Haynesville Consulting
The "Quant" World​
Quantitative Analytics ("Quant"), also known as Financial Mathematics, derives and extends the mathematical methods, economic frameworks and computational models to the competitive and volatile Finance industry.
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There are two separate Investment Banking areas that require advanced Quantitative Analytics:
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"Q" Quant: quantitative pricing using arbitrage-pricing probability
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"P" Quant: quantitative trading using the actual probability
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Haynesville Consulting was invited by the University of Oxford, U.K. to provide a public lecture on the field of Quantitative Analytics at the Investment Banking industry.
Quantitative Pricing:Â Extrapolate the Present
The aim of Quantitative Pricing is to determine the fair value of a financial product, such as vanilla and exotic options. Once a fair price has been determined, the liquidity provider can make a market on such financial product. Therefore, Quantitative Pricing is a complex extrapolation implementation to define the current market value of a financial product, which is quoted by the global Capital Market.
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Quantitative Pricing with fundamental contributions by Robert Merton by introducing the geometric Brownian motion to option pricing. This starts the study and development of the Black-Scholes-Merton Model. For this achievement, Myron Scholes and Robert Merton were awarded the 1997 Nobel Memorial Prize in Economic Sciences. The model is now widely used in volatility smile, bond options, and interest-rate curves, which are the key areas of Quantitative Pricing.
Quantitative Trading: Model the Future
Quantitative Trading and Asset Management aim at predicting the price movements and implementing trading signals at a given future investment horizon. Quantitative Trading is an extremely sophisticated area in the Investment Banking industry, and a quantitative trading system consists of four major components:
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Strategy Identification: investigating a trading idea, exploiting an edge and deciding on trading frequency
Strategy Backtesting: obtaining market data, analysing strategy performance and removing backtesting biases
Execution System: connecting to a trading platform, automating the trading and minimising transaction costs
Risk Management: monitoring market movement, optimising capital allocation and hedging trading risks
