To be a Quant

2023-08-13 00:00:00 +0000

Preparing to be a quant is an exciting journey. There is no only one path. For sure you’re going to be involved in different topics such as: scientific method, finance, quantitative finance, behavioral economics, statistics, machinelearning, and technology. Personally that’s why this field is so exciting.

Managing all this is no easy task, mainly if you are not familiarized with mathematics. Some people have asked me what is the best starting point for that. My answer is usually the same, it dependes (remember I’m an economist as well :#). There are different types of quant: front office quant, model validation quant, research quant, quantitative developer, statistical arbitrage quant, quant portfolio manager, and quantitative risk. I’m almost sure will be others in a few years’ time.

I’ve been working as front office quant, quantitative risk analyst (market risk), model validation quant, and in my free time quantitative trader/quantitative portfolio manager of my own portfolio (soon I’ll share my personal score in other post). In all of them there is shared knowledge but of course specialized knowledge.

• For instance as a quantitative risk analyst you need to keep an eye on what is happening in the portfolios in a daily basis. It includes to assess your models in order to be sure the models is appropriate for the market’s behaviour, calculate measures of risks (such as value at risk, expected shortfall, asset haircuts, and so on.), input data analysis, internal asset’s development (curves, tools, models). You have noticed you must be quite familiarized with basic models and its assumptions. Additionaly, and depending the size of the company you will be working on building the tools other people is going to use to keep under control the risk of the portfolios. It means to code the tools. Some relevant things you’ll learn in this kind of job is market risk metrics, and how to use them, such as VaR (Value at Risk), CVar/ES (Conditional Value at Risk or Expected Shorfall), haircut, CCR (Counterparty Credit Risk), a lot of how to price and to use pricing formulas specially on derivatives and structured products, and for sure many other ones. A drawback is that the role may turns monotonous when the company is not able to automate recurrent processes.

• As a front quantitative analyst you are able to help traders in different things. Since helping to assess if an strategy making sense for any portfolio (considering the portfolio’s mandate) until to help them to confirm risk measures with risk management limits and controlling the into algorithmic trading. In between there are many exciting this to do. Just imagin you are in charge of build a model to help hedging a portfolio. To me the most exciting one is to build algorithms for trading. It implies to consider algorithmis for execution and for asset allocation. As you know, the most of the inputs in this kind of algorithms are time series, so to know about methodologies on this subject is quite important. Just be aware that you as a front quantitative analyst may be an influence for trading decisicions then a wrong model may lead to losses in your money desk. Nobody wants to be responsible for losses in any portfolio. You’ll be getting into in trading vocabulary such as order types, trading venues, algorithmic trading, OMS (Order Management Systems), communication protocols (FIX, OUCH, SAIL, iLINK, and others), portfolio mandate, trading book, and so on. A drawback is that when the internal culture with traders is not mature the traders may consider this role as the role for doing the boring stuff, thus the professional is going to be a lot administrative things … and that is not the goal.

• As a quantitative trader you will produce the halo effect, until you screw up the first time. As some years ago to be a trader is synonimus of highly paid professionals. Nowadays it keeps being true, however is turning the profession to a more technical one. Same as some years ago to have the title of a trader doesn’t mean you are profitable. It happens as well as a quantitative trader. You’ll be involved in building profitable models, many times reading and understanding the market data in real time. Thus, I’d say the most important skill here is to capture patterns, but more important is to capture patterns that may keep through time (that’s the challenge here). YOu’ll be involved in many statistical models to correctly assess relationships in financial, economic and alternative data. In addition you need to be aware the different strategies in the market to apply the correct one, for instance be able to identify when it is better send an limit order, or how to set up TP (Take Profit) and SL (Stop Loss) levels. Technical analysis is likely one of the most used tools in this field. The drawback is that you may be frustrated when you don’t find a successful strategy which took time to develop.

• Last but not least, as a quantitative model validator your task is to assess the soundness of the models used for other areas. The most common one is quantitative model validator for risk. It implies to assess the soundness on risk models for calculating risk metrics (regulatory and internal ones). There are a huge amount of risk metrics for each type of risk (market, credit, liquidiy, counterparty, operational, and so on). However, the common among them is that those are intensive in statistical methods. The most exciting part of this role is that you are going to learn a lot about models. You’re not only assessing the models but also learning how developers and first line of defense built them. Some times it is plaged of regulation in the banking industry, and honestly for a quant that is not appealing.

All of those positions have had in common the use of mathematics, statistics, code development, and quantitative finance. At the same time each one has specialized knowledge. So let me list some relevant topics for became a quant.

• Set theory and probability
  • Conditional probability
  • Common probability distributions (Normal, t, Chi, Log-Normal,..)
• Fundamentals of statistics
  • Mean, variance, statistical momentums
  • Hypothesis tests
  • Copulas
• Introduction to algorithms
• Pricing theory
  • Bonds
  • Futures / Forwards
  • Options
  • Swaps
• Risk Management
  • Types of Risk (Market, Credit, Liquidity, so on.)
  • Value at Risk (VaR) : Historical simulation, Parametric (Mean-Variance), Monte Carlo Simulation
  • Expected Shorfall / Conditional Value at Risk (ES / CVaR)
  • Sensitivities
  • Loss Given Default (LGD)
  • Probability of Default (PD)
  • Exposure at Default (EAD)
  • Basel models (I, II, III), and IV soon.
  • Counterparty Clearing House
• Portfolio theory
  • Modern Portfolio Theory (MPT)
  • Capital Asset Pricing Model (CAPM)
  • Arbitrage Pricing Theory (APT)
  • Black-Litterman model
• Quantitative Finance
  • How to build interest rate curves (Nelson-Siegel, Bootstraping, ..)
  • Numerical Methods
  • Stochastic calculus
  • Arbitrage Definition
  • Portfolio Hedging
• Fundamentals on machine learning
• Fundamentals of trading
  • Type of Orders (Limit, Stop-Limit, Market, …)
  • What is trading venue
  • Dark Pools
  • OTC vs Exchange trading

The list is more extensive than this one. I think this one is a good starting point. ENJOY!!