Why do we care about Libor?

Background

The London Interbank Offered Rate (Libor) is the expected interest rate for leading banks in London to borrow money from other banks. Libor rates are calculated for five currencies and seven borrowing periods ranging from overnight to one year. It is considered to be the most important number in the financial world because at least $350 trillion in derivatives and other financial products are tied to it. (Article)

The Libor was invented by Minoz Zombanakis, who tried to let banks lend $80 million to the cash-strapped Iran in 1969. Banks hesitated to lend money at a fixed rate for long periods, because of the rising inflation in UK. Zombanakis offered a solution in which the interest rate would be recalculated every few months. He marketed the deal to a variety of local and foreign banks that could each take a slice, and the banks in the syndicate would report their funding costs just before a loan-rollover date. The weighted average plus a spread for profit became the price of the loan for the next period, and the rate was called London interbank offered rate (Article).

In 1984, the British Bankers’ Association (BBA) established the BBA standard for interest rate swaps including the fixing of BBA interest-settlement rate, which was later officially called BBA Libor in 1986.

C++ implementation of a simple order book

Please refer to my github for the code: https://github.com/DongliangLarryYi. 

1.     Data Structure

1.1  A basic order

Each order include ID, Side (buy or sell), Price, and Size.

//  Thanks to Daniel Cao who provided part of this code

#include <iostream>

#include <list>

#include <algorithm>

#include <iterator>

#include <fstream>

#define MAX_ID 100000 // at most 100000 orders

using namespace std;

// structure of an order which include id, side, price, size and related functions

class Order{

private:

    int id;

    char side;

    double price;

    int size;

Summary of some machine learning concepts and methods

Concepts

Cost function: It is used to measure the accuracy of a predictive model. It takes an average difference of all the results predicted by the model with inputs from x's (features or explanatory variables) and the actual output y's. (Week 1 of Andrew Ng’s class)

Regularization: It is added in the cost function to put some penalty on parameters of a model. It is used to prevent overfitting. (Week 3)

Gradient descent: It is a method to find the local minimum of a function with respect to some parameters. It is used in machine learning to find the best parameters in the model. (Week 1)

Life experience in physics and finance - book review of ‘My life as a quant’

Basic introduction of the book                

The author, Emanuel Derman, is a co-developer of the short rate model – Black-Derman-Toy model. He is also the director of Columbia’s Financial Engineering program. One special thing worth mentioning is that he switched his career from physics to finance at his 40’s.

I read this book with recommendation from a financial engineering program. It is said to be useful for me to understand and accelerate a career in quantitative finance.

The first half part of this book is about Emanuel’s life in physics, and it is not related to finance.

The second part is about his work and thoughts in wall street. The interesting part is that he had worked with many top quantitative finance researchers and practitioners, so the description of these talented guys (including author) is interesting for me to understand how they worked and thought on quantitative finance. 

My different stages in reading WSJ

Initial stage

I started to read WSJ with intention after the recommendation from a famous financial engineering program in US. It is said that reading WSJ is important for a graduate to find a financial job. Then I began to read articles on WSJ, but these articles were way long and took me half an hour to read. Some articles on the homepage seemed not interesting or related to me.  

Gradually as I spent more time on CFA preparation and solving technical problems in financial engineering, I read WSJ less than before. I skimmed headlines and only read interesting ones. As a result, my reading became more related to world news especially in East Asia which I am familiar with. Although I keep reading headlines on the homepage, I have less interest to read these unfamiliar and uninteresting articles.