Measuring the Degree of Currency Misalignment Using Offshore Forward Exchange Rates: The Case of the Korean Financial Crisis

This paper proposes a new method of measuring the degree of currency misalignment through the use of offshore forward exchange rates. Using default risk adjusted no­arbitrage conditions for forward exchange contracts, we calculate the spot exchange rates and the domestic interest rates that are implied from the observed forward exchange rates. The difference between the implied and the observed spot exchange rates is our measure of currency misalignment. Our methodology is based on the presumption that, during a currency crisis, offshore forward exchange rates reflect market sentiments more closely than onshore spot and forward exchange rates. The latter are usually tightly regulated and heavily affected by government intervention during a non­normal event such as a financial crisis. We apply the method to the Korean financial crisis in 1997 and discuss its implication for evaluating the IMF adjustment program and explaining foreign capital flows.currency misalignment, covered interest parity, non­deriverable forwards, Korean financial crisis

BOCK : Bayesian Optimization with Cylindrical Kernels

A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.Comment: 10 pages, 5 figures, 5 tables, 1 algorith

The Stock Market, Profit and Investment

Should managers, when making investment decisions, follow the signals given by the stock market even if those do not coincide with their own assessments of fundamental value? This paper reviews the theoretical arguments and examines the empirical evidence, constructing and using a new US time series of data on the q ratio from 1900 to 1988. We decompose q - - the ratio of the market value of corporate capital to its replacement cost - - into the product of two terms, reflecting "fundamentals" and "valuation", the ratio of market value to fundamentals. We then examine the relation of investment to each of the two, using a number of alternative proxies for fundamentals. We interpret our results as pointing, strongly but not overwhelmingly, to a larger role of "fundamentals" than of "valuation" in investment decisions.

Social Impacts of the Asian Crisis: Policy Challenges and Lessons

human development, economic growth, globalization, inequality, poverty

Trends in Unemployment Rates in Korea: A Search-Matching Model Interpretation

We investigate the steady decline in aggregate unemployment rates in Korea since the 1960's. We argue that a pronounced decrease in the intensity of reallocation shocks, which resulted in a downward trend in the natural rate of unemployment, has been an important factor in this decline. Our claim is based on a structural search-matching model, the times series of job-separation and job-finding rates, and sectoral-shift measures that we construct from a micro data for the past three decades.Unemployment rates in Korea, Search, Reallocation shocks

Detecting the Scale and Resolution Effects in Remote Sensing and GIS.

This study examines the relationship between resolution and fractal dimensions of remotely sensed images. Based on the results of testing for the reliability of the algorithms on hypothetical surfaces, the isarithm algorithm is selected for determining the fractal dimensions of remotely sensed images. This algorithm is then applied to simulated fractal Brownian motion images and four calibrated airborne multispectral remotely sensed image data sets with different true and artificial resolutions for Puerto Rico. The results from applying the fractal method to images at different levels of resolution suggest that the higher the resolution of an image, the higher the fractal dimension of the image and the more complex the image surface. This relationship between resolution and fractal dimension is further verified by results from analysis employing the local variance method for the same data sets; where it is found that the higher the resolution, the higher the local variance or the more complex the image surface. The images with artificial resolutions were found to be unrealistic in simulating images with different resolutions because the aggregate method used in generating these images dose not exactly simulate the sensor\u27s response to resolution changes. The aggregate method has been widely used in image resampling and cautious use of this algorithm is suggested in future studies. The findings show that the fractal method is a useful tool in detecting the scale and resolution effects of remotely sensed images and in evaluating the trade-offs between data volume and data accuracy. More studies employing fractals and other spatial statistics to images with different artificial resolutions generated using better aggregation algorithms are needed in the future in order to further detect the scale and resolution effects in remote sensing and GIS