The Impact of Jump Distributions on the Implied Volatility of Variance

2014 ◽  
Author(s):  
Elisa Nicolato ◽  
Camilla Pisani ◽  
David Sloth
Keyword(s):  
Implied Volatility ◽  
The Impact

scholarly journals ANALYSIS OF MARKET VOLATILITY VIA A DYNAMICALLY PURIFIED OPTION PRICE PROCESS

2014 ◽  
Vol 09 (03) ◽  
pp. 1450006 ◽  
Author(s):  
CHUONG LUONG ◽  
NIKOLAI DOKUCHAEV
Keyword(s):  
Stock Price ◽  
Implied Volatility ◽  
Financial Time Series ◽  
Option Price ◽  
Option Prices ◽  
Price Process ◽  
Volatility Index ◽  
Quadratic Interpolation ◽  
Dynamic Estimation ◽  
The Impact

The paper studies methods of dynamic estimation of volatility for financial time series. We suggest to estimate the volatility as the implied volatility inferred from some artificial "dynamically purified" price process that in theory allows to eliminate the impact of the stock price movements. The complete elimination would be possible if the option prices were available for continuous sets of strike prices and expiration times. In practice, we have to use only finite sets of available prices. We discuss the construction of this process from the available option prices using different methods. In order to overcome the incompleteness of the available option prices, we suggests several interpolation approaches, including the first order Taylor series extrapolation and quadratic interpolation. We examine the potential of the implied volatility derived from this proposed process for forecasting of the future volatility, in comparison with the traditional implied volatility process such as the volatility index VIX.

The Impact of Jump Distributions on the Implied Volatility of Variance

2017 ◽  
Vol 8 (1) ◽  
pp. 28-53 ◽  
Author(s):  
E. Nicolato ◽  
C. Pisani ◽  
D. Sloth
Keyword(s):  
Implied Volatility ◽  
The Impact

The Impact of Macroeconomic Announcements on Implied Volatility

2011 ◽  
Author(s):  
Roland Füss ◽  
Ferdinand Mager ◽  
Holger Wohlenberg ◽  
Lu Zhao
Keyword(s):  
Implied Volatility ◽  
Macroeconomic Announcements ◽  
The Impact

scholarly journals Between ℙ and ℚ: The ℙℚ Measure for Pricing in Asset Liability Management

2018 ◽  
Vol 11 (4) ◽  
pp. 67 ◽  
Author(s):  
Marcel van Dijk ◽  
Cornelis de Graaf ◽  
Cornelis Oosterlee
Keyword(s):  
Option Pricing ◽  
Real World ◽  
Implied Volatility ◽  
Insurance Companies ◽  
Asset Liability Management ◽  
Pricing Models ◽  
Liability Management ◽  
Market Variables ◽  
Implied Volatility Surfaces ◽  
The Impact

Insurance companies issue guarantees that need to be valued according to the market expectations. By calibrating option pricing models to the available implied volatility surfaces, one deals with the so-called risk-neutral measure Q , which can be used to generate market consistent values for these guarantees. For asset liability management, insurers also need future values of these guarantees. Next to that, new regulations require insurance companies to value their positions on a one-year horizon. As the option prices at t = 1 are unknown, it is common practice to assume that the parameters of these option pricing models are constant, i.e., the calibrated parameters from time t = 0 are also used to value the guarantees at t = 1 . However, it is well-known that the parameters are not constant and may depend on the state of the market which evolves under the real-world measure P . In this paper, we propose improved regression models that, given a set of market variables such as the VIX index and risk-free interest rates, estimate the calibrated parameters. When the market variables are included in a real-world simulation, one is able to assess the calibrated parameters (and consequently the implied volatility surface) in line with the simulated state of the market. By performing a regression, we are able to predict out-of-sample implied volatility surfaces accurately. Moreover, the impact on the Solvency Capital Requirement has been evaluated for different points in time. The impact depends on the initial state of the market and may vary between −46% and +52%.

scholarly journals Brexit and its Impact on the Pound in the Foreign Exchange Market

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Keith Pilbeam
Keyword(s):  
Economic Policy ◽  
Foreign Exchange ◽  
Implied Volatility ◽  
Foreign Exchange Market ◽  
Policy Uncertainty ◽  
The European Union ◽  
Exchange Market ◽  
Soft Or ◽  
Economic Policy Uncertainty ◽  
The Impact

Abstract In this paper we outline the impact and likely future impact of Brexit on the pound. We argue that Brexit implies a significant depreciation of the pound and the degree of depreciation required is heavily linked to whether there will be a soft or hard Brexit. We find that the pound has had broadly similar depreciations to date against both the dollar and the euro. Brexit has considerably raised UK economic policy uncertainty and this, in turn, has at times led to an significant increase in future implied volatility of the pound. While there is an overall link between the state of the ongoing Brexit negotiations with the European Union and movements in the pound in the foreign exchange market, the link is not especially strong unless the perception that the negotiations are going badly has exceeded 60%.

Macroeconomic information and implied volatility: evidence from Australian index options

2014 ◽  
Vol 6 (1) ◽  
pp. 46-62 ◽  
Author(s):  
Hassan Tanha ◽  
Michael Dempsey ◽  
Terrence Hallahan
Keyword(s):  
Implied Volatility ◽  
Return Distribution ◽  
Index Options ◽  
Content Type ◽  
Market Participants ◽  
Macroeconomic Information ◽  
State Price ◽  
Macroeconomic Announcements ◽  
State Price Density ◽  
The Impact

Purpose – The purpose of this paper is to understand that option pricing is the response of option implied volatility (IV) to macroeconomic announcements. Design/methodology/approach – The authors use high-frequency data on ASX SPI 200 index options to examine the response of option IV, as well as higher moments of the underlying return distribution, to macroeconomic announcements. Additionally, the authors identify the response of the moments as a function of moneyness of the options. Findings – The findings suggest that in-the-money and out-of-the money options have difference characteristics in their responses, leading to the conclusion that heterogeneity in investor beliefs and preferences affect option IV through the state price density (SPD) function. Originality/value – The research contributes to the literature that examines whether IV captures the beliefs of market participants about the likelihood of future states together with the preferences of market participants towards these states. In particular, the authors relate changes in option IV to changes in macroeconomic announcements, through the impact of these announcements on the moments of the SPD function.

scholarly journals Stock Return Autocorrelation and Individual Equity Option Prices

2021 ◽  
Vol 9 (1) ◽  
pp. p51
Author(s):  
Fei Fang
Keyword(s):  
Stock Return ◽  
Implied Volatility ◽  
Option Price ◽  
Option Prices ◽  
Equity Options ◽  
First Order ◽  
Price Structure ◽  
Clear Link ◽  
Equity Option ◽  
The Impact

This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. The relation between stock return autocorrelation and option price structure is more pronounced when market is volatile, especially during financial crisis. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. Thus, stock return autocorrelation can help differentiate the price structure across individual equity options.

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