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Estimating long-term volatility parameters for market-consistent models
Author(s) -
Emlyn Flint,
ER Ochse,
Daniel Polakow
Publication year - 2015
Publication title -
south african actuarial journal
Language(s) - English
Resource type - Journals
ISSN - 1680-2179
DOI - 10.4314/saaj.v14i1.2
Subject(s) - volatility (finance) , econometrics , volatility smile , implied volatility , economics , forward volatility , variance swap , volatility swap , valuation (finance) , stochastic volatility , sabr volatility model , market data , financial economics , accounting , finance
Contemporary actuarial and accounting practices (APN 110 in the South African context) require the use of market-consistent models for the valuation of embedded investment derivatives. These models have to be calibrated with accurate and up-to-date market data. Arguably, the most important variable in the valuation of embedded equity derivatives is implied volatility. However, accurate long-term volatility estimation is difficult because of a general lack of tradable, liquid medium- and long-term derivative instruments, be they exchange-traded or over the counter. In South Africa, given the relatively short-term nature of the local derivatives market, this is of particular concern. This paper attempts to address this concern by (1) providing a comprehensive, critical evaluation of the long-term volatility models most commonly used in practice, encompassing simple historical volatility estimation and econometric, deterministic and stochastic volatility models; and (2) introducing several fairly recent nonparametric alternative methods for estimating long-term volatility, namely break-even volatility and canonical option valuation.The authors apply these various models and methodologies to South African market data, thus providing practical, long-term volatility estimates under each modelling framework whilst accounting for real-world difficulties and constraints. In so doing, they identify those models and methodologies they consider to be most suited to long-term volatility estimation and propose best estimation practices within each identified area. Thus, while application is restricted to the South African market, the general discussion, as well as the suggestion of best practice, in each of the evaluated modelling areas remains relevant for all long-term volatility estimation.

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