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We employ a seminonparametric (SNP) methodology in characterizing the conditional density of the exchange rate changes. The model selection procedure based on the BIC is used by moving upward along an expansion path. We find the semiparametric AR(4)-GARCH(2,2) model for the KRW/USD returns and the semiparametric AR(1)-GARCH(2,2) model for the JPY/USD returns as the BIC preferred SNP models. Simulations from the BIC minimizing SNP models seem to appropriately mimic the actual data. The time dependent heterogeneity of the actual data is recognized by the simulations from the semiparametric AR-GARCH-type models and the nonlinear nonparametric AR-GARCH-type models. We show that it is important to take departures from the Gaussianity of the data into account in specifying conditional heterogeneity of the exchange rate returns process. We also provide evidence on the benefits from using the SNP models in estimating the conditional density function via simulations.

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This paper discusses theoretical explanations of currency crises and the contagion from them. We provide a basic introduction to the frameworks of three classical currency crisis models and assess each model’s implications. We then introduce a global game approach in these crisis models, which helps to overcome the limits found in them. Based on our global game analysis, we explain contagion between two economies.

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This paper revisits the claim that public provision of in-kind transfer is more efficient than transfers in cash. A simple job search model suggests that moral hazard would become more severe if recipients can save the transfer payment privately (the hidden saving problem), inducing them to make less effort to find jobs (that is, double deviation problem). We show that because the hidden saving problem always exists, economic efficiency requires overprovision of in-kind transfers and undersupply of cash grants. Our finding suggests that saving is not a virtue for government transfers.

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This paper investigates a new semiparametric ARCH-X model to account for both leverage effect and long memory property in volatility. It is a partial linear model combining a nonparametric ARCH component and an exogenous covariate that is persistent in memory. This model can allow for a flexible functional form of the asymmetric relationship between stock return and volatility and generate the long memory property in volatility. We adopt a realized volatility measure as the covariate in our model. For the daily FTSE 100 Index return series, the nonparametric component of our model captures the leverage effect and is estimated to be a complex nonlinear function. It is shown that our model outperforms other parametric or nonparametric volatility models both in within-sample and out-of-sample forecasts.