Birger Nilsson

Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is U.K. inflation and we utilize monthly data from 1969 to 2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast horizons. Both non-linear models perform significantly better than the VAR model.

Mean-Variance vs. Full-Scale Optimization: Broad Evidence for the UK

In the Full-Scale Optimization approach the complete empirical financial return probability distribution is considered; and the utility maximizing solution is found through numerical optimization. Using a portfolio choice setting of three UK equity indices we identify several utility functions featuring loss aversion and prospect theory; under which Full-Scale Optimization is a substantially better approach than the mean-variance approach. As the equity indices have return distributions with small deviations from normality; the findings indicate much broader usefulness of Full-Scale Optimization than has earlier been shown. The results hold in and out of sample; and the performance improvements are given in terms of utility as well as certainty equivalents.

Foreign Institutional Investment, Ownership, and Liquidity: Real and Informational Frictions

The literature widely documents the negative liquidity impact of foreign participation in firms that permit high foreign institutional ownership. This paper employs a unique setting for the limited participation of qualified foreign institutional investors (QFIIs) in Chinas A-share market and examines how this impacts on stock liquidity in emerging markets. Contrary to the findings in the literature, foreign investor participation helps enhance the liquidity of affected stocks by promoting trade activities and price discovery. The improvement in liquidity does not occur through the information friction channel, but rather the real friction channel. Our results are robust to endogeneity issue and the possible influence of the global financial crisis, industry effects and the stock exchange. Further, the liquidity improving effects of QFII are even stronger when the analysis is performed on a subsample of QFII firms.

Does Commonality in Illiquidity Matter to Investors

This paper investigates whether investors are compensated for taking on commonality risk in equity portfolios. A large literature documents the existence and the causes of commonality in illiquidity, but the implications for investors are less understood. In a more than fifty year long sample of NYSE stocks, we find that commonality risk carries a return premium of at least 2.0 per cent annually. The commonality risk premium is statistically and economically significant, and substantially higher than what is found in previous studies. It is robust when controlling for illiquidity level effects, transaction costs, as well as variations in illiquidity measurement.

Inflation forecasting, relative price variability and skewness

This article presents out-of-sample inflation forecasting results based on relative price variability and skewness. It is demonstrated that forecasts on long horizons of 1.5–2 years are significantly improved if the forecast equation is augmented with skewness.

New York mark-ups on petroleum products

In this paper we analyse rigidities in the behaviour of the mark-up on regular, midgrade and premium varieties of petrol in the New York area using a set of weekly frequency data and a methodology that analyses the pricing process using deterministic and stochastic techniques. The results are consistent across methodologies and indicate that the speeds of adjustment to the long-run equilibrium mark-up differ across varieties of petrol with margins of the premium variety falling faster than they rise, contrary to the popular claim of welfare-decreasing asymmetries in price transmission.

Dynamics in Systematic Liquidity

We develop the principal component analysis (PCA) approach to systematic liquidity measurement by introducing moving and expanding estimation windows. We evaluate these methods along with traditional estimation techniques (full sample PCA and market average) in terms of ability to explain (1) cross-sectional stock liquidity and (2) cross-sectional stock returns. For several traditional liquidity measures our results suggest an expanding window specification for systematic liquidity estimation. However, for price impact liquidity measures we find support for a moving window specification. The market average proxy of systematic liquidity produces the same degree of commonality, but does not have the same ability to explain stock returns as the PCA-based estimates.

Liquidity : Systematic Liquidity, Commonality, and High-Frequency Trading

Empirical work investigating commonality in liquidity and systematic liquidity risk utilizes various different estimators of systematic liquidity. This chapter is the first to compare and contrast such estimators. We distinguish two classes of systematic liquidity estimators that both have many followers in the literature: (1) weighted average estimators based on concurrent liquidity shocks and (2) principal components estimators based on both concurrent and past liquidity shocks. Our results show that the simpler weighted average estimators perform at least as well as the more complex principal components estimators. This finding is robust across different evaluation criteria and different underlying liquidity measures.