "Sigma" 0.219511803 0.411003577DV01 79.52120397 41.45293307Rho 0.815635113 Z of 5% significance level  1.64

Component1 Component2 Component3 Component41040.296776 0.604495838 0.380613366 0.375321341

VaR 978.99

The confident level of the portfolio is 95%, the portfolio expected 978 in one day 5% of the time.

Page 23: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

- Types of investorsA) BullishB) Bearish- Option Spread

Trading Strategy

Page 24: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Credit risk - CVaR

Possibility that counterparty defaults United Parcel Service Inc. - ‘AA’ rating Delta Airlines – ‘A’ rating Verizon Communication Inc - ‘BBB’ rating Pennsylvania Elec. Co – ‘BBB’ rating Low default risk Chances that losses exceed $28343.22 less

than 1%

Page 25: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Conclusion ….

Page 26: Student NameStudent IDTutor Name Ali Taqi201000556 Ghassan Alsoud Mohammed Haji201100040 Fatima Mohammed201000393 Marwa Malik201000472

Thank you …