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CFA Level 2 > Portfolio Management > Flashcards

Portfolio Management Flashcards

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1
Q

What is the correlation between (sensitivity of corporate bond’s spread to changes in business cycle) to (that business’ level of cyclicality)

A

Positive

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2
Q

Sources of Tracking Error

A

1) Fees and expenses
2) Representative sampling/optimization
3) ADRs
4) Fund accounting practices
5) Regulatory and tax requirements
6) Asset manager operations

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3
Q

Holding period cost (%)

A

Round-trip trade cost (%) + Management fee for period (%) + bid-offer spread on purchase/sale

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4
Q

Standardized Beta

A
  • utilized in fundamental factor models
  • the value of the attribute for the asset minus the average value of the attribute across all stocks divided by the standard deviation of the attribute’s values across all stocks
  • (value of attribute k for asset i - average value of attribute k) / standard deviation of the values of attribute k
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5
Q

Active Return

A

return on a portfolio minus the return on the portfolio’s benchmark

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6
Q

Active risk

A

The standard deviation of active returns

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7
Q

Tracking Error

A
  • also called tracking risk and can be referred to as active risk
  • sample standard deviation * (R(p) - R(b))
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8
Q

Information Ratio

A
  • tool for evaluating mean active returns per unit of active risk
  • IR = (R(p) - R(b)) / TE
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9
Q

Active risk squared

A
  • variance of active return
  • s^2 * (R(p) - R(b))
  • Active factor risk + active specific risk
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10
Q

Active factor risk

A

The contribution to active risk squared resulting from the portfolio’s different-from-benchmark exposures relative to factors specified in the risk model

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11
Q

Active specific risk

A
  • also called security selection risk
  • Measures the active non-factor or residual risk assumed by the manager
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12
Q

3 Types of Multifactor models

A

1) Macroeconomic factor models

2) Fundamental factor models

3) Statistical factor models
- statistical methods are applied to a set of historical returns to determine portfolios that explain historical returns in one of two sense, factor analysis models and principal-components models

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13
Q

Factor Analysis Models

A

Factors are the portfolios that best reproduce historical return covariances

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14
Q

Value at risk (VaR)

A

The minimum loss that would be expected a certain percentage of the time over a certain period of time given the assume market conditions

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15
Q

How many standard deviations below expected value is 5% VAR

A

1.65

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16
Q

How many standard deviations below expected value is 1% VAR

A

2.33

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17
Q

1 Standard Deviation below expected value is what % VAR

A

16%

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18
Q

Steps to estimate VaR using parametric method

A

1) multiply portfolio standard deviation by the number of standard deviations that correspond to the VaR
2) subtract the step 1 from daily expected return
3) take the absolute value of step 2
4) Multiple step 3 by portfolio value

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19
Q

Steps to estimate annual parametric VaR

A

1) find the corresponding annual std (daily std * sqrt(250)) and expected return (daily expected return * 250)
2) multiple annual std by number of standard deviations corresponding to the VaR
3) subtract that value from the annual expected return calculated in step 1
4) take absolute value of that figure
5) multiple that figure by the total portfolio value

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20
Q

Historical simulation method of VaR

A

uses the current portfolio and reprices it using the actual historical changes in the key factors experienced during the lookback period

  • does not use expected return, standard deviation, or correlations
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21
Q

Conditional VaR

A

The weighted average of all loss outcomes in the return distribution that exceed the VaR loss

  • answers the question, “How much can i expect to lose if VaR is exceed?”
  • more comprehensive than VaR
  • referred to as expected tail loss or expected shortfall
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22
Q

Incremental VaR (IVaR)

A

A measure of the incremental effect of an asset on the VaR of a portfolio by measuring the difference between the portfolio’s VaR while including a specified asset and the portfolio’s VaR with that asset eliminated

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23
Q

Marginal VaR (MVaR)

A

A measure of the effect of a small change in a position size on portfolio VaR

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24
Q

Delta

A

relationship between the option price and the underlying price

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25
Q

Gamma

A

2nd order, how sensitive delta is to change in the value of underlying

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26
Q

Vega

A

1st order, the change in the value of the derivative based off a change in the value of volatility

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27
Q

Pass/fail test

A

Stress tests used by companies that use leverage

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28
Q

Reverse stress testing

A

user identifies key risk exposures in the portfolio and subjects those exposures to extreme market movements

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29
Q

How often are securities in an ETF disclosed

A

daily

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30
Q

Creation basket

A

list of required in-kind securities published each day by the ETF sponsor

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31
Q

When an authorized participant transacts to create or redeem ETF shares, the related costs are borne by who

A

transacting shareholders

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32
Q

Smallest contributor to tracking error

A

Changes to the underlying index securities

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33
Q

Fund-Closure Risk

A

Risk that an ETF may shut down due to regulations, competition, and corporate action

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34
Q

Expectations-related risk

A

Risk that some ETF investors may not fully understand how more complex ETFs will perform

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35
Q

3 Assumptions of APT

A

1) A factor model describes asset returns
2) There are many assets, so investors can form well-diversified portfolios that eliminate asset-specific risk
3) No arb opportunities exist among well-diversified portfolios

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36
Q

Expected return when using macroeconomic factor model

A

the intercept (when all model factors take on a value of zero)

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37
Q

which of the factor models make minimal assumptions

A

statistical factor models

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38
Q

Two components of portfolio’s active return

A

factor tilt and security selection

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39
Q

What are the factors in macroeconomic factor model

A

surprises in macroeconomic data

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40
Q

In which model are standardized betas used

A

Fundamental factor models

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41
Q

Diversification reduces which part of a portfolio’s risk

A

active specific risk, the portfolio with the lowest active specific risk is the most diversified

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42
Q

Delta range of long call options

A

0 to 1

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43
Q

Delta range of short call options

A

0 to -1

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44
Q

Delta range of long put options

A

0 to -1

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45
Q

Delta range of short put options

A

0 to 1

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46
Q

Surplus at risk

A

application of VAR that estimates how much the assets might underperform liabilities with a given confidence level, usually over a year

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47
Q

Ex post vs ex ante tracking error

A

If tracking error is measured historically, it is ex post
If tracking error is predictive, it is ex ante

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48
Q

gross exposure

A

measures the combination of long and short exposures and can be an important metric in the management of hedge fund exposure

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49
Q

scenario limit

A

limit on the estimated loss for a given scenario, which if exceeded, would require corrective action in the portfolio

50
Q

relative Var

A

measure of the degree to which the performance of the portfolio might deviate from its benchmark, ex ante tracking error

51
Q

bootstrapping

A

random sampling with replacement

52
Q

inverse transformation

A

method of random observation generation, often used in simulation

53
Q

what do you do when returns from various factors are correlated in monte carlo simulation

A

specify a multivariate distribution rather than modeling each factor or asset on a standalone basis

54
Q

impact of increase in income on marginal utility of consumption

A

decreases marginal utility of consumption

55
Q

impact of increase in income on required risk premium

A

decreases required risk premium

56
Q

Correlation (positive/negative) between real short-term interest rates and real GDP growth

A

Positive

57
Q

correlation (positive/negative) between real short-term interest rates and volatility of real GDP growth

A

positive

58
Q

the covariance between a risk-averse investor’s inter-temporal rate of substitution and the expected future price of a risky asset is, positive/negative/zero

A

negative

59
Q

what are positive output gaps usually associated with

A

economic growth beyond sustainable capacity

60
Q

break even inflation rate

A

The difference between the yield on a zero-coupon, default-free nominal bond and on a zero-coupon default free real bond of the same maturity

61
Q

data snooping

A

practice of determining a model by extensive searching through a dataset for statistically significant patterns

62
Q

p hacking

A

same thing as data snooping

63
Q

inter-temporal rate of substitution

A

ratio of the marginal utility of consumption n periods in the future to the marginal utility of consumption today

64
Q

utility derived from an additional unit of consumption in bad economic times

A

relatively high, because current income and consumption is low

65
Q

asset that has relatively high returns when marginal utility of consumption is high provides a hedge against bad times…risk premium? price? required rate of return?

A

negative risk premium, high price, low required rate of return

66
Q

shape of the yield curve for default free government bonds during a recession

A

steepen

67
Q

correlation of returns to short-dated bonds to bad times vs. long-dated bonds

A

more negatively correlated with bad times than are returns to long-dated bonds

68
Q

The information coefficient, links which two factors together

A

Forecasted active returns with realized active returns

69
Q

how to measure consistency of active return

A

IR, Active Return/Active Risk

70
Q

relationship between sharpe ratio and information ratio

A

SR^2 (portfolio) = SR^2 (benchmark) + (TC^2) * IR^2 (fund)

71
Q

Information ratio of the unconstrained optimal portfolio

A

(transfer coefficient) * (information coefficient) * (sq(breadth))

IC * Sq(Breadth)

TC = 1

72
Q

Transfer coefficient

A

Measures the degree to which the investor’s forecasts are translated into active weights

The correlation between any set of active weights and forecasted active returns

73
Q

Expected active portfolio return

A

E(R(a)) = IC*Sq(breadth)

74
Q

Expected Active Return

A

Managed portfolio return - benchmark return

75
Q

Active risk of the managed portfolio

A

square root of the sum of active weights times the active volatility squared for each security

76
Q

Unconstrained portfolio optimal active risk

A

std(p) = TC* (IR / SR(b) )*std(b)

77
Q

Information coefficient of a market timer

A

IC = 2*(% correct) - 1

78
Q

Limitations of the fundamental law

A

1) Bias in measurement of the ex-ante IC
2) Lack of independence while measuring the breadth of an active strategy

79
Q

2 Broad categories of trading algorithsm

A

1) Execution algorithms
2) High-frequency algorithms

80
Q

Execution algorithms

A

Break a large order, typically placed by institutions, into smaller pieces which are then placed strategically over time to minimize negative price risk

81
Q

High-frequency algorithms

A

Rules for trading on real-time market data that a computer uses to pursue profit opportunities

Referring to rapidly updated information sources

82
Q

Types of Execution Algorithms

A

1) Volume-weighted average price (VWAP)
2) Implementation shortfall
3) Market participation

83
Q

VWAP algorithm

A

split an order into pieces sized proportionally to the security’s historical trading pattern over a day

84
Q

Implementation shortfall algorithm

A

continually adjust the speed at which a trade executes as market conditions change to try to minimize difference between decision price and final execution price

85
Q

market participation algorithms

A

slice larger orders into smaller pieces that are then entered in the market at a pace that matches the pace of overall trading of the security

86
Q

Types of high frequency trading algorithms

A

1) Statistical arb
2) Liquidity aggregation and smart order routing
3) Real-time pricing
4) Trading on news
5) Genetic tuning

87
Q

Statistical arb algo

A

Used to trade securities that historically have moved together

1) Pairs trading
2) Index arb
3) Basket trading
4) Spread trading
5) Mean reversion
6) Delta-neutral strategies

88
Q

Liquidity aggregation/smart order routing

A

Sending each order to the market that has the best combination of price and liquidity (smart order routing) or by spreading the order over several trading venues (liquidity aggregation)

89
Q

Genetic Tuning

A

a self-evolving system that tests many different strategies, implements profitable strategies, and kills off money-losers

90
Q

Two methods of using algos to mitigate trading risks

A

1) Real-time trade risk firewalls
2) Back-testing and market simulation

91
Q

Real-time trade risk firewalls

A

Constantly calculate risk exposures on trades to ensure that risk limits are not exceeded. Trades that would exceed limits are blocked

92
Q

Backtesting and market simulation

A

Testing algos to see how they would perform in response to various offline scenarious or historical data

93
Q

Value added from security selection

A

(Portfolio return - benchmark return) *portfolio allocation

94
Q

Value added from asset allocation

A

(Portfolio weight - benchmark weight) * Benchmark return

95
Q

Impact on information ratio when the aggressiveness of the active weights of an unconstrained portfolio is increase/decreased

A

No change

96
Q

Impact on the sharpe ratio by the addition of cash or leverage in a portfolio

A

No change

97
Q

Information ratio of a closet index

A

indeterminate, small, and often negative due to management fees

98
Q

highest attainable sharpe ratio of an actively managed portfolio

A

SR(p)^2 = SR(b)^2 + IR^2

99
Q

What does the information coefficient measure

A

IC measures an investment manager’s ability to forecast returns

100
Q

Active share

A

measure of the percentage of the portfolio that differs from the benchmark

exact replications of indexes have active share of 0

completely different will be 1

101
Q

Index tracking

A

The one-day difference in returns between the ETF fund and the underlying index

102
Q

First step of a VAR calculation

A

convert the et of holdings in the portfolio into a set of exposures to risk factors

103
Q

When are indicated NAVs for ETFs calculated by the exchange

A

during the day

104
Q

All else equal, saving today for one year will how impact marginal utility of consumption

A

increase marginal utility of consumption today

105
Q

With respect to ETF’s, what is a soft closure

A

a change in investment strategy

106
Q

Realized value added through active management is a function of the correlation between what

A

what the portfolio manager implements through active weights and the portfolio’s realized active returns

107
Q

Information coefficient represents the correlation between what

A

What the portfolio manager forecasts for active return and the portfolio’s realized active returns

108
Q

Transfer coefficient represents the correlation between what

A

Optimal active weights and actual active weights

109
Q

Full fundamental Law

A

TC * IC * Sq(BR) * Active Risk

110
Q

Who absorbs the costs of transacting securities for the ETF

A

the authorized participant

111
Q

Which source of tracking error contributes to overperformance in an ETF

A

securities lending

112
Q

Portfolio construction is completed in which step of the backtesting process

A

Historical investment simulation

113
Q

Who uses maximum drawdown as a risk measure

A

Hedge fund

114
Q

What kind of model is the Carhart 4-factor model

A

fundamental factor

115
Q

In which model are factor sensitives estimated first

A

fundamental factor models

116
Q

iNAVs

A

Intraday “fair value” estimates of an ETF share based on its creation basket composition for that day

117
Q

What is the use of ETFs in portfolio rebalancing

A

maintain exposure to target weights

118
Q

Which part of the macroeconomic factor model is asset-specific risk specified by

A

error term

119
Q

Neutral Rate of interest

A

Short-term interest rate + targeted rate of inflation

120
Q
A

CFA Level 2 (11 decks)

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