Answer: IV only

**Explanation:** Let's analyze each statement: **Statement I:** "One of the downsides of MC simulation is that it can be computationally intensive and sometimes necessitate the use of expensive software" - This is **CORRECT**. Monte Carlo simulation requires generating thousands or millions of random scenarios, which can be computationally expensive and may require specialized software. **Statement II:** "MC simulation can be applied in the presence of data that takes on the lognormal distribution" - This is **CORRECT**. Monte Carlo simulation can handle various probability distributions, including lognormal distributions, which are commonly used in finance for modeling asset prices. **Statement III:** "MC allows for a wider variety of scenarios than those that can be deduced from historical data by itself" - This is **CORRECT**. Monte Carlo simulation can generate forward-looking scenarios based on statistical distributions, not just historical patterns. **Statement IV:** "It can only be applied if portfolios contain linear positions" - This is **INCORRECT**. Monte Carlo simulation can handle both linear and nonlinear positions (like options). For nonlinear positions, the simulation typically involves two steps: first simulating the underlying risk factors, then pricing the derivative based on those simulated values. Since only Statement IV is incorrect, the correct answer is **C. IV only**. The text confirms: "MC simulations can also generate distributions for portfolios that also contain nonlinear positions. They do account for options, where the first steps entails simulation of the risk factor and the second step prices the option, thereby accounting for nonlinearity."

Author: Nikitesh Somanthe