Reserve Option Mechanism : Does it Work as an Automatic Stabilizer?
Central Bank of the Republic of Turkey (CBRT) designed and implemented a new scheme since end-2011, called reserve options mechanism (ROM) in order to alleviate the adverse impact of capital flow volatility on the domestic economy. Although there are numerous studies on the mechanics of ROM, there has been no attempt to investigate the determinants of the ROM utilization in practice. In this paper, we aim to fill this gap by using bank-level data to assess the behavioral aspects of ROM. Our results suggest that the relative cost of Turkish lira funding to foreign currency funding as well as the reserve option coefficients set by the CBRT largely explains the variations in the ROM utilization. We find that the most relevant proxy for the cost of Turkish lira funding for banks is overnight money market interest rates and the weighted average cost of CBRT funding. Moreover, foreign currency liquidity does not seem to be a significant parameter in driving the utilization of ROM. We argue that the systematic policy induced movements in the short term domestic interest rates—higher during outflows, lower during inflows—may undermine the automatic stabilizer feature of ROM. And we propose an adjustment in the remuneration of reserve requirements to strengthen the automatic stabilizer effect of ROM.
RESERVE OPTIONS MECHANISM
Recently, Central Bank of the Republic of Turkey designed new policy instruments in order to reduce the adverse impact of volatile capital flows on macroeconomic and financial stability. This paper aims to introduce one of the new instruments: "Reserve Options Mechanism" (ROM). We describe the transmission channel of the ROM and compare it with alternative instruments. Our analysis concludes that ROM has the potential to be a useful policy tool for macroeconomic and financial stability.
Multi-layer inpainting on Chinese artwork
Digital inpainting uses spatial or frequency information to restore partially damaged/removed photos and artworks. Most restoration algorithms take a picture as a single layer. We propose a new approach, which subdivides a Chinese painting into several layers. Each layer is inpainted separately. The layer fusion mechanism then finds the optimal inpaint among layers, which are restored layer-bylayer. We apply the algorithm on Chinese and western drawing. The result shows a high PSNR value as well as a high user satisfaction. The demonstration of our work is available at: http://www.mine.tku.edu.tw/demos/inpaint.