We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

This paper evaluates the optimal bail-out and bail-in mix in the case of bankruptcy of Banco Espírito Santo (BES), SA, the second largest Portuguese private bank. The solution after the crisis of the BES, was to partition the bank into a good bank (Novo Banco (New Bank)) and keep the toxic...

The problem of instrument proliferation and its consequences (overfitting of the endogenous explanatory variables, biased IV and GMM estimators, weakening of the power of the overidentification tests) are well known. This paper introduces a statistical method to reduce the instrument count. The...

Given a nonparametric regression model, we assume that the number of covariates $d\rightarrow\infty$ but only some of these covariates are relevant for the model. Our goal is to identify the relevant covariates and to obtain some information about the structure of the model. We propose a new...

The local polynomial estimator is particularly affected by the curse of dimensionality. So, the potentialities of such a tool become ineffective for large dimensional applications. Motivated by this, we propose a new estimation procedure based on the local linear estimator and a nonlinearity...

This paper presents an empirical analysis of a life cycle model. We incorporate labor supply and family structure into the standard precautionary savings model and estimate structural parameters based on the moment conditions for the life cycle profiles of consumption, working hours, and wealth...

We provide a detailed classification of core and non-core liabilities for the Turkish banking system à laShin and Shin (2010). We further carry out a two-stage liquidity stress test similar to Van Den End (2010) where we simulate inflow and outflow factors as well as the network topology of...

This study develops a simple forecasting model using Japanese prefectural data. The Markov chain, known as a stochastic model, corresponds to a first-order vector auto-regressive (VAR) model. If the transition probability matrix can be appropriately estimated, a forecasting model using the...

Partial Least Squares Regression (PLSR) is a multivariate statistical method for constructing predictive models when the variables are many and highly collinear. Its goal is to predict a set of response variables from a set of predictor variables. This prediction is achieved by extracting a set...

Napjaink egyik legnagyobb érdeklődést kiváltó gazdaságelméleti területe a tőzsde. Az a felismerés ugyanis, hogy különböző értékpapírok árfolyamainak mozgását jól le lehet írni egy sztochasztikus folyamattal, megnyitotta az utat a tőzsde, illetve különböző...