Kalpesh Agrawal- Kalpesh Agrawal
CM2 Topics
List of CM2 topics.
The dominant theory
Fundamental analysis
Efficient Market Hypothesis (EMH)
Weak form EMH
Semi-strong form EMH
Strong form EMH
Informational efficiency
Volatility tests
Meaning of utility
Utility functions
The expected utility theorem
The marginal utility of wealth
Fair gamble
Derivation of the expected utility theorem
Comparability
Transitivity
Independence
Certainty equivalence
Non-satiation (Utility function = U'(w) > 0)
Risk aversion
Risk-averse investor (Utility function = U''(w) < 0)
Risk-seeking investor (Utility function = U''(w) > 0)
Risk-neutral investor (Utility function = U''(w) = 0)
Measuring risk aversion
Addictive/Absolute gamble:
Take one example of a fair gamble represented by a random variable y. The amounts that an investor can lose or win are fixed absolute amounts. In this case, the amount lost or won by the investor is independent of the wealth (w).
So, if the investor accepts the gamble, the resulting final wealth is w + y.
U(Cw) = E[U(w + x)]
Multiplicative/proportional gamble:
Take one example of a fair gamble represented by a random variable y. The amounts that an investor can lose or win are all expressed as proportions of the initial wealth.
So, if the investor accepts the gamble, the resulting wealth is w * y.
U(Cw) = E[U(w * y)]
Absolute risk aversion (ARA)
denoted by A(w)
measured by the function A(w) = -U''(w) / U'(w)
Relative risk aversion
denoted by R(w)
measured by the function R(w) = -w * (-U''(w) / U'(w))
The quadratic utility function
The log utility function
Iso-elastic utility function
The power utility function
Negative exponential utility function
Hyperbolic Absolute Risk Aversion (HARA)
State-dependent utility functions
How to find maximum and minimum premium
Limitations of utility theory
What is Behavioural finance?
Absolute dominance
Stochastic dominance
First-order stochastic dominance
Second-order stochastic dominance
Prospect theory:
Daniel Kahneman and Amos Tversky developed a 'Prospect Theory' in 1979.
In this paper, they stated that people would like to evaluate gains and losses differently.
Kahneman conducted more research in loss aversion and discovered several additional key insights.
The insights captured by him are called the Fourfold Pattern, which goes beyond loss aversion to show the di¤erent ways people respond to high-probability and low-probability events when they are either gains or losses.
This is the answer to "Why Insurance exists"!!!
Even though the chance of a loss is small, if it happens it will be painful. Hence we're willing to pay a small insurance premium to eliminate or reduce the risk.
