Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility

We use realized volatility to study the influence of central bank interventions on the yen/dollar exchange rate. Realized volatility is a technical innovation that allows specifying a system of equations for returns, realized volatility, and interventions without endogeneity bias. We find that during the period 1995 through 1999, interventions of the Japanese monetary authorities did not have the desired effect with respect to the exchange rate level and we measure an increase in volatility associated with interventions. During the period 1999 through 2004, the estimations are consistent with successful interventions, both in depreciating the yen and in reducing exchange rate volatility.

Peningkatan Kemampuan Guru Biologi dalam Menyusun RPP dan Melaksanakan Supervisi Klinis pada SMA Binaan Kabupaten Belu, Provinsi Nusa Tenggara Timur

Supervisi klinis ternyata telah meningkatkan kemampuan guru untuk menyusun RPP dan melaksanakan  pembelajaran menjadi lebih baik. Itu berarti bahwa supervisi klinis dan pembinaannya sangat penting untuk dilaksanakan. Berdasarkan data pada siklus 1 dari unsur RPP, ada 2 sekolah kategori B dan 3 sekolah kategori C dengan rata-rata 70. 25 sedangkan pada unsur pelaksanaan pembelajaran ada sekolah kategori B dan 4 sekolah kategori C dengan rata-rata 67.81. Pada siklus 2 pada unsur RPP, 5 sekolah berkategori B dengan rata-rata 76.75 pada unsur pelaksanaan pembelajaran, 1 sekolah kategori A dan 4 sekolah B dengan rata-rata 76.23. KKM pada siklus 1, peserta didik yang telah memenuhi KKM= 159  (78%), sedangkan yang belum tuntas= 45  (22%). Pada siklus 2, ada 201  (98, 5%) peserta sudah tuntas dan 3  (1,5%) dari 204 belum memenuhi KKM. Karena itu, guru hendaknya meningkatkan kemampuan pengelolaan pembelajaran biologi dan kepala sekolah dan atau pengawas hendaknya selalu mengadakan supervisi klinis dengan pembinaannya secara periodik

Forecasting Stock Prices via Deep Learning During COVID-19: A Case Study from an Emerging Economy

In this study we apply a Deep Learning Technique to predict stock prices for the 30 stocks that compose the BIST30, Turkish Stock Market Index before and after the onset of Covid-19 crises. Specifically, we utilize the Bi-Directional Long-Short Term Memory (BiLSTM) model which is a variation of the Long-Short-Term Memory (LSTM) model to predict stock prices for the BIST30 stocks. We compare the performance of the model to other commonly used machine learning models like decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), artificial neural networks (ANN), and other deep Leaning models like recurrent neural network (RNN), and the Long-Short-Term Memory (LSTM) model. The BiLSTM model seems to have better performance compared to conventional models used for predicting stock prices and continues to have superior performance in the Covid19 period. The LSTM model seems to have a good overall performance and is the next best model.&nbsp