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on Econometric Time Series |
| By: | Martin Magris |
| Abstract: | The heterogeneous autoregressive (HAR) model is revised by modeling the joint distribution of the four partial-volatility terms therein involved. Namely, today's, yesterday's, last week's and last month's volatility components. The joint distribution relies on a (C-) Vine copula construction, allowing to conveniently extract volatility forecasts based on the conditional expectation of today's volatility given its past terms. The proposed empirical application involves more than seven years of high-frequency transaction prices for ten stocks and evaluates the in-sample, out-of-sample and one-step-ahead forecast performance of our model for daily realized-kernel measures. The model proposed in this paper is shown to outperform the HAR counterpart under different models for marginal distributions, copula construction methods, and forecasting settings. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.08522&r=all |
| By: | Yilanci, Veli |
| Abstract: | This paper proposes a residual-based cointegration test in the presence of smooth structural changes approximated by a Fourier function. The test offers a simple way to accommodate unknown number and form of structural breaks and have good size and power properties in the presence of breaks. |
| Keywords: | cointegration test; Fourier function; structural breaks. |
| JEL: | C12 |
| Date: | 2019–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95395&r=all |
| By: | Mateusz Buczyński (Faculty of Economic Sciences, University of Warsaw); Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | Numerous advances in the modelling techniques of Value-at-Risk (VaR) have provided the financial institutions with a wide scope of market risk approaches. Yet it remains unknown which of the models should be used depending on the state of volatility. In this article we present the backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several most known VaR models, among many: GARCH, EVT, CAViaR and FHS with multiple sets of parameters. The backtesting procedure has been based on the excess ratio, Kupiec and Christoffersen tests for multiple thresholds and cost functions. The added value of this article is that we have compared the models in four different scenarios, with different states of volatility in training and testing samples. The results indicate that the best of the models that is the least affected by changes in the volatility is GARCH(1,1) with standardized student's t-distribution. Non-parmetric techniques (e.g. CAViaR with GARCH setup (see Engle and Manganelli, 2001) or FHS with skewed normal distribution) have very prominent results in testing periods with low volatility, but are relatively worse in the turbulent periods. We have also discussed an automatic method to setting a threshold of extreme distribution for EVT models, as well as several ensembling methods for VaR, among which minimum of best models has been proven to have very good results - in particular a minimum of GARCH(1,1) with standardized student's t-distribution and either EVT or CAViaR models. |
| Keywords: | Value-at-Risk, GARCH, Extreme Value Theory, Filtered Historical Simulation, CAViaR, market risk, forecast comparison |
| JEL: | G32 C52 C53 C58 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2019-12&r=all |