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Combining the Dynamic Conditional Correlation and Range-GARCH Models to Improve Covariance Forecasts
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<div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87412</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Combining the Dynamic Conditional Correlation and Range-GARCH Models to Improve Covariance Forecasts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Fiszeder">Piotr Fiszeder</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcin%20Fa%C5%82dzi%C5%84ski"> Marcin Fa艂dzi艅ski</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Moln%C3%A1r"> Peter Moln谩r</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model. <iframe src="https://publications.waset.org/abstracts/107388.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC%20model" title=" DCC model"> DCC model</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20and%20low%20prices" title=" high and low prices"> high and low prices</a>, <a href="https://publications.waset.org/abstracts/search?q=range-based%20models" title=" range-based models"> range-based models</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20forecasting" title=" covariance forecasting"> covariance forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107388/combining-the-dynamic-conditional-correlation-and-range-garch-models-to-improve-covariance-forecasts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107388.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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