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    Robust estimation of term structure and implied volatility in emerging markets
    (2016-07) Ahi, Emrah; Şener, Emrah; Şener, Emrah; Department of Business; Ahi, Emrah
    Despite powerful advances in interest rate curve modeling for data-rich countries in the last 30 years, comparatively little attention has been paid to the key practicalproblem of estimation of the term structure of interest rates for emerging markets. This may be partly due to limited data availability. However, emerging bond markets are becoming increasingly important and liquid. It is, therefore, important to understand whether conclusions drawn from developed countries carry over to emerging markets. I estimate model parameters of fully exible Nelson Siegel Svensson term structures model which has become one of the most popular term structure model among academics, practitioners, and central bankers. I investigate four sets of bond data: U.S. Treasuries, and three major emerging market government bond data-sets (Brazil, Mexico and Turkey). I found that gradient and direct search methods perform poorly in estimating term structures of interest rates, while global optimization methods, particularly the hybrid particle swarm optimization, do well. Results are consistent across countries, both in- and out-of-sample, and for perturbations in prices and starting values. Another asset class I used the Nelson Siegel model is FX options where volatility smile for both emerging and developed markets is consistent with factor analysis in which three factor explains almost 100 % of the variation. I examine a number of models from literature to test whether they are consistent on the trading of options on the currencies from the over-the-counter market. I examine the in-sample and out-of-sample performance of the Nelson-Siegel model and found it has a superior performance when compared with benchmark models on FX options data set.