Search

Dutch Abstract: Pensioenfondsen moeten hun Vereist Eigen Vermogen (VEV) berekenen als onderdeel van het financieel toetsingskader (FTK). De VEV wordt eenvoudig bepaald met de bekende wortelformule, maar daarbij moet worden uitgegaan van de evenwichtssituatie waarbij het Eigen Vermogen exact gelijk is aan de VEV. Dit betekent dat de VEV iteratief moet worden berekend, wat met name in ALM analyses bewerkelijk is. Dit artikel presenteert een directe formule voor de VEV zodat er niet geitereerd hoeft te worden. English Abstract: Dutch pension funds have to calulcate their Regulatory Capital Requirement (RCR) as part of the regulatory framework. The RCR is calculated by the well-kown square-root formula based on an equilibrium state where the equity of the pension fund equals the RCR. This implies an iterative procedure for calculating the RCR, which is demanding for ALM analyses. This paper presents a direct formula for calculating the RCR, whilst removing the need for iterations.

Real-time macroeconomic data are typically incomplete for today and the immediate past ('ragged edge') and subject to revision. To enable more timely forecasts the recent missing data have to be imputed. The paper presents a state-space model that can deal with publication lags and data revisions. The framework is applied to the US leading index. We conclude that including even a simple model of data revisions improves the accuracy of the imputations and that the univariate imputation method in levels adopted by The Conference Board can be improved upon. (C) 2011 Elsevier Inc. All rights reserved.

Empirical modeling of the yield curve is often inconsistent with absence of arbitrage. In fact, many parsimonious models, like the popular Nelson-Siegel model, are inconsistent with absence of arbitrage. In other cases, arbitrage-free models are often used in inconsistent ways by recalibrating parameters that are assumed constant. For these cases, this paper introduces an arbitrage smoothing device to control arbitrage errors that arise in fitting a sequence of yield curves. The device is applied to the US term structure for the family of Nelson-Siegel curves. It is shown that the arbitrage smoothing device contributes to parameter stability and smoothness.

We present a comprehensive overview of derivative pricing in Gaussian affine asset pricing models. Gaussian affine asset pricing models are widely used in practice for pricing and scenario analysis due to their tractable pricing implications and easy estimation. This tractability is essential to efficiently evaluate portfolios of derivatives within many scenarios and time periods. We present efficient closed-form pricing formulas for the most common derivative instruments used by pension funds and insurance companies, such as interest rate swaps, swaptions, inflation-linked swaps, equity options, based on results from the literature. The pricing formulas are presented in a comprehensive computable form by utilising results based on the matrix exponential. Next, we show how some models commonly used in practice fit in the Gaussian affine framework, so that the pricing formulas can be applied to these cases. In particular, we discuss the KNW model by Koijen, Nijman and Werker (2010), which is widely used in the pension industry. Finally we discuss how our results can be applied to a time-inhomogeneous extension of the model that allows perfect calibration to the observed yield curve.

The paper proposes a computationally convenient and parsimonious approach for creating a funding liquidity factor, building on work that relates the lack of funding of financial institutions to their ability to exploit arbitrage opportunities. Fontaine and Garcia (2012) propose a price-based funding liquidity factor from mispricing of bonds of similar characteristics but different ages. However, their arbitrage-free Nelson–Siegel framework requires the use of a non-linear Kalman filter, which is computationally intensive in practice. The novelty of this paper is to suggest an easier method for constructing an alternative liquidity factor that retains much of the same properties. Our proposed method for constructing this proxy liquidity factor relaxes the arbitrage-free assumption in the specification of the term structure model and bases it on a simple and flexible term structure specification. We demonstrate that this parsimonious liquidity factor fits the data well. The constructed factor is highly correlated with the Fontaine and Garcia (2012) liquidity factor and other funding liquidity measures, such as a liquidity factor by Hu et al. (2012), the TED-spread and the CP-spread.

The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices significantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between different hourly prices, but the risk of overfitting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations.

We propose a tractable class of arbitrage-free models for the term structure of electricity prices, where spot and forward prices are a linear function of latent factors. The modeling approach offers much flexibility in the specification of the factor dynamics by only restricting their risk-neutral drift. We derive a canonical form where the parameters determining the factor loadings for the forward prices can be separated from the parameters describing the factor dynamics. The factor loading parameters can be consistently estimated by directly fitting the cross-section of forward prices. The modeling framework is applied to a panel of daily prices on forward contracts from the Nordpool electricity market, using affine factor dynamics. We find that forward prices (i) are mainly driven by changes in the level, slope and curvature of the forward curve; (ii) exhibit time-varying volatilities; and (iii) incorporate time-varying forward premia.

Winner 2014 Crowell Second Prize. We assess the effect of aggregate stock market illiquidity on U.S. Treasury bond risk premia. We find that the stock market illiquidity variable adds to the well established Cochrane-Piazzesi and Ludvigson-Ng factors. It explains 10%, 9%, 7%, and 7% of the one-year-ahead variation in the excess return for two-, three-, four-, and five-year bonds respectively and increases the adjusted R 2 by 3-6% across all maturities over Cochrane and Piazzesi (2005) and Ludvigson and Ng (2009) factors. The effects are highly statistically and economically significant both in and out of sample. We find that our result is robust to and is not driven by information from open interest in the futures market, long-run inflation expectations, dispersion in beliefs, and funding liquidity. We argue that stock market illiquidity is a timely variable that is related to "right-to-quality" episodes and might contain information about expected future business conditions through funding liquidity and investment channels.

The paper presents an arbitrage-free yield model based on macro-portfolio dynamics. Apart from a level factor, detrended portfolio values serve as factors for the yield model. Using trend-balanced portfolios and parameters in terms of the instantaneous mean-variance frontier, risk premia and yield curve dynamics are modeled in a natural, integrated, parsimonious way. Positivity constraints on parameters and factors are formulated that guarantee the model is well posed. In an empirical analysis a four-factor model is applied to daily US Treasury yields. We estimate by quasi-maximum likelihood using the unscented Kalman filter. The model fits well, while the estimates of both parameters and factors satisfy the positivity constraints. The analysis reveals a strong link between business cycle variation in yields and risk premia.

Motivated by yield curve modeling, we solve dynamic mean-variance efficiency problems in both discrete and continuous time. Our solution applies to both complete and incomplete markets and we do not require the existence of a riskless asset, which is relevant for yield curve modeling. Stochastic market parameters are incorporated using a vector of state variables. In particular for markets with deterministic parameters, we provide explicit solutions. In such markets, where no riskless asset need be present, we describe term-independent uniformly mean-variance efficient investment strategies. For constant parameters we show the existence of a unique, symmetrically distributed, trend stationary, uniformly MV efficient strategy.