Evaluating the RiskMetrics methodology in measuring volatility and Value-at-Risk in financial markets

Szilárd Pafka, I. Kondor

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was commonly found that RiskMetrics performs satisfactorily well, and therefore the technique has become widely used in the financial industry. We find, however, that the success of RiskMetrics is the artifact of the choice of the risk measure. First, the outstanding performance of volatility estimates is basically due to the choice of a very short (one-period ahead) forecasting horizon. Second, the satisfactory performance in obtaining Value-at-Risk by simply multiplying volatility with a constant factor is mainly due to the choice of the particular significance level.

Original languageEnglish
Pages (from-to)305-310
Number of pages6
JournalPhysica A: Statistical Mechanics and its Applications
Volume299
Issue number1-2
DOIs
Publication statusPublished - Oct 1 2001

Fingerprint

Value at Risk
volatility
Financial Markets
Volatility
methodology
Methodology
Fat Tails
Financial Data
Risk Measures
Significance level
fats
forecasting
horizon
artifacts
Forecasting
Horizon
Distribution Function
distribution functions
industries
Industry

Keywords

  • Market risk
  • Risk measurement
  • RiskMetrics
  • Value-at-Risk
  • Volatility

ASJC Scopus subject areas

  • Mathematical Physics
  • Statistical and Nonlinear Physics

Cite this

Evaluating the RiskMetrics methodology in measuring volatility and Value-at-Risk in financial markets. / Pafka, Szilárd; Kondor, I.

In: Physica A: Statistical Mechanics and its Applications, Vol. 299, No. 1-2, 01.10.2001, p. 305-310.

Research output: Contribution to journalArticle

@article{9f4d6daeb086423ebeb2fe457a2587b8,
title = "Evaluating the RiskMetrics methodology in measuring volatility and Value-at-Risk in financial markets",
abstract = "We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was commonly found that RiskMetrics performs satisfactorily well, and therefore the technique has become widely used in the financial industry. We find, however, that the success of RiskMetrics is the artifact of the choice of the risk measure. First, the outstanding performance of volatility estimates is basically due to the choice of a very short (one-period ahead) forecasting horizon. Second, the satisfactory performance in obtaining Value-at-Risk by simply multiplying volatility with a constant factor is mainly due to the choice of the particular significance level.",
keywords = "Market risk, Risk measurement, RiskMetrics, Value-at-Risk, Volatility",
author = "Szil{\'a}rd Pafka and I. Kondor",
year = "2001",
month = "10",
day = "1",
doi = "10.1016/S0378-4371(01)00310-7",
language = "English",
volume = "299",
pages = "305--310",
journal = "Physica A: Statistical Mechanics and its Applications",
issn = "0378-4371",
publisher = "Elsevier",
number = "1-2",

}

TY - JOUR

T1 - Evaluating the RiskMetrics methodology in measuring volatility and Value-at-Risk in financial markets

AU - Pafka, Szilárd

AU - Kondor, I.

PY - 2001/10/1

Y1 - 2001/10/1

N2 - We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was commonly found that RiskMetrics performs satisfactorily well, and therefore the technique has become widely used in the financial industry. We find, however, that the success of RiskMetrics is the artifact of the choice of the risk measure. First, the outstanding performance of volatility estimates is basically due to the choice of a very short (one-period ahead) forecasting horizon. Second, the satisfactory performance in obtaining Value-at-Risk by simply multiplying volatility with a constant factor is mainly due to the choice of the particular significance level.

AB - We analyze the performance of RiskMetrics, a widely used methodology for measuring market risk. Based on the assumption of normally distributed returns, the RiskMetrics model completely ignores the presence of fat tails in the distribution function, which is an important feature of financial data. Nevertheless, it was commonly found that RiskMetrics performs satisfactorily well, and therefore the technique has become widely used in the financial industry. We find, however, that the success of RiskMetrics is the artifact of the choice of the risk measure. First, the outstanding performance of volatility estimates is basically due to the choice of a very short (one-period ahead) forecasting horizon. Second, the satisfactory performance in obtaining Value-at-Risk by simply multiplying volatility with a constant factor is mainly due to the choice of the particular significance level.

KW - Market risk

KW - Risk measurement

KW - RiskMetrics

KW - Value-at-Risk

KW - Volatility

UR - http://www.scopus.com/inward/record.url?scp=0035471435&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035471435&partnerID=8YFLogxK

U2 - 10.1016/S0378-4371(01)00310-7

DO - 10.1016/S0378-4371(01)00310-7

M3 - Article

AN - SCOPUS:0035471435

VL - 299

SP - 305

EP - 310

JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

IS - 1-2

ER -