Past Exam Questions Flashcards
Arguments for using semi-variance as a risk measure
Most investors do not dislike uncertainty of returns as such; rather they dislike the possibility of low returns.
One measure that seeks to quantify this view is downside semi-variance.
Arguments against using semi-variance as a risk measure
- not easy to handle mathematically
- takes no account of variability above the mean
- if returns are symmetrically distributed, semi-variance is proportional to variance - so it gives no extra information
- Semi-variance measures downside relative to the mean rather than another benchmark that might be more relevant to the investor
4 Assumptions of mean-variance portfolio theory
- Investors select their portfolios on the basis of the expected return and variance of that return over a single time horizon
- The expected returns, variance of returns and covariance of returns are known for all assets and pairs of assets.
- Investors are never satiated. At a given level of return, they will always prefer a portfolio with lower variance to one with higher variance.
8 Key arguments against modelling market returns using a Gaussian Random walk
- Market crashes appear more often than one would expect from a normal distribution
- While the random walk produces continuous price paths, jumps or discontinuities seem to be an important feature of real markets
- Days with no change, or very small change, also happen more often than the normal distribution suggests
- Assumption of independent increments is contradicted by empirical evidence of mean reversion and momentum effects
- Assumption of a constant volatility is contradicted by empirical evidence.
- Can be argued that expected returns on shares are likely to vary with bond yields, which contradicts the assumption of a constant mean
- Random walks have a fractal dimension of 1.5, whereas investigations of market returns often reveal a fractal dimension around 1.4
- Markets are often (negatively) skewed.
7 Properties of the one-factor Vasicek model
- incorporates mean reversion
- time homogenous, ie future dynamics of r(t) only depend upon the current value of r(t) rather than what the present time t actually is
- Arbitrage free
- Allows negative interest rates
- Easy to implement since the characteristic functions of all related quantities are available
- Constant volatility
7 Properties of the Cog-Ingersoll-Ross model
- Incorporates mean reversion
- Arbitrage free
- Time homogenous
- Volatility depends on the level of the rates (high when rates are high)
- does not allow negative interest rates
- More involving to implement than the Vasicek model as its linke to the chi-squared distribution
- one-factor model
State 12 limitations of CAPM
- Unrealistic assumptions
- Empirical evidence don’t support it
- Investors don’t always use the same “currency”
- Markets are not always perfect
- Investors don’t always have the same expectations
- Cannot borrow/lend unlimited amounts at the same risk-free rate
- Difficult to check as need to think about investment markets as well as capital markets
- Unrealistic to invest in the market portfolio in practice as so many stocks
Does not consider:
- multiple time periods
- or optimisation of consumption over time
Does not account for:
- taxes
- inflation
- situations in which no riskless asset exists
3 Forms of the Efficient Market Hypothesis
- Strong
- Semi-strong
- Weak
Strong form EMH
Market prices reflect all current information relevant to the stock, including information which is not public
Semi-strong form EMH
Market prices reflect all current, publicly available information relevant to the stockq
Weak form EMH
Market prices reflect all information available in the past history of the stock price
7 reasons why its hard to test any of the 3 EMH forms in practice
- need to make assumptions such as normality of returns / stationarity
- Transaction costs may prevent the exploitation of anomalies, so EMH might hold net of transaction costs
- Allowance for risk - EMH does not preclude higher returns as a reward for risk; however the EMH does not tell us how to price such risks
- Testing strong form EMH is problematic & requires access to info that’s not public
- It can be difficult to define “public information” or to determine exactly when information becomes public
- Impossible to test all of the possible trading rules that might be used by technical analysts
- Assumptions made about how security prices should react to new information may be invalid
4 Axioms of Expected Utility Theorem
- Comparability
- Transitivity
- Independence
- Certainty equivalence
Comparability
An investor can state a preference between all available certain outcomes
Transitivity
If a is preferred to B and B is preferred to C, then A is preferred to C.
Independence
If an investor is indifferent between two certain outcomes, A and B, then he is also indifferent between the following two gambles:
- A with probability p and C with probability (1-p)
- B with probability p and C with probability (1-p)
Certainty equivalence
Suppose A is preferred to B and B is preferred to C.
Then there is a unique probability, p, such that the investor is indifferent between B and a gamble giving a with probability p and C with probability (1-p).
B is known as the certainty equivalent of the gamble.
Non Satiation in terms of U(w)
U’(w) > 0
Risk-neutrality in terms of U(w)
U’‘(w) = 0
2 limitations of using Value at Risk to measure the downside risk in an investment portfolio
- VaR does not illustrate the size of the loss in the tail of the distribution, only the likelihood
- Usefulness of VaR may be limited by a lack of data to determine the tail of the distribution.
3 Main Assumptions of Mean-Variance portfolios
- Investors select their portfolios on the basis of the expected return and the variance of that return over a single time horizon
- The expected returns, variance of returns and covariance of returns are known for all assets and pairs of assets.
- Investors are never satiated. At a given level of risk, they will always prefer a portfolio with a higher returnn to one with a lower return.
3 types of credit risk model
- Structural model
- Reduced-form models
- Intensity-based models
Structural models
Explicit models of a corporate entity issuing both debt and equity.
aim to link default events explicitly to the fortunes of the issuer.
Reduced-form models
Statistical models which use market statistics (credit ratings) rather than specific data relating to the issuer, and give statistical models for their movement.
Intensity-based models
Model the factors influencing the credit events which lead to default and typically do not consider what triggers these events.
Describe how the Merton model can be used to estimate the risk-neutral probability of default.
The company is modelled as having fixed debt L and variable assets Ft.
This means equity holders can be regarded as holding a European call on the assets with a strike of L.
It follows from the Black-Scholes model that we can deduce the (risk-neutral) default probability from the share price.
Utility
The satisfaction that a consumer obtains from a particular course of action.
Marginal rate of substitution
The amount of one good that a consumer is prepared to swap for one extra unit of another good.
Indifference curves
Joins all the consumption bundles of equal utility.
The slope of a consumer’s indifference curves will depend on his/her individual preferences and is equal to the marginal rate of substitution.
Consumption bundle
A given combination of goods.
3 Assumptions of consumer choice theory
- A consumer can rank any two bundles
- Consumers prefer more of a good to less of it
- Consumer preferences exhibit diminishing marginal rates of substitution
Assumptions of the budget constraint
- Prices of goods are fixed
- Consumer’s income is fixed
These two assumptions determine which consumption bundles are affordable. The budget line joins all points that a consumer can afford, assuming that all income is spent.
Utility Maximisation
Economists assume that consumers’ choices exhibit rational behaviour.
A rational consumer will choose the consumption bundle that maximises his own utility. This is the concept of utility maximisation.
Efficient portfolio
A portfolio is efficient if the investor cannot find a better one in the sense that is has a :
- higher expected return with the same variance
- lower variance with the same expected return.
Assumptions required for the existence of efficient portfolios
- Investors are never satiated
- Dislike risk
- Select assets based on mean and variance of return only
- Mean return, variance (or standard deviation) and co-variances are known for all assets.
Arbitrage pricing theory
Equilibrium market model that does not rely on the strong assumptions of the capital asset pricing model (CAPM).
Requires that the returns on any stock be linearly related to a set of factor indices as shown:
3 Main elements to the traditional theory of consumer choice
- Consumer’s preferences
- Budget constraint
- How the consumer decides which consumption bundle to choose
How consumers choose
Implications of utility maximisation:
- rational consumer will choose a consumption bundle such that the marginal rate of substitution is equal to the slope of the budget line - that is, where the ratios of marginal utilities equal the ratios of prices.
8 Key findings in behavioural finance
- anchoring and adjustment
- prospect theory
- framing
- myopic loss aversion
- estimating probabilities
- overconfidence
- mental accounting
- effect of options
Anchoring
Term used to explain how people will produce estimates.
They then adjust away from this initial anchor to arrive at their final judgement.
Prospect theory
Theory of how people make decisions when faced with risk and uncertainty. Replaces the conventional risk averse / risk seeking decreasing marginal utility theory.
Framing
The way a choice is presented (framed) and particularly, the wording of a question in terms of gains and losses, can have an enormous impact on the answer given or the decision made.
Myopic loss aversion
Similar to prospect theory, but considers repeated choices rather than a single gamble.
3 issues that might affect probability estimates
- Dislike of “negative” events
- Representative Heuristics
- Availability
Dislike of “negative events
The valence of an outcome has an influence on the probability estimates of its likely occurrence.
Valence
The degree to which it is considered as negative or positive
Representative Heuristics
People find more probable that which they find easier to imagine. As the amount of detail increases, its apparent likelihood may increase.
Availability
People are influenced by the ease with which something can be brought to mind.
Overconfidence
People tend to overestimate their own abilities, knowledge and skills.
This may be a result of
- hindsight bias
- confirmation bias
Hindsight bias
Events that happen will be thought of as having been predictable prior to the event.
Events that do not happen will be thought of as having been unlikely prior to the event.
Confirmation bias
People will tend to look for evidence that confirms their point of view (and dismiss evidence that does not justify it)
Mental accounting
People show a tendency to separate related events and decisions and find it difficult to aggregate events.
Effect of options
- Primary effect
- Recency effect
- People might be more likely to choose an intermediate option
- A range of options tend to discourage decision-making.
- Status Quo bias
- Regret aversion
- Ambiguity aversion
Primary effect
People are more likely to choose the first option presented
Recency effect
In some instances, the final option that is discussed may be preferred (The gap in time between the presentation of options & decisions may influence this)
Status Quo bias
People have a marked preference for keeping things as they are
Regret aversion
By retaining the existing arrangements, people minimise the possibility of regret.
Ambiguity aversion
People are prepared to pay a premium for rules.
Reasons why standard Brownian motion might not be suitable for the short-rate
- Interest rates may not be positive
- Interest rates do not display mean reversion
- Model won’t give a realistic range of yield curves
- Model whon’t fit historical data well
- It cannot be calibrated to current market data
- Not very flexible (single factor model_
- Model is Arbitrage-free
Outline evidence against normality assumptions in models of market returns
- Market crashes appear more often than expected from a normal distribution (jumps/discontinuities are an important feature of real markets)
- Days with now change / very small change happen more often than the normal distribution suggests.
- Q-Q plots of the observed changes in FTSE All Share against those that would be expected if returns were lognormally distributed show substantial differences. Actual returns have many more extreme events.
Expected utility theorem
A function, U(w) can be constructed representing an investor’s utility of wealth, w, at some future date.
Decisions are made on the basis of maximising the expected value of utility under the investor’s particular beliefs about the probability of different outcomes.
4 Axioms for Expected Utility Theorem
- Comparability
- Transitivity
- Independence
- Certainty equivalence
Name 3 types of multifactor models
- Macroeconomic factor models
- Fundamental factor models
- Statistical factor models
Macroeconomic factor models
Use observable economic time series as the factors.
Fundamental factor models
Use company specific variables as the factors
Statistical factor models
Principle components analysis is used to determine a set of indices which explain as much as possible of the observed variance.
These indices are unlikely to have any meaningful economic interpretation and may vary considerably between different data sets.
Cross-sectional properties of statistical distributions
Cross-sectional property fixes a time horizon and looks at the distribution over all the simulations.
(e.g. what will inflation be next year?)
The estimates are implicitly conditional on past information.
Can be deduced from prices of options and other derivatives.
Longitudinal properties of statistical distributions
Longitudinal property looks at the distribution over a long period of time.
E.g. what will the distribution of inflation be over the next 1000 years?
Unlike cross-sectional properties does not reflect market conditions at a particular time.
Alternative risk measures
- Value at Risk
- Tail Value at Risk
- Expected Shortfall
6 Assumptions underlying the Black-Scholes model
- Price of the underlying share follows a geometric Brownian motion
- There are no risk-free arbitrage opportunities
- Risk-free rate of interest is constant, the same for all maturities and the same for borrowing or lending.
- Unlimited short selling (negative holdings) is allowed
- There are no taxes or transaction costs
- Underlying asset can be traded continuously and in infinitesimally small numbers of units
Arbitrage opportunity
A situation where we can make a sure profit with no risk.
More precisely it means that
- We can start at time 0 with a portfolio which has a net value of zero.
At some future time T
- the probability of a loss is 0
- the probability that we make a strictly positive profit is greater than 0.
Outline relevant empirical evidence & theoretical arguments for:
- the volatility of returns over time
Direct statistical evidence shows volatility varies over time.
Volatility implied from option prices also shows volatility / volatility expectations vary over time.
Outline relevant empirical evidence & theoretical arguments for:
- the expected value of returns over time
Good theoretical reasons to expect the expected value of returns to vary over time.
Equities should give a risk premium over bonds and bond yields vary over time.
Empirically difficult to test.
Outline relevant empirical evidence & theoretical arguments for:
- Whether stock prices are mean reverting.
Empirically unsettled.
Some evidence for mean reversion but rests heavily on aftermath of a few dramatic crashes also conversely some evidence of momentum effects.
Outline relevant empirical evidence & theoretical arguments for:
- statistical distribution of returns
Strong empirical evidence that prices are non-normal.
Crashes happen more than would be expected. In addition more days with small/no changes than one would expect.
Discuss the extent to which a continuous time lognormal model of security prices can capture the statistical properties empirically observed or expected in the stock market.
- Random walk assumes constant volatility.
- Random walk assumes drift is constant
- No allowance for mean reversion
- Assumes normality.
State how investors are assumed to make decisions in modern portfolio theory.
Select on the basis of expected return and variance of return over a single time horizon.
Define an efficient portfolio in the context of MPT.
A portfolio is efficient if an investor cannot find a better one in the sense that it has both a higher expected return and a lower variance.
Define the market price of risk in CAPM
(Em - r)/σm
Name 3 approaches to modelling credit risk
- Structural models
- Reduced form models
- Intensity-based models
Structural models
explicit models of a corporate entity issuing both equity and debt.
Aim to link default events explicitly to the fortunes of the issuing corporate entity.