Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

Dionne, Georges; Duchesne, Pierre; Pacurar, Maria

doi:10.1016/j.jempfin.2009.05.005

Cited by 63 publications

(47 citation statements)

References 39 publications

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“…Furthermore, a Monte Carlo simulation has been shown to produce useful estimates of intraday VaR using tick-by-tick data (Dionne et al, 2009;Brooks and Persand, 2003).…”

Section: Introduction -Motivation and Review Of Literaturementioning

confidence: 99%

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Degiannakis

Floros

Dent

2013

International Review of Financial Analysis

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The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Additionally, the results suggest that underestimation of the true VaR figure becomes less prevalent as the forecasting horizon increases. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered for the 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Finally, the rolling-sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models. Hence, the parameters' time-variant characteristic cannot be entirely due to the news information arrival process of the market; a portion must be due to the FIGARCH modelling process itself.

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“…Furthermore, a Monte Carlo simulation has been shown to produce useful estimates of intraday VaR using tick-by-tick data (Dionne et al, 2009;Brooks and Persand, 2003).…”

Section: Introduction -Motivation and Review Of Literaturementioning

confidence: 99%

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Degiannakis

Floros

Dent

2013

International Review of Financial Analysis

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“…historical simulation), semiparametric approaches (e.g. extreme value theory) and Monte Carlo simulation (Dionne et al ., ). For example, Semenov () proposes a historical simulation technique which allows the accurate estimation of one‐day and 10‐day VaR figures conditional on the historical sensitivity of assets returns (within a portfolio) to various macroeconomic factors (risk factor betas) over a period of time.…”

Section: Introductionmentioning

confidence: 97%

A Monte Carlo Simulation Approach to Forecasting Multi‐period Value‐at‐Risk and Expected Shortfall Using the FIGARCH‐skT Specification

Degiannakis

Dent

Floros

2012

The Manchester School

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In financial literature, Value-at-Risk (VaR) and Expected Shortfall (ES) modelling is focused on producing 1-step ahead conditional variance forecasts. The present paper provides a methodological contribution to the multi-step VaR and ES forecasting through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a fractionally integrated GARCH framework for leptokurtic and asymmetrically distributed portfolio returns. Accounting for long memory within the conditional variance process with skewed Student-t (skT) conditionally distributed innovations, accurate 95% and 99% VaR and ES forecasts are calculated for multi-period time horizons. The results show that the FIGARCH-skT model has a superior multi-period VaR and ES forecasting performance.

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“…These studies have either used conventional ARCH-GARCH approaches or their variants. , Coroneo and Veredas(2006), Dionne et al(2009) and the most recently So and Xu (2013). This paper presents the first application (to the best of our knowledge) of the recently developed MC-GARCH model to forecast intraday VaR of ASX-50 stock exchange.…”

Section: Introductionmentioning

confidence: 98%

Intraday Volatility Forecast in Australian Equity Market

2013

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On the afternoon of May 6, 2010 Dow Jones Industrial Average (DJIA) plunged about 1000 points (about 9%) in a matter of minutes before rebounding almost as quickly. This was the biggest one day point decline on an intraday basis in the DJIA's history. An almost similar dramatic change in intraday volatility was observed on April 4, 2000 when DJIA dropped by 4.8%. These historical events present very compelling argument for the need of robust econometrics models which can forecast intraday asset volatility. There are numerous models available in the finance literature to model financial asset volatility. Various Autoregressive Conditional Heteroskedastic (ARCH) time series models are widely used for modelling daily (end of day) volatility of the financial assets. The family of basic GARCH models work well for modelling daily volatility but they are proven to be not as efficient for intraday volatility. The last two decades has seen some research augmenting the GARCH family of models to forecast intraday volatility, the Multiplicative Component GARCH (MCGARCH) model of Engle & Sokalska (2012) is the most recent of them. MCGARCH models the conditional variance as the multiplicative product of daily, diurnal, and stochastic intraday volatility of the financial asset. In this paper we use MCGARCH model to forecast intraday volatility of Australia's S&P/ASX-50 stock market, we also use the model to forecast the intraday Value at Risk. As the model requires a daily volatility component, we test a GARCH based estimate and a Realized Variance based estimate of daily volatility component.

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Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

Cited by 63 publications

References 39 publications

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

A Monte Carlo Simulation Approach to Forecasting Multi‐period Value‐at‐Risk and Expected Shortfall Using the FIGARCH‐skT Specification

Intraday Volatility Forecast in Australian Equity Market

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