Model selection – choosing the relevant variables and structures – is a central task in econometrics. Given a limited number of observations, estimation and inference depend on this choice. A frequently treated model-selection problem arises in multivariate autoregressive models, where the...

Quantitative investment strategies are often selected from a broad class of candidate models estimated and tested on historical data. Standard statistical technique to prevent model overfitting such as out-sample back-testing turns out to be unreliable in the situation when selection is based on...

Quantitative investment strategies are often selected from a broad class of candidate models estimated and tested on historical data. Standard statistical technique to prevent model overfitting such as out-sample back-testing turns out to be unreliable in the situation when selection is based on...

Since its publication, the seminal structural model of default by <xref ref-type="bibr" rid="B45">Merton (1974) has become the workhorse for gaining insights about how firms choose their capital structure, a “bread and butter” topic for financial economists. Capital structure theory is inevitably linked to several...</xref>

The main purpose of this paper is to extend the empirical research on the behavior of credit spreads on the USD denominated Malaysian bonds. We find that international political events have more influence on the changes of bond yield spreads from Malaysian USD issues than domestic events....

Theoretically, corporate debt is economically equivalent to safe debt minus a put option on the firm’s assets. We empirically show that indeed portfolios of long Treasuries and short traded put options ("pseudo bonds") closely match the properties of traded corporate bonds. Pseudo bonds...

Inspired by findings of low–dimensional nonlinearities and the Theorem of Takens (1983) forecasting models of financial time series are often built upon nonparametric, i.e. universal nonlinear, univariate relationships. Empirical investigations, however, are seriously contaminated by the...

Threshold Accepting (TA) is a powerful optimization heuristic from the class of stochastic local search algorithms. It has been applied successfully to different optimization problems in statistics and econometrics, including the uniform design problem. Using the latter application as example,...

Threshold Accepting (TA) is a powerful optimization heuristic from the class of stochastic local search algorithms. It has been applied successfully to different optimization problems in statistics and econometrics, including the uniform design problem. Using the latter application as example,...

In this paper we investigate the performance of the threshold accepting heuristic for the index tracking problem. The index tracking problem consists in minimizing the tracking error between a portfolio and a benchmark. The objective is to replicate the performance of a given index upon the...