Exploring the research landscape of implied volatility index: A bibliometric analysis

• Published date: 01 January 2024
• Author: Shubham Jain,Suresh Kumar Mittal
• Date: 01 January 2024
• DOI: http://doi.org/10.1002/jcaf.22661

Received: 26 July 2 023 Revised: 5 September 2023 Accepted: 9 September 2023

DOI: 10.1002/jcaf.22661

REVIEW ARTICLE

Exploring the research landscape of implied volatility index:

A bibliometric analysis

Shubham Jain Suresh Kumar Mittal

Haryana School of Business, Guru

Jambheshwar University of Science and

Technology, Hisar, India

Correspondence

Shubham Jain, Haryana School of

Business, Guru Jambheshwar University

of Science and Technology,Hisar, India.

Email: shubhamjain.sj13@yahoo.com

Abstract

The purpose of this study is to present a comprehensive literature review on

Implied Volatility (IV) and its significance for investment decisions. The study

employs a combination of bibliometric information, qualitative synthesis, and

citation analysis to provide an overview of the current research on IV. Addition-

ally, the study maps the research field by identifying leading journals, authors,

research centers, and publications. The findings of the study highlight the impor-

tance of IV both theoretically and practically, and the fragmented nature of

existing knowledge. The comprehensive literaturereview conducted in this study

contributes to the existing body of knowledge on IV.The research limitations of

the study are due to the use of a single database for data extraction, which may

have resulted in missing some relevant articles. It is suggested that futurestudies

may use multiple databases to improve the comprehensiveness of the literature

review.

KEYWORDS

bibliometric analysis, citation analysis, content analysis, implied volatility, VIX

1 INTRODUCTION

Volatility driving from different industrial, economic, and

political factors provides opportunities for investors totake

investment decisions in financial markets. Understanding

and modelling the determining components of volatility is

crucial for financial market participants. Initially,volatility

in financial markets was measured by the variance of the

change in asset prices. It has also been estimated through

different conditional measures of volatility such as ARCH

and GARCH type models. Such models used historical

data to estimate current and future volatility, which pro-

vides little information about investors’ expectations for

future market volatility. It is considered as a major draw-

back of using these models (Ryu, 2012). Implied volatility

is a futuristic and relevant measure of volatilitythat reflects

the projections of the market for future price changes in a

security. Implied volatility is based on the option prices of

the underlying assets which show the investors’ perception

for the future market conditions for the underlying assets.

In 1993, Chicago board of option exchange introduced the

concept of volatility index, namely, VIX as a measure of

investor’s sentiment for the American market benchmark

index S&P-100. VIX is a benchmark index for the short

term American market volatility which utilizes the option

prices of S&P-100 index implied volatilities based on Black

and Scholes (1973) option pricing model. However,in 2003,

the CBOE modified its computations and introduced a

new VIX which is based on the S&P-500 index. The new

volatility index is different from old index because earlier

is based only on eight at-the-money call and put option

prices whereas the later also utilized the out-of-the-money

option prices. CBOE VIX is recognized as an investor senti-

ment barometer,commonly referred to as the ‘investor fear

gauge,’ and its levels indicate market uncertainty (Wha-

ley, 2000). The US VIX is not the only indicator of market

J Corp Account Finance. 2024;35:325–336. © 2023 Wiley PeriodicalsLLC. 325wileyonlinelibrary.com/journal/jcaf