finance

Non-Parametrics Chris Hansman Empirical Finance: Methods and Applications Imperial College Business School March 1-2, 2021 1/46 Non-Parametrics 1. Kernel Density Estimation 2. Non-Parametric Regression 2/46 Kernel Density Estimation 1. Parametric vs. non-parametric approaches 2. Histograms and the uniform kernel 3. Different bandwidths 4. Different kernels 3/46 Estimating Densities 􏰒 Suppose we see n=100 draws from […]

# Tutorial Week 7 # Davide Benedetti # 27/02/2021 library(tidyverse) library(tidyquant) tickers % mutate(return = log(adjusted/lag(adjusted)) * 252 ) %>% # select only columns ‘symbol’, ‘date’ and ‘return’ select(symbol, date, return) %>% # remove rows with NaN’s na.omit() %>% # unstack the stocks as column variables spread(symbol, return) %>% # remove columns with too many

# Empirical Finance Lecture 4 Analysis (Part 1) # Author: Chris Hansman # Email: chansman@imperial.ac.uk # Date : 28/01/21 # Loading Libraries library(tidyverse) # Reading Data poly

Assignment 1 Empirical Finance: Methods and Applications January 25, 2021 • Datasets for problems 4, 5, and 6 are available on insendi. • You should submit a single pdf solution containing answers to all sub-parts of all problems (including 4-7). Typewritten solutions are preferred but handwritten and scanned solutions are acceptable. • Marks for each

# Assignment 1 Code — Empirical Finance # Author: Chris Hansman # Email; chansman@imperial.ac.uk # Date: 09/02/21 # install.packages(“tidyverse”) # install.packages(“glmnet”) # install.packages(“glmnetUtils”) library(tidyverse) library(readxl) library(lubridate) library(glmnet) library(glmnetUtils) library(stargazer) #Problem 4 # Cleaning Data msales% summarise(n_price=mean(SALE_PRICE)) %>% arrange(n_price) # Part b msales %>% lm(log_price ~ age + NEIGHBORHOOD, data=.) %>% stargazer(type = “text”, digits =

# Lecture 5 Analysis # Author: Chris Hansman # Email: chansman@imperial.ac.uk # Date : 06/02/21 # Libraries library(tidyverse) library(broom) # Loading Returns all_returns % filter(term == “mkt_returns”) %>% select(-term) #A similar approach to recover Residual SD by Group residual_sd % nest(-asset) %>% mutate(fit = map(data, ~ lm(returns ~ mkt_returns, data = .)), results = map(fit,

Assignment 2 Empirical Finance: Methods and Applications February 22, 2021 • You should submit a single pdf solution containing answers to all sub-parts of all problems (including 4-5). Typewritten solutions are preferred but handwritten and scanned solutions are acceptable. • Marks for each problem are listed below. Within each problem sub-parts are equally weighted. •

Assignment 1 Empirical Finance: Methods and Applications February 13, 2021 • Datasets for problems 4, 5, and 6 are available on insendi. • You should submit a single pdf solution containing answers to all sub-parts of all problems (including 4-7). Typewritten solutions are preferred but handwritten and scanned solutions are acceptable. • Marks for each

Candidate Name: CID Number: MSc Risk Management and Financial Engineering Examinations 2018/2019 For internal Students of Imperial College of Science Technology and Medicine. This paper also forms part of the examination for the Associateship. Empirical Finance: Methods and Applications (B (BS1033) Tuesday 12th March; 14:00-16:00 CLOSED BOOK Instructions Only college approved calculators may be used.

# Empirical Finance Lecture 4 Analysis (Part 2) # Author: Chris Hansman # Email: chansman@imperial.ac.uk # Date : 28/01/21 # Installing Packages #install.packages(“glmnet”) #install.packages(“glmnetUtils”) #Loading Libraries library(glmnet) library(glmnetUtils) library(tidyverse) set.seed(050187) #————————————————–# # Loading Data #————————————————- # Training with 100 Observations as a Tibble train_100