An asset price of USD 0 is possible in:
Explanation
In standard BSM theory, prices are strictly positive.
Other questions
Which probability distribution is most commonly used to describe asset prices in financial modeling?
What is the lower bound of a log-normal distribution?
Which statistical property characterizes the shape of a log-normal distribution?
The Black-Scholes-Merton option pricing model assumes that the price of the underlying asset follows which distribution?
If an asset's price is log-normally distributed, what distribution do its continuously compounded returns follow?
A stock has an opening price of USD 100 and a closing price of USD 105. What is the continuously compounded daily return?
A stock price moves from USD 50 to USD 48. Calculate the continuously compounded return.
What does the abbreviation 'i.i.d.' stand for in the context of asset returns?
What does the property of 'stationarity' imply about an asset's returns?
Which rule is used to scale the standard deviation of returns from a shorter period to a longer period?
If the daily standard deviation of a stock's return is 1 percent, what is the approximate annualized standard deviation (assuming 250 trading days)?
If the daily expected return of an asset is 0.05 percent, what is the expected annual return (assuming 250 trading days)?
You observe a stock price of USD 120 on Day 1 and USD 140 on Day 2. Calculate the continuously compounded return.
If the weekly variance of returns is 0.0004, what is the annual standard deviation (assuming 52 weeks)?
Which of the following describes 'independence' in the context of i.i.d. returns?
Given a daily return of 0.1 percent and a daily volatility of 1.5 percent, what is the annual Sharpe Ratio numerator (Annual Return) assuming 250 days and a risk-free rate of 0?
Monte Carlo simulation is best described by which analogy?
Which of the following is the first step in the Monte Carlo simulation process?
Monte Carlo simulation is particularly strong for pricing which type of financial instrument?
What is a cited weakness of Monte Carlo simulation?
What is the primary difference between Bootstrapping and Monte Carlo simulation regarding data source?
In Bootstrapping, how are samples drawn from the original dataset?
Bootstrapping is most useful when:
If a stock's annual volatility is 30 percent, what is the estimated monthly volatility (assuming 12 months)?
Calculate the continuously compounded return for a stock that drops from 200 to 170.
Comparing analytic methods to Monte Carlo simulation, analytic methods:
A daily return of 1% is observed. If the returns are i.i.d., what is the cumulative simple return over 2 days (ignoring compounding for a moment, or assuming small numbers)?
If the daily volatility is 1.825 percent, what is the annualized volatility assuming 250 days?
What does 'with replacement' mean in the context of Bootstrapping?
In a Monte Carlo simulation, step 2 involves 'Defining Ranges'. This refers to:
Which method builds a 'True Sampling Distribution' based on the observed data?
When scaling from daily to annual data, the variance of returns is multiplied by:
If a distribution has a 'fat tail' (leptokurtic), relying on a normal distribution assumption in a simulation would likely:
Continuously compounded returns are also known as:
A limitation of using historical data for simulations (like Bootstrapping) is that:
If Day 1 return is 2 percent and Day 2 return is 3 percent, what is the two-day continuously compounded return?
In a Monte Carlo simulation for retirement planning, a likely 'input variable' would be:
Calculate the annual expected return if the daily expected return is 0.01 percent (250 days).
If a simulation has 1,000 trials, the resulting distribution of outcomes allows analysts to:
Which method is preferred when checking the 'robustness' of a trading strategy using only its past trade data?
Log-normal distributions are skewed to the:
If daily mean return is 17.33% (annualized) and you want to convert it back to daily:
Which of the following describes the 'Velocity' characteristic of Big Data (mentioned in the context of simulation inputs/tech)?
In the equation for continuously compounded return R = ln(S1/S0), what does S0 represent?
When simulating asset prices using a log-normal model, the returns are assumed to be:
If a simulation model assumes volatility is constant, but in reality volatility changes (heteroskedasticity), the model is likely to:
To calculate the continuously compounded annual return given a daily return of 0.05%, you perform which operation?
Which simulation technique would be required to generate a 'probability density function' of a portfolio's future value?
If a stock price is USD 50 and annual volatility is 20%, what is the daily standard deviation used in a simulation step?