Explain carefully why a distribution with a heavier left tail and less heavy right tail than the

step 1 Okay, so we're going to let n equal number of trials and p equal the probability of success. Oka

Explain carefully why a distribution with a heavier left...

Key Concepts

Implied Volatility Skew

The implied volatility skew refers to the pattern of implied volatilities that vary with strike prices. When the left tail of the return distribution is heavier, lower strike options (typically puts) become more expensive, leading to higher implied volatilities at these strikes compared to higher strikes. This results in a downward sloping (or 'smirk') volatility profile, where implied volatility decreases as the strike price increases.

Risk-Neutral Distribution

In options pricing, the risk-neutral distribution represents the probability distribution of future asset prices under the assumption that investors are indifferent to risk. The shape of this distribution affects option premiums directly; a heavier left tail in the risk-neutral distribution leads to a higher premium for options that protect against downside risk. This altered weighting of probabilities compared to the lognormal assumption is directly tied to the phenomenon of the volatility smile or skew.

Heavy Tails (Tail Risk)

Heavy tails in a probability distribution, particularly on one side, indicate a higher probability of extreme outcomes compared to a normal or lognormal distribution. A heavier left tail signifies a higher likelihood of large downward movements (crashes), whereas a less heavy right tail suggests a lower chance of extreme upward movements. Options, especially out-of-the-money put options, become more valuable due to increased risk of substantial losses in the underlying asset.

Volatility Smile

A volatility smile is a graphical representation that shows implied volatility plotted against various strike prices. It reflects how market participants price options with different strikes and highlights deviations from the constant volatility assumption of the Black-Scholes model. Variations in implied volatility across strikes suggest that the underlying asset's return distribution deviates from the lognormal assumption, indicating that certain outcomes (especially extreme events) are priced differently.

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