Fall 2016 Computer Science 458 Lecture 8: 9/26/2016

Administrivia

Per piazza posting, Jieung Kim office hours: Monday and Wednesdays, 5:30 pm to 7:30pm. AKW 311.

Wizards of Odds Discussion of blackjack odds. May give you some insight into the problem in hw1.

We have the following guest speakers:

Monday, September 26. Dr. John Niccolai, Citadel. Dinner 6pm

      Fed Panfilov (fp252)
      Kenny Seals-Nutt (kes78)
      Nidal Hishmeh (nh298)
      Jack Siegel (js2985)
      Alternate:
      William Sun (wjs45)

Wednesday, September 28. Kayo Teramoto, Palantir. Dinner 7PM!

      Fan Gao (fg299)
      Yinfeng Zhang (yz683)
      Chengyang Liu (cl938)
      Vanessa Qiying Kuang(qk22)
      Alternates:
      Flora Qian Qiao (qq22)
      Leesa Shiyang Xu (sx69)
      Sean Lao (xl452)

Select students for dinners

Assignment 1 and new Assignment 2

See Assignments

Lecture

Modern Portfolio Theory

Vocabulary

Statistics

volatility

variance [ var() ]

standard deviation [ sd() ]

mean [ mean() ]

random variable

correlation coefficient [ cor() ]

covariance [ cov() ]

Finance

stock = equity

bond = debt = fixed income

IPO = initial public offering

options (buy = call, sell = put)

derivatives (options, futures, forwards, swaps, credit default swaps, swaptions, mortgage backed securities [MBS], collateralized debt obligations [CDO's], CDO-squared)

Black Scholes option pricing model

efficient frontier

portfolio

asset classes (equity, fixed income, real estate, private equity, venture capital, real estate, timber, jewelry store receivables, rock band royalties)

time horizon

risk-free rate

transaction costs

Capital Allocation Line (CAL) == Capital Market Line (CML)

market portfolio

unsystematic risk

systematic risk

Capital Asset Pricing Model (CAPM)

Security Market Line (SML)

liquidity premium

Sharpe Ratio

Value at Risk (VAR)

Alpha

arbitrage

liquidity

market capitalization (market cap)

large cap, mid cap, small cap

basis points, bps, "bips"

leverage buy out (LBO)

junk bonds == high yield bonds

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Multi Strategy Funds
G10
On-the-run / off-the-run
Basis point / bps / bips (beat the benchmark, haircut)
IRS interest rate swap market, e.g., mortgages
arbitrage
BRICs
EURUSD - exchange rates, FX. See http://www.x-rates.com/
Black-Scholes
Exchanges: CME
LIBOR - London Interbank Overnight Rate
Bonds: TIPS, TBA, On-the-run 10y
OTC - derivatives market
Transparency: Exchange > Securities < OTC
Sharpe Ratio
PnL - Profit and Loss
IRS swap has 200 fields
Notional Value
bullet / vanilla swap (Zero Present Value)
Fed funds rate is a step function
OIS - overnight index swap
Monte Carlo
[Black Swans]
CAD swaps dataq is unreliable
Capacity - can have a great strategy (Sharpe > 40) that does not scale.
Spectrum of automation: Macro < Model Informed < Model Driven < Algorithmic < HFT
Languages used at Citadel: C++, Python, C#, Excel.

Examples

Indices excercise data (2007): xls csv R Rplots.pdf
Which investment would you prefer? How could you create a diversified portfolio from these investments that has a better risk/return profile?

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