| Summary: | <p class="IsiAbstrakIndo"><span lang="IN">M</span><span lang="EN-GB">odel volatilitas <em>Autoregressive Conditional Heteroscedasticity</em></span><span lang="IN"> (ARCH)</span><em><span lang="EN-GB">lag</span></em><span lang="EN-GB"> 1, dimana </span><em><span lang="IN">return error </span></em><span lang="IN">berdistribusi <em>skewed</em></span><em><span lang="EN-GB"> Student-t</span></em><span lang="IN">,</span><span lang="EN-GB"> diaplikasikan untuk runtun waktu <em>return</em> kurs beli harian <em>Euro</em> (EUR) dan <em>Japanese Yen</em> (JPY) terhadap <em>Indonesian Rupiah</em> (IDR) dari Januari 2009 sampai Desember 2014. </span><span lang="IN">Metode<em> indepence chain Metropolis</em></span><em><span lang="IN">-</span></em><em><span lang="IN">Hastings</span></em><span lang="IN"> (IC-MH) yang efisien dibangun dalam algoritma</span><em><span lang="EN-GB">Markov Chain Monte Carlo</span></em><span lang="EN-GB"> (MCMC) untuk memperbarui nilai-nilai parameter dalam model yang tidak bisa dibangkitkan secara langsung dari distribusi <em>posterior</em>. </span><span lang="IN">Meskipun</span><span lang="EN-GB"> 95% interval <em>highest posterior density</em></span><span lang="IN"> dari parameter <em>skewness </em>memuat nol untuk semua data pengamatan, tetapi sebagian besar distribusi <em>posteriornya</em> berada di daerah negatif</span><span lang="EN-GB">, yang mengindikasikan dukungan terhadap distribusi</span><em><span lang="IN">skewed </span></em><span lang="IN">Student<em>-t</em>. Selain itu diperoleh nilai derajat kebebasan disekitar 15 dan 18, yang mengindikasikan dukungan terhadap <em>heavy</em></span><em><span lang="IN">-</span></em><em><span lang="IN">tailedness</span></em><span lang="IN">.</span></p><p class="BasicParagraph"><em>Autoregressive Conditional Heteroscedasticity (ARCH) volatility model of lag 1, where return error has a skewed Student-t distribution, for the buying rate Euro (EUR) and Japanese Yen (JPY) to Indonesian Rupiah (IDR) from January 2009 to December 2014,. An efficient independence chain Metropolis</em><em>-</em><em>Hastings (IC-MH) method is developed in an algorithm Markov Chain Monte Carlo (MCMC) to update the parameters of the model that could not be sampled directly from their posterior distributions. Although 95% highest posterior density interval from skewness parameter contains zero for all the data, most of the posterior distribution located in the negative area, indicating support for the skewed Student-t distribution into the return error. Furthermore the value of degrees of freedom is found around 15 and 18, indicating support for the heavy-tailedness.</em><em></em></p><p class="IsiAbstrakIndo"><span lang="IN"><br /></span></p>
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