Michel van der Wel

Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson-Siegel Model with Time-Varying Parameters

In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter that we treat as the fourth latent factor. Second, we specify the overall volatility as a generalized autoregressive conditional heteroscedasticity (GARCH) process. We present empirical evidence of considerable increases in within-sample goodness of fit for these advances in the dynamic Nelson–Siegel model.

Smooth Dynamic Factor Analysis with Application to the U.S. Term Structure of Interest Rates

SUMMARY We consider the dynamic factor model and show how smoothness restrictions can be imposed on factor loadings by using cubic spline functions. We develop statistical procedures based on Wald, Lagrange multiplier and likelihood ratio tests for this purpose. The methodology is illustrated by analyzing a newly updated monthly time series panel of US term structure of interest rates. Dynamic factor models with and without smooth loadings are compared with dynamic models based on Nelson–Siegel and cubic spline yield curves. We conclude that smoothness restrictions on factor loadings are supported by the interest rate data and can lead to more accurate forecasts. Copyright

Customer Order Flow, Intermediaries, and Discovery of the Equilibrium Risk-free Rate

Macro announcements change the equilibrium risk-free rate. We find that Treasury prices reflect part of the impact instantaneously, but intermediaries rely on their customer order flow after the announcement to discover the full impact. This customer flow informativeness is strongest when analyst macro forecasts are most dispersed. The result holds for 30-year Treasury futures trading in both electronic and open-outcry markets. We further show that intermediaries benefit from privately recognizing informed customer flow, as their own-account trading profitability correlates with customer order access.

Dynamic Factor Models for the Volatility Surface

The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable features for representing the surface and its dynamics: a general dynamic factor model, restricted factor models designed to capture the key features of the surface along the moneyness and maturity dimensions, and in-between spline-based methods. Key findings are that: (i) the restricted and spline-based models are both rejected against the general dynamic factor model, (ii) the factors driving the surface are highly persistent, (iii) for the restricted models option Delta is preferred over the more often used strike relative to spot price as measure for moneyness.

Measuring Convergence Using Dynamic Equilibrium Models: Evidence from Chinese Provinces

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 1978 to 2010. 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.

Are market makers uninformed and passive? Signing trades in the absence of quotes

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.

Economic Valuation of Liquidity Timing

This paper provides a comprehensive economic evaluation of the short-horizon predictive ability of liquidity on monthly stock returns, using dynamic asset allocation strategies. We assess the economic value of the out-of-sample power of empirical models based on different liquidity measures and find three key results: liquidity timing leads to tangible economic gains; a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on various liquidity measures to one that conditions on the Zeros measure (Lesmond, Ogden, and Trzcinka, 1999); the Zeros measure outperforms other liquidity measures because of its robustness in extreme market conditions. These findings are stable over time and robust to controlling for existing market return predictors or considering risk-adjusted returns.

An Asset Pricing Approach to Testing General Term Structure Models

We develop a new empirical approach to term structure analysis that allows testing for time-varying risk premiums and arbitrage opportunities in models with both unobservable factors and factors identified as the innovations to observed macroeconomic variables. Factors can play double roles as both covariance-generating common shocks driving yields and determinants of market prices of risk in cross-sectional pricing. The evidence favors time-varying risk prices significantly related to the second Stock–Watson principal component of macroeconomic variables and to changes in the industrial production index. Our preferred specification includes these two observable and two unobservable factors, with the no-arbitrage condition imposed.

Common Factors in Commodity Futures Curves

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.

Combining Density Forecasts Using Censored Likelihood Scoring Rules

We investigate the added value of combining density forecasts for asset return prediction in a specific region of support. We develop a new technique that takes into account model uncertainty by assigning weights to individual predictive densities using a scoring rule based on the censored likelihood. We apply this approach in the context of recently developed univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and Nikkei stock market indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates of the individual models.We investigate the added value of combining density forecasts focused on a specific region of support. We develop a forecast combination scheme that assigns weights to individual predictive densities based on the censored likelihood scoring rule. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, Realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same conclusion, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve compared to estimates based on equal weights, the log score or individual models.