FINANCIAL MODELING ACTUAL FINAL EXAM |110 COMPLETE AND GRADED
QUESTIONS AND ANSWERS 2026 LATEST UPDATED | 100% GRADED CORRECT | 100%
GUARANTEED TO PASS | GET A+
1. VaR (Value at Risk): A statistical technique used to measure and quantify the level of financial risk within a firm or
investment portfolio over a specified time frame 2. VaR 3 inputs: 1) Amount of potential loss
2) The probability of that amount of loss (confidence level)
3) Time frame
3. VaR Assumptions: 1) Normal Distribution of Returns
2) Standard Market Conditions
4. 2 Ways to Calculate Var: 1) Variance-Covariance Method
2) Monte Carlo Simulation
5. Variance-Covariance Method (VaR): 1) Calculate period returns of the stocks & covariance matrix 2) Calculate the
portfolio mean/std
3) Calculate the inverse of the normal cumulative distribution (PPF) with a specified confidence interval, mean, and
standard deviation.
4) Estimate the VaR for the portfolio by subtracting the initial investment from the calculation in step 3
6. Monte Carlo Simulation: - Computational method using random numbers
- A class of computational algorithms that rely on repeated random sampling to compute their results.
7. Monte Carlo Uses: 1) When simulating physical and mathematical systems
2) Suited to calculation by computers
3) Used when it is infeasible or impossible to compute an exact result with a deterministic model
,8. Why do we need Monte Carlo Sim?: Use random numbers extensively to simulate stock prices, investment
strategies, and option strategies.
9. Random Number Generator: Lehmer (famous mathematician, Berkeley): "A random sequence is a vague notion
embodying the idea of a sequence in which each term is unpredictable to the uninitiated and whose digits pass a certain
number of tests, traditional with statisticians and depending somewhat on the uses to which the sequence is to be put."
10. Psuedo Random Number Generator: - In python, we have different (pseudo) random number generators -
"random" module
- a deterministic algorithm that produces a sequence of numbers that appears random but is completely predictable if the
initial "seed" value is known
11. Goal of random number generators: 1) examine the uniform random-number generators 2) generate normally
distributed random numbers
3) generate correlated random numbers using the Cholesky decomposition
12. Monte Carlo Limitations: - Often time-consuming, computationally wasteful
- Analytical methods (formulas) are better if available
13. 2 Reasons We May Still want Monte Carlo: 1) Other distribution
2) More complicated VaR problems
14. Random Walk Hypothesis: 1) Prices in financial markets follow a random walk, or in continuous time, an arithmetic
Brownian motion without drift
2) The best predictor for tomorrow's price, in a least squares sense, is today's price if RWH applies
15. Eugene Fama Quote about RWH: "For many years, economists, statisticians, and teachers of finance have been
interested in developing and testing models of stock price behavior. One important model that has evolved from this
research is the theory of random walks. This theory casts serious doubt on many other methods for describing and
predicting stock price behavior— methods that have considerable popularity outside the academic world. For
example, we shall see later that, if the random-walk theory is an accurate description of reality, then the various
, "technical" or "chartist" procedures for predicting stock prices are completely without value."
16. Forms of Efficient Market Hypothesis (EMH): The RWH is consistent with the efficient markets
hypothesis (EMH), which, non-technically speaking, states that market prices reflect "all available information." Several
forms of EMH:
1) Weak
2) Semi-strong
3) Strong
The form depends what "all available information" entails: "A market is efficient with respect to an information set S if
it is impossible to make economic profits by trading on the basis of information set S" (Michael Jensen (1978)) 17.
Weak Form EMH: All past market data
- Prices already reflect past prices, returns, volume, technical patterns.
- You cannot earn excess returns using technical analysis.
- But fundamental analysis might still help, because public news is not guaranteed to be priced in.
Key idea: You can't beat the market by looking at charts or historical price trends.
18. Semi-strong form EMH: All publicly available information
- Includes past price data plus all public news: earnings, filings, economic data, press releases, analyst reports, etc. -
Neither technical analysis nor fundamental analysis can consistently earn abnormal profits.
Key idea: The instant information becomes public, it's already reflected in the stock price.
19. Strong form EMH: All information (public + private) - Prices
even reflect inside information.
- No one can consistently earn excess returns, not even insiders.
QUESTIONS AND ANSWERS 2026 LATEST UPDATED | 100% GRADED CORRECT | 100%
GUARANTEED TO PASS | GET A+
1. VaR (Value at Risk): A statistical technique used to measure and quantify the level of financial risk within a firm or
investment portfolio over a specified time frame 2. VaR 3 inputs: 1) Amount of potential loss
2) The probability of that amount of loss (confidence level)
3) Time frame
3. VaR Assumptions: 1) Normal Distribution of Returns
2) Standard Market Conditions
4. 2 Ways to Calculate Var: 1) Variance-Covariance Method
2) Monte Carlo Simulation
5. Variance-Covariance Method (VaR): 1) Calculate period returns of the stocks & covariance matrix 2) Calculate the
portfolio mean/std
3) Calculate the inverse of the normal cumulative distribution (PPF) with a specified confidence interval, mean, and
standard deviation.
4) Estimate the VaR for the portfolio by subtracting the initial investment from the calculation in step 3
6. Monte Carlo Simulation: - Computational method using random numbers
- A class of computational algorithms that rely on repeated random sampling to compute their results.
7. Monte Carlo Uses: 1) When simulating physical and mathematical systems
2) Suited to calculation by computers
3) Used when it is infeasible or impossible to compute an exact result with a deterministic model
,8. Why do we need Monte Carlo Sim?: Use random numbers extensively to simulate stock prices, investment
strategies, and option strategies.
9. Random Number Generator: Lehmer (famous mathematician, Berkeley): "A random sequence is a vague notion
embodying the idea of a sequence in which each term is unpredictable to the uninitiated and whose digits pass a certain
number of tests, traditional with statisticians and depending somewhat on the uses to which the sequence is to be put."
10. Psuedo Random Number Generator: - In python, we have different (pseudo) random number generators -
"random" module
- a deterministic algorithm that produces a sequence of numbers that appears random but is completely predictable if the
initial "seed" value is known
11. Goal of random number generators: 1) examine the uniform random-number generators 2) generate normally
distributed random numbers
3) generate correlated random numbers using the Cholesky decomposition
12. Monte Carlo Limitations: - Often time-consuming, computationally wasteful
- Analytical methods (formulas) are better if available
13. 2 Reasons We May Still want Monte Carlo: 1) Other distribution
2) More complicated VaR problems
14. Random Walk Hypothesis: 1) Prices in financial markets follow a random walk, or in continuous time, an arithmetic
Brownian motion without drift
2) The best predictor for tomorrow's price, in a least squares sense, is today's price if RWH applies
15. Eugene Fama Quote about RWH: "For many years, economists, statisticians, and teachers of finance have been
interested in developing and testing models of stock price behavior. One important model that has evolved from this
research is the theory of random walks. This theory casts serious doubt on many other methods for describing and
predicting stock price behavior— methods that have considerable popularity outside the academic world. For
example, we shall see later that, if the random-walk theory is an accurate description of reality, then the various
, "technical" or "chartist" procedures for predicting stock prices are completely without value."
16. Forms of Efficient Market Hypothesis (EMH): The RWH is consistent with the efficient markets
hypothesis (EMH), which, non-technically speaking, states that market prices reflect "all available information." Several
forms of EMH:
1) Weak
2) Semi-strong
3) Strong
The form depends what "all available information" entails: "A market is efficient with respect to an information set S if
it is impossible to make economic profits by trading on the basis of information set S" (Michael Jensen (1978)) 17.
Weak Form EMH: All past market data
- Prices already reflect past prices, returns, volume, technical patterns.
- You cannot earn excess returns using technical analysis.
- But fundamental analysis might still help, because public news is not guaranteed to be priced in.
Key idea: You can't beat the market by looking at charts or historical price trends.
18. Semi-strong form EMH: All publicly available information
- Includes past price data plus all public news: earnings, filings, economic data, press releases, analyst reports, etc. -
Neither technical analysis nor fundamental analysis can consistently earn abnormal profits.
Key idea: The instant information becomes public, it's already reflected in the stock price.
19. Strong form EMH: All information (public + private) - Prices
even reflect inside information.
- No one can consistently earn excess returns, not even insiders.
