Intraday Value At Risk Research Articles

Parametric Quantile Autoregressive Conditional Duration Models With Application to Intraday Value‐at‐Risk Forecasting

ABSTRACTThe modeling of high‐frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians—above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time‐varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log‐symmetric distributions reparametrized by their quantile. The proposed quantile log‐symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in‐depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, we derive a semiparametric intraday value‐at‐risk (IVaR) model and then the proposed models are applied to two price duration data sets.

Intraday VaR: A copula-based approach

The availability of high-frequency trading data and developments in computing technology make it possible to evaluate Intraday Value-at-Risk (IVaR), a useful risk management tool for investors and regulators. In this study, we propose a new model to evaluate and predict the IVaR for stocks based on irregularly spaced intraday transaction data filtered by volume events. Our model obtains the joint distribution of volume durations and corresponding returns with a copula function, and IVaR predictions are generated via Monte Carlo simulations based on the estimated model. Backtesting results for 30 randomly selected Chinese stocks show that our proposed model outperforms a well-established alternative model in offering more precise IVaR over 30- and 60-min horizons. We also find that the mean of the IVaR results exhibits a W-shaped intraday pattern.

Intraday Risk Management of Cryptocurrency Returns During 2020–2021 Upsurge: A Conditional EVT Approach

The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s— t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.

Efficacy of GARCH-EVT Model in Intraday Risk Management: Evidence from Severely Pandemic-affected Countries

Countries around the world have experienced severe-to-moderate economic restrictions during the first two waves of COVID-19 pandemic. The present article captures the time frame of this unprecedented turmoil to test the efficacy of the conditional extreme value theory (EVT) model to forecast value at risk (VaR) and expected shortfall (ES). The article considers the eight most-affected countries and applies the generalized autoregressive conditional heteroskedasticity (GARCH) process in appropriate GARCH-EVT models assuming Student’s t distribution to accommodate the fat-tailed behaviour of financial return series. The study confirms that our model is substantially superior in forecasting intraday VaR and ES in comparison to other conditional EVT models with a classical assumption of normal distribution as well as an unconditional EVT model. Contrary to more commonly used 5-minute-frequency data in intraday risk management, we use hourly data, which is much less researched in the context of stock market volatility forecasting. Moreover, our study adds to the reliability of conditional EVT models because of its accuracy in predicting intraday risk during extreme economic uncertainty in severely pandemic-hit countries.

Idiosyncrasies of Intraday Risk in Emerging and Developed Markets: Efficacy of the MCS-GARCH Model and Extreme Value Theory

This article aims to study the effectiveness of the multi-step multiplicative component generalized autoregressive conditional heteroscedasticity (MCS-GARCH) model in forecasting intraday value at risk (VaR) and expected shortfall (ES) for both emerging and developed markets. The intraday VaR- and ES-forecasting performance of the MCS-GARCH model is compared with that of three other models. Five-minute returns data of stock market indices from four different countries are used in this study: India, China, the USA and the UK. The VaR and ES quantiles are estimated with the help of conditional extreme value theory (EVT) framework as well as with simple normal distribution assumption. The backtesting of VaR and ES suggest that the conditional EVT models provide superior forecasts compared to their GARCH counterparts. However, the accuracy of these models differs between emerging and developed markets. The multi-step MCS-GARCH or its EVT counterpart fails to outperform other competing models consistently, especially in emerging markets. The inability of the MCS-GARCH model in forecasting intraday risk accurately suggests that the multiple steps in the estimation of this model may lead to the errors-in-variables problem. The results of this study are unique in their suggestion that multi-step models may not be a better option in the estimation of VaR and ES.

Forecasting value at risk with intra-day return curves

Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods such as those based on realized volatility depend primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this study, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast value at risk (VaR). Our methods are based on the recently proposed functional generalized autoregressive conditionally heteroscedastic (GARCH) models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can be used to forecast intra-day VaR curves, which we considered and studied with companion backtests to evaluate the quality of these intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we forecast the one-day-ahead daily and intra-day VaR with the proposed methods and various benchmark models. The empirical results suggested that the functional GARCH models estimated based on the overnight cumulative intra-day return curves exhibited competitive performance with benchmark models for daily risk management, and they produced valid intra-day VaR curves.

Modeling and forecasting intraday VaR of an exchange rate portfolio

AbstractThe main task of this work was to predict, for the next 15 minutes, the value‐at‐risk (VaR) of an equally weighted portfolio composed of four exchange rates against the American dollar: Japanese yen, euro, Australian dollar and Swiss franc. The dataset consists of transaction prices of each asset recorded every 15 minutes, from January 7, 2013 to December 31, 2013. For each time series, the multiplicative‐component generalized autoregressive conditional heteroskedasticity model of Engle and Sokalska (Journal of Financial Econometrics, 2012, 10, 54–83) is fitted, and the dependence among the series is modeled by a D‐vine pair‐copula. VaR predictions are estimated based on simulated observations of the fitted model following the proposal of Berg and Aas (European Journal of Finance, 2009, 15, 639–659). The proposed method presents good results in terms of out‐of‐sample intraday VaR forecasting.

Intraday risk management in International stock markets: A conditional EVT approach

The study compares the predictive ability of various models in estimating intraday Value-at-Risk (VaR) and Expected Shortfall (ES) using high frequency share price index data from sixteen different countries across the world for a period of seven and half months from September 20, 2013 to May 07, 2014. The main emphasis of the study has been given to Extreme Value Theory (EVT) and to evaluate how well Conditional EVT model performs in modeling tails of distributions and in estimating and forecasting intraday VaR and ES measures. We have followed McNeil and Frey's (2000) two stage approach called Conditional EVT to estimate dynamic intraday VaR and ES. We have compared the accuracy of Conditional EVT approach to intraday VaR and ES estimation with other competing models. The best performing model is found to be the Conditional EVT in estimating both the quantiles for the entire sample. The study is useful for market participants (such as intraday traders and market makers) involved in frequent intraday trading in such equity markets.

Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach

We propose to compute the Intraday Value-at-Risk (IVaR) for stocks using real-time transaction data. Tick-by-tick data filtered by price duration are modeled using a two-state asymmetric autoregressive conditional duration (AACD) model, and the IVaR is calculated using Monte Carlo simulation based on the estimated AACD model. Backtesting results for the New York Stock Exchange (NYSE) show that the IVaR calculated using the AACD method outperforms those using the Dionne et al. (2009) and Giot (2005) methods.

Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1 % IVaR, while normal GARCH models excel for 2.5 and 5 % IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.

Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall

Abstract We estimate several GARCH- and Extreme Value Theory (EVT)-based models to forecast intraday Value-at-Risk (VaR) and Expected Shortfall (ES) for S&P 500 stock index futures returns for both long and short positions. Among the GARCH-based models we consider is the so-called Autoregressive Conditional Density (ARCD) model, which allows time-variation in higher-order conditional moments. ARCD model with time-varying conditional skewness parameter has the best in-sample fit among the GARCH-based models. The EVT-based model and the GARCH-based models which take conditional skewness and kurtosis (time-varying or otherwise) into account provide accurate VaR forecasts. ARCD model with time-varying conditional skewness parameter seems to provide the most accurate ES forecasts.

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

Abstract This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD–ARMA–EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.

The Role of Liquidity in Value at Risk - The Case of Hong Kong

This paper extends the conventional Value at Risk (VaR) to incorporate liquidity in intraday time horizon. Our rationale is that the Liquidity Adjusted Intraday VaR (L-VaR) is particularly relevant to day traders who are more interested in intraday market movement that could not be simply represented by a single VaR figure generated from interday data, and whose activities are done in volatile markets where being able to trade promptly is particularly important. Using intraday stock market data in Hong Kong, we show that liquidity can constitute on average 30% of the VaR for small-price stocks. Furthermore, it will be relatively less risky, both in terms of L-VaR and VaR, for day traders to trade in the middle of the trading days. Furthermore, given the importance of derivative securities nowadays, we conjecture that if liquidity is an important component in VaR for stocks, it will also assert significant impact of the derivatives counterpart. We therefore extend the study to highlight this importance by comparing the structure of the bid/ask spread of the Hong Kong options and stock market. We also provide explanation for wider spreads in option markets given different levels of moneyness and time to maturity.

Intraday Value at Risk (IVaR) Using Tick-by-Tick Data with Application to the Toronto Stock Exchange

The objective of this paper is to investigate the use of tick-by-tick data for market risk measurement. We propose an Intraday Value at Risk (IVaR) at different horizons based on irregularly time-spaced high-frequency data by using an intraday Monte Carlo simulation. An UHF-GARCH model extending the framework of Engle (2000) is used to specify the joint density of the marked-point process of durations and high-frequency returns. We apply our methodology to transaction data for the Royal Bank and the Placer Dome stocks traded on the Toronto Stock Exchange. Results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active on the market. The UHF-GARCH model performs well out-of-sample for almost all the time horizons and the confidence levels considered even when normality is assumed for the distribution of the error term, provided that intraday seasonality has been accounted for prior to the estimation.

O uso de dados de alta freqüência na estimação da volatilidade e do valor em risco para o IBOVESPA

Este artigo investiga o uso de dados de alta frequencia na estimacao das volatilidades diaria e intradiaria do IBOVESPA e no calculo do valor em risco (VaR). Os modelos GARCH e EGARCH sao usados em conjunto com metodos deterministicos de filtragem de sazonalidade para a previsao da volatilidade e do VaR intradiarios. Uma comparacao com o metodo nao-parametrico baseado no quantil empirico e efetuada. No calculo do VaR diario, dois metodos simples de previsao buscam captar a informacao de volatilidade contida nos dados de alta frequencia. O primeiro utiliza o desvio padrao amostral com janela movel e o segundo faz uso da tecnica de alisamento exponencial. Alguns metodos tradicionais aplicados a dados diarios sao usados para comparacao. No calculo do VaR diario, os dois metodos baseados em dados intradiarios apresentaram bom desempenho. No calculo do VaR intradiario, os resultados mostram que a filtragem do padrao sazonal e indispensavel a obtencao de medidas uteis de volatilidades com o uso dos modelos GARCH e EGARCH.