‘Hot-start’ initialisation of the Heston model
Multi-factor models usually have hidden variables – for example, stochastic volatility – with unknown spot values. This suggests the initial value of such variables is actually random, with a distribution generated through stochastic evolution before formal modelling starts. Serguei Mechkov applies this approach to the Heston model and demonstrates its significant effect on the implied volatility smile
CLICK HERE TO VIEW THE PDF
A logical way of making a model more closely fit observed behaviour is to augment its dimensions by adding a hidden process that somehow affects the evolution of the principal observable value. The complete model specification then acquires the parameters of the hidden process and its possible correlation with the principal process. This also requires a description of how the hidden process starts, which is the main subject of this paper.
The very fact the
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Risk management
An AI-first approach to model risk management
Firms must define their AI risk appetite before trying to manage or model it, says Christophe Rougeaux
BofA sets its sights on US synthetic risk transfer market
New trading initiative has already notched at least three transactions
Op risk data: At Trafigura, a $1 billion miss in Mongolia
Also: Insurance cartels, Santander settlement and TSB’s “woeful” customer treatment. Data by ORX News
Cyber risk can be modelled like credit risk, says Richmond Fed
US supervisors may begin to use historical datasets to assess risk at banks and system-wide
The changing shape of risk
S&P Global Market Intelligence’s head of credit and risk solutions reveals how firms are adjusting their strategies and capabilities to embrace a more holistic view of risk
To liquidity and beyond: new funding strategies for UK pensions and insurance
Prompted by policy shifts and macro events, pension funds and insurance firms are seeking alternative solutions around funding and liquidity
More cleared repo sponsors join Eurex ahead of cross-margining
End of TLTROs for banks and pension fund search for liquidity management tools drives uptake
Reimagining model risk management: new tools and approaches for a new era
A collaborative report by Chartis and Evalueserve on how the use of automation can combat the growing complexity of managing model risk due to regulation and market volatility
