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Volatility Workshop

Led by Bruno Dupire, Head Of Quantitative Research, Bloomberg L.P.

Monday 11 May 2020

Your 2020 workshop leader


Bruno Dupire 

Head Of Quantitative Research

Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the derivatives research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used local volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

Your 2020 workshop highlights

Overview

This workshop covers many practical aspects of volatility data, modelling, risk management, and trading.

It provides notably a step by step explanation of how to construct a volatility surface, how to implement a Local Volatility model and various extensions of it, how to price and manage variance swaps, how to exploit links between various volatility derivatives. 

It provides detailed examples of trading and risk management of popular exotic products.

Morning section

Fundamentals:

  • Historical volatility estimation and implied volatility calculation
  • How to construct a good implied volatility surface
  • How to compute a fair skew in the absence of options
  •  Market facts: volatility regimes, handling earnings


Volatility models:

  • Review of the most commonly used volatility models: black-scholes, local volatility model, heston model, SABR models, stochastic local volatility model, path dependent models, fractional volatility
  • Implementation of the local volatility model
  • Implementation of local stochastic volatility models
  • Machine learning to create data driven models
  • Case studies: barrier options, autocallables and accumulators
Afternoon section

Volatility derivatives:

  • Variance swaps, replication, practical issues
  • Volatility swaps
  • Cross corridor variance swaps
  • VIX: spot, futures, options and ETFs
  • options on realized variance

Volatility trading & arbitrage:

  • Volatility as an asset class
  • Frequency/phase arbitrage
  • Skew trades
  • Term structure of VIX arbitrage
  • Earning trades: 3 ways to play forward variance