Projects on The Insight Seekers

FIN_EF - TT01 - Statistics Refresher n Linear Regression Exercises

Thu, 25 Sep 2025 00:00:00 +0000

Part I (Probability)

Q1. Random Variables

a. Gender: random, no underlying covariate/biased introduced; b. No. crash: random, depending on unpredictable factors: hardware failures, software malfunction, etc.; c. Commute time: random, influenced by unpredictable variables: transport delays, traffic jams, accidents; d. Computer assignment: random, no obvious assignment process known; e. Rainfall: random due to randomness of atmostpheric system. Temperature, wind speed, humidity, and other factors may vary randomly.

Q4. Sample Mean

There is a thin chance that the sample average weight of four students is exactly equal to the class (population) mean. However, the sample mean is generally close to but not equal to the population average due to sample error.

FIN_EF - TT03 - Panel Data Model n Logit Regression Exercises

Thu, 25 Sep 2025 00:00:00 +0000

Logit Model Exercises

Question 1

a. Describe ROC Curve

ROC curves usually look like a bow reaching out to the top left corner of the chart. The more the curve bow out from the diagonal, the better the credit scoring model.

b. Estimate of the Probability of External Financing

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mdl2_zi <- \(def, ass, iag, p_fin){-0.72 + 0.02*def + 0.0003*ass - 0.002*iag + 0.79*p_fin}
mdl2_prob <- \(z){exp(z)/(1+exp(z))}
prob_firm1 <- mdl2_prob(mdl2_zi(1.10, 1.00, 0.00, 0.00))

The probability of external financing of Firm 1 using Model 2 is 33.23.

c. Marginal Effect Explanation

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mdl2_me_pf <- \(z){(exp(z)/(1+exp(z))^2)*0.79}
me_f1 <- mdl2_me_pf(mdl2_zi(1.10, 1.00, 0.00, 0.00))
me_f2 <- mdl2_me_pf(mdl2_zi(0.13, 1.00, 0.00, 0.50))

The marginal effect generally varies across observations because it depends not only on the estimated coefficients but also the evaluation point through \(z_i\), i.e. \(f(z_i)\). For instance, the marginal effects shrink at extreme probabilities as \(f(z_i)\) becomes smaller.

FIN_EF - TT04 - Panel Data, Endogeneity, and Diff-in-Diff Exercises

Thu, 25 Sep 2025 00:00:00 +0000

Panel Data, Endogeneity, and Diff-in-Diff Exercises

Question 4

a. Endogeneity Issue

The regression model omits a dummy variable of Industry (1=Creative, 0=Non-creative), i.e. \(\epsilon_{it} = c D_i^{CRE} + u_{it}\), which may create endogeneity problem if \(Cov(F_{it}, D_i^{CRE}) \neq 0\).

b. Sign of Biased Coefficient

Assume that \(F_{it}<0.5\) on average, then \(Cov(F_{it}, D_i^{CRE}) > 0\) and \(Cov(R_{it}, D_i^{CRE}) > 0\), leading to a positively biased estimate of \(b\), i.e. \(\hat b > b\).

S417 Financial Econometrics - Coca Cola Stock Volatility Analysis

Thu, 25 Sep 2025 00:00:00 +0000

Disclaimer: In this assignment, I have utilised Claude 4 Sonet in various aspects, including clarifying the expectations of the questions, facilitating my understanding of the addressed concepts, roxygen2-style document generation for helper functions, code debugging, and proof-reading.

Exercise 1 - Financial Data

a. Stylized Facts Analysis

i. Data Crawling & Prepocessing

if (!file.exists("Data/price_dt.rds")) {
  # Download Data
  stock_ls <- c("COST", "WMT", "KO", "PEP")
  price_dt <- tq_get(stock_ls,
                     get = "stock.prices",
                     from = "2000-01-01",
                     # to = as.character(Sys.Date() - 1)
                     to = "2025-08-09"
                     )
  # Save model data
  saveRDS(price_dt, file = "Data/price_dt.rds")  
} else {
  # Access saved stock data
  price_dt <- readRDS("Data/price_dt.rds")
}

# Prepare return data
return_all_dt <- prep_return_dt(price_dt)
 
# Extract different frequencies
daily_returns <- return_all_dt$daily
weekly_returns <- return_all_dt$weekly
monthly_returns <- return_all_dt$monthly

head(daily_returns)

## # A tibble: 6 × 9
##   symbol date       adjusted     ret grossret  logret    sqret absret  volume
##   <chr>  <date>        <dbl>   <dbl>    <dbl>   <dbl>    <dbl>  <dbl>   <dbl>
## 1 COST   2000-01-04     28.1 -0.0548    0.945 -0.0563 0.00317  0.0563 5722800
## 2 COST   2000-01-05     28.6  0.0171    1.02   0.0169 0.000287 0.0169 7726400
## 3 COST   2000-01-06     29.2  0.0201    1.02   0.0199 0.000396 0.0199 7221400
## 4 COST   2000-01-07     31.1  0.0662    1.07   0.0641 0.00411  0.0641 5164800
## 5 COST   2000-01-10     31.8  0.0208    1.02   0.0206 0.000425 0.0206 4454000
## 6 COST   2000-01-11     30.6 -0.0355    0.964 -0.0362 0.00131  0.0362 2955000

# Calculate 5% quantile
daily_q5_dt <- quantile(daily_returns |> pull(ret), probs = 0.05)
weekly_q5_dt <- quantile(weekly_returns |> pull(ret), probs = 0.05)
monthly_q5_dt <- quantile(monthly_returns |> pull(ret), probs = 0.05)

Compute summary statistics