Parameter Uncertainty with Focus on Recovery
organized by
Alex Weissensteiner, Free University of Bozen-Bolzano, Italy
venue: Free University of Bolzano - Room E4.23
date: Nov 30, 2018
map: see below
Speakers
Jörgen Blomvall - Linköping University
Christian Skow Jensen - Copenhagen Business School
Marco Menner - University of Konstanz
Carlo Sala - ESADE Business School
Paul Schneider - University of Lugano
Thursday, Nov 29 come together
19:00 meeting in the lobby of Hotel Luna Mondschein
19:30 dinner at Torgglhaus
Friday, Nov 30 scientific program
09:00 - 09:15 Welcome speech by Oswin Maurer (dean of the faculty of economics)\
09:15 - 10:15 Recovery of the physical density from option prices (Jörgen Blomvall)
10:15 - 10:45 coffee break
10:45 - 11:45 Generalized Recovery (Christian Skov Jensen, David Lando, Lasse Heje Pedersen)
12:00 - 14:00 lunch break - Franziskanerstube
14:00 - 15:00 The Ross Recovery Stochastic Discount Factor (Marco Menner)
15:00 - 16:00 The impact of misalignment of beliefs on the estimation of the pricing kernel (Giovanni Barone-Adesi, Antonietta Mira, Carlo Sala)
16:00 - 16:30 coffee break
16:30 - 17:30 Scenario Generation Under Ambiguity (Paul Schneider, Fabio Trojani)
19:30 dinner Vögele
Abstract of invited talks:
The impact of misalignment of beliefs on the estimation of the pricing kernel (SSRN)
Giovanni Barone-Adesi, Antonietta Mira, Carlo Sala
Estimating the markets beliefs about future returns by means of backward-looking historical data leads to an uninformative and hence unconditional physical measure. What is missing are the investors forward-looking beliefs, which are instead naturally captured by an option-based risk-neutral measure. The information gap between the two measures leads to an information premium and, accordingly with the theory, the two measures are therefore not comparable. This paper studies theoretically and empirically the impact of this misalignment of beliefs on the estimation of the pricing kernel and its connection with the pricing kernel puzzle. To study the misalignment, we propose a stock-and-option-based physical measure estimation. Starting from the classical approach, which relies on historical data only, the proposed measure also exploits the information coming from the daily option cross-section. As a natural test, the proposed measure is used to extensively investigate the shape of the 2002-2015 S&P 500 pricing kernel.
Recovery of the physical density from option prices
Jörgen Blomvall
As e.g. Barkhagen, Blomvall and Platen (2016) show, the physical pdf of S&P500 returns can be recovered from the risk-neutral pdf. This talk will discuss how the recovery was made, and cover challenges to improve the quality of the physical pdf. Components which will be covered include measurement of the risk-neutral pdf, recovery of the physical pdf and how supply/demand and other aspects impact the recovery.
The Ross Recovery Stochastic Discount Factor
Marco Menner
This paper explores the recovered stochastic discount factor (SDF) based on Ross (2015) from an empirical perspective. The recovered SDF is not able to price the S&P 500 and the risk-free asset and deviates significantly from alternative SDFs that can price both assets. The theoretical literature showed that the Ross recovery SDF fails to identify a crucial, permanent SDF component and equals the residual transitory SDF component. This paper finds that the empirical Ross recovery SDF does not resemble the empirical transitory SDF component. It also demonstrates the virtual risk-neutrality of recovered SDFs once reasonable economic constraints were added.
Generalized Recovery (SSRN)
Christian Skov Jensen, David Lando, Lasse Heje Pedersen
We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. We make no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015). Recovery is feasible when the number of maturities with observable prices is higher than the number of states of the economy (or the number of parameters characterizing the pricing kernel). When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return and other recovered statistics.
Scenario Generation Under Ambiguity
Paul Schneider, Fabio Trojani
We develop a low-dimensional scenario model to describe univariate intertemporal martingale distributions. We cope with the ambiguity induced from incomplete option markets as an alpha-maxmin decision maker. Scenarios are formulated in terms of a representative time-varying discrete probability distribution for which no estimation error is involved and computation is instant. An application using S&P 500 options shows that a 4-atomic discrete probability measure performs on par with a multivariate stochastic volatility model with jumps.
Participation is free of charge - but please register by sending an email to: alex.weissensteiner@unibz.it
Arrival via train: both the university and the hotel are within walking distance