Research | Eva Verschueren

Project 1

Tue, 03 Sep 2019 00:00:00 +0000

It Takes Two to Tango: Estimation of the Zero-Risk Premium Strike of a Call Option via Joint Physical and Pricing Density Modeling

It is generally said that out-of-the-money call options are expensive and one can ask the question from which moneyness level this is the case. Expensive actually means that the price one pays for the option is more than the discounted average payoff one receives. If so, the option bears a negative risk premium. The objective of this paper is to investigate the zero-risk premium moneyness level of a European call option, i.e., the strike where expectations on the option’s payoff in both the P- and Q-world are equal. To fully exploit the insights of the option market we deploy the Tilted Bilateral Gamma pricing model to jointly estimate the physical and pricing measure from option prices. We illustrate the proposed pricing strategy on the option surface of stock indices, assessing the stability and position of the zero-risk premium strike of a European call option. With small fluctuations around a slightly in-the-money level, on average, the zero-risk premium strike appears to follow a rather stable pattern over time.

Paper

Project 2

Mon, 02 Sep 2019 00:00:00 +0000

The Skin-in-the-game Bond: a Novel Sustainable Capital Instrument

We introduce a novel sustainable capital instrument: the skin-in-the-game bond. With features inspired by contingent convertibles (CoCos), this bond is an alternative for the green, social, sustainability and sustainability-linked bonds available on the market. A skin-in-the-game bond is linked to the performance of a benchmark that relates to the broad concept of sustainability in at least one of its pillars, being the environment (E), society (S) or corporate governance (G). When the benchmark hits a preset trigger level, (part of) the bond’s face value is withheld and directed into a government-controlled fund by the issuer. The skin-in-the-game bond offers a higher yield to investors than a standard corporate bond, in order to compensate for the risk of losing out on (part of) the investment. Both issuer and investor have skin-in-the-game; the embedded financial penalty incentivizes the preservation of a favorable benchmark value. In this presentation, we elaborate on the general concept of a skin-in-the-game bond, as well as on a tailored valuation model, illustrated by two examples: the ESG and nuclear skin-in-the-game bonds.

Working Paper

Project 3

Sun, 01 Sep 2019 00:00:00 +0000

On the Pricing of Capped Volatility Swaps using Machine Learning Techniques

A volatility swap is a forward contract on an asset's annualized, realized volatility, over a fixed period of time. At expiration, the payoff of the contract is given by

Notional x [min(CapLevel, RealizedVolatility)-VolatilityStrikePrice].

The cap level limits the risk exposure of the issuer of the contract and is most often fixed at 2.5 times the strike.

Volatility swaps are directly exposed to the volatility of the underlying asset, making volatility a tradable market instrument. For this reason, the contracts are nowadays popular tools in fund-based risk management and are used for both speculative and hedging purposes. Volatility swaps are traded over-the-counter, meaning that no price is readily available on exchange. However, fund managers can call upon external pricing entities to receive the current price of a specific contract. In reality, this price is often the end product of an unknown internal procedure. Moreover, occasionally, prices from different pricing sources differ substantially.

In this project, we show how we can deploy machine learning techniques to price capped volatility swaps. To this purpose, we build a unique dataset consisting of daily observed prices of traded volatility swaps over the lifetime of the contract, on individual stocks as well as stock indices. Before settlement of the contract, the unknown component within the payoff structure is the realized volatility, meaning that especially features with predictive power for this future realized volatility need to be taken into account. To this end, information from the implied moments on the underlying asset is exploited, to complement other features such as strike, (remaining) time till maturity and accrued volatility up to the valuation date.

Research project in collaboration with Assenagon.