Working Papers

On this part of the site you a list of my working papers. For questions, suggestions, comments, or anything else related to my research please feel free to contact me at: vanderwel@ese.eur.nl. For a list without abstracts, click here.

Heterogeneous Macro and Financial Effects of ECB Asset Purchase Programs
Joint work with Terri van der Zwan and Erik Kole
Abstract: Central banks resorted to asset purchase programs to replace conventional policy measures, which became ineffective after interest rates approached the zero lower bound. We investigate their effects on financial markets and focus on heterogeneous transmission using a Bayesian structural vector autoregression analysis. Since financial markets react directly to policy announcements, we base our identification scheme on market surprises at the announcement time. We find evidence of a stimulating effect on the economy, declining government bond yields, increasing stock prices, increasing value-growth spread and a reduction in stress in corporate and sovereign debt markets after an asset purchase shock. We disentangle the effect among industry sectors and EMU countries and find that the effect is heterogeneous, with financial stocks and the economy of Southern European countries being the most positively affected.
Please click on the title to find the paper on SSRN.

Global Evidence on Unspanned Macro Risks in Dynamic Term Structure Models
Joint work with Yaoyuan Zhang
Abstract: There are mixed results on whether macro risks are spanned by the yield curve. This paper reviews the major arguments and takes a global perspective to obtain comprehensive evidence. We study a large cross-section of 22 countries, including both developed and emerging markets. Our regression evidence confirms that macro information provides explanatory power for bond excess returns on top of yield factors. This finding is particularly strong in emerging markets. However, from a mechanical perspective, discriminating between spanned and unspanned models when considering in-sample fit and term premium predictions makes no difference.
Please click on the title to find the paper on SSRN.

Forecasting Bond Risk Premia using Stationary Yield Factors
Joint work with Martin Martens and Tobias Hoogteijling
Abstract: The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which correspond to changes in level, slope and curvature. The new factors based on changes in yields have strong predictive power for bond risk premia, in contrast to the factors based on yield levels. We also provide insights into the impact this has on the added value of macro data for bond risk premia predictions and the recent conclusion that machine learning provides better forecasts than linear regression.
Please click on the title to find the paper on SSRN.

Common Factors in Commodity Futures Curves
Joint work with Dennis Karstanje and Dick van Dijk
Abstract: We examine the comovement of factors driving commodity futures curves. We adopt the framework of the dynamic Nelson-Siegel model, enabling us to examine not only comovement in price levels but also futures curve shapes, as characterized by their slope and curvature. Our empirical results based on 24 commodities over the period 1995-2012 demonstrate that the individual commodity futures curves are driven by common components. The commonality is mostly sector specific, which implies that commodities are a heterogeneous asset class. The common components in the level of the curve have become more important over time, coinciding with the financialization of the commodities market. The market-wide level component, which is common to all commodities, is related to economic output variables, exchange rates and hedging pressure. Factors driving the shape of the futures curve are related to inventory data (theory of storage), hedging pressure (theory of normal backwardation) and interest rates. The use of full curve data alters findings on comovement, compared to the use of only first-nearby contract data. The full curve commonality results give more insight in the market dynamics and can help in the construction of commodity futures portfolios and hedging decisions.
Please click on the title to find the paper on SSRN.

A Bayesian Infinite Hidden Markov Vector Autoregressive Model
Joint work with Didier Nibbering and Richard Paap
Abstract: We propose a Bayesian infinite hidden Markov model to estimate time-varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model, parameters can vary over potentially an infinite number of regimes. The Dirichlet process however favours a parsimonious model without imposing restrictions on the parameter space. An empirical application demonstrates the ability of the model to capture both smooth and abrupt parameter changes over time, and a real-time forecasting exercise shows excellent predictive performance even in large dimensional VARs.
Please click on the title to find the paper on SSRN.

Why do the Pit-Hours Outlive the Pit?
Joint work with Sait Ozturk and Dick van Dijk
Abstract: We study why a majority of trades happen during the pit hours, i.e. when the trading pit is open. We examine the case of 30-year U.S. Treasury futures. The pit hour activity clustering cannot be explained by the informativeness of pit trading or the liquidity. Instead, a feedback mechanism between price informativeness, information asymmetry, price impact of trades and trading activity explains why the pit hours outlive the pit. In the recent years the negative effect of price impact on trading activity ceases to be a significant factor, likely due to improvements in electronic trading infrastructure and order execution algorithms.
Please click on the title to find the paper on SSRN.

Measuring Convergence using Dynamic Equilibrium Models: Evidence from Chinese Provinces
Joint work with Lei Pan and Olaf Posch
Abstract: We propose a model to study economic convergence in the tradition of neoclassical growth theory. We employ a novel stochastic set-up of the Solow (1956) model with shocks to both capital and labor. Our novel approach identifies the speed of convergence directly from estimating the parameters which determine equilibrium dynamics. The inference on the structural parameters is done using a maximum-likelihood approach. We estimate our model using growth and population data for China's provinces from 1980 to 2009. We report heterogeneity in the speed of convergence both across provinces and time. The Eastern provinces show a higher tendency of convergence, while there is no evidence of convergence for the Central and Western provinces. We find empirical evidence that the speed of convergence decreases over time for most provinces.
Please click on the title to find the paper on SSRN.

Are Market Makers Uninformed and Passive? Signing Trades in the Absence of Quotes
First author, joint work with Albert Menkveld and Asani Sarkar
Abstract: We develop a new likelihood-based approach to sign trades in the absence of quotes. It is equally efficient as existing MCMC methods, but more than 10 times faster. It can deal with the occurrence of multiple trades at the same time, and noisily observed trade times. We apply this method to a high-frequency dataset of the 30Y U.S. treasury futures to investigate the role of the market maker. Most theory characterizes him as an uninformed passive liquidity supplier. Our results suggest that some market makers actively demand liquidity for a substantial part of the day and are informed speculators.
Please click on the title to find the paper on SSRN.