PERFORMANCE OF STOLL's SPREAD COMPONENT ESTIMATOR: EVIDENCE FROM SIMULATIONS,TIME-SERIES,AND CROSS-SECTIONAL DATA

Stoll (1989) introduces an intuitive procedure to estimate the basic components of the bid-ask spread (order-processing cost, inventory cost, and adverse-selection cost). He also provides reasonable estimates of the magnitudes of the order-processing, inventory, and adverse-selection costs of making markets for a large cross-section of NASDAQ/NMS stocks. Empirical applications of Stoll's model produce widely different estimates of the bid-ask spread components. We derive the sampling properties of Stoll's estimator of the realized bid-ask spread, i.e., the sum of the order-processing and inventory components. We test Stoll's model in simulations, using the ideal conditions implied by the model. We conclude that noise in serial covariance estimates causes estimates of the realized spread to be severely biased and highly unreliable in short time-series and small cross-sectional samples.     

Twenty years of jumps in commodity markets

In this article, we provide statistical evidence around jumps affecting commodity returns. Using nearly 20 years of daily data, we use Laurent, Lecourt, and Palm's (2011) methodology to jump extraction, and discuss various aspects of the estimated jump activity. On average across various commodity markets, we find a high number of days for which returns exhibit the presence of jumps, consistently with the intuition that commodities are affected by large price fluctuations. We emphasize that the post-jump average return depends on the commodity sector considered (e.g. agriculture, energy, or metals). We also show evidence of a jump-to-volatility channel for commodities (similar to the effect usually found for equities). Finally, we diagnose around 40 dates during which commodity indices, stocks, bonds and currencies `co-jump’, revealing a tail dependence between standard and alternative assets.     

THE IMPORTANCE OF PUNISHMENT SUBSTITUTABILITY IN CRIMINOMETRIC STUDIES

This study investigates the role of punishment substitutability in the empirical estimation of the economic model of crime. Using a dynamic panel data model fitted to a panel of Local Government Areas in New South Wales, Australia, we evaluate the effects of financial penalties and imprisonment on the crime rate. Our results show that crime is clearly a dynamic phenomenon, and that failure to incorporate both financial penalties and imprisonment can lead to a misspecified model. Furthermore, our results vary significantly for different crime categories, highlighting the importance of analysing specific crime categories separately.     

Family Firm Heterogeneity and Tax Aggressiveness: A Mixed Gamble Approach

Few studies try to understand how the unique preferences of family firms affect tax strategies, and how family firm heterogeneity drives variation in tax activities. Drawing on the mixed gamble approach, this study examines the tax aggressiveness of different types of family firms, considering how various sources of heterogeneity alter the perception of potential gains and losses to socioemotional and financial wealth. Based on a panel dataset of 242 private family firms for the period 2012–2014, this study shows that strong family-owned firms, family firms with a family CFO, family-founder firms, and family-named firms display lower levels of tax aggressiveness. These findings demonstrate that family firm heterogeneity is a crucial factor in the mixed gamble calculus of tax aggressiveness.     

Calibration of local volatility model with stochastic interest rates by efficient numerical PDE methods

Decisions in Economics and Finance - Long-maturity options or a wide class of hybrid products are evaluated using a local volatility-type modelling for the asset price S(t) with a stochastic...     

Disillusionment from Failure as a Source of Successful Learning

In this article I review the literature on organizational learning from failed product launches. I challenge the view that failure is an inevitable part of the “probe and learn” process as well as the view that companies do not learn anything from such failures. I conclude there is no direct link between learning and commercial failures, and highlight many obstacles that hamper the learning process. Finally, even when learning does occur, it will not always result in improved organizational performance. Copyright © 2011 ASAC. Published by John Wiley & Sons, Ltd.     

A Review of Heinrich von Stackelberg's Book: ‘Market Structure and Equilibrium’

In this note, we review the recent translation in English by Bazin et al. of Stackelberg's book ‘Marktform und Gleichgewicht’ (1934).     

Bounds for VIX futures given S&P 500 smiles

We derive sharp bounds for the prices of VIX futures using the full information of S&P 500 smiles. To that end, we formulate the model-free sub/superreplication of the VIX by trading in the S&P 500 and its vanilla options as well as the forward-starting log-contracts. A dual problem of minimizing/maximizing certain risk-neutral expectations is introduced and shown to yield the same value.The classical bounds for VIX futures given the smiles only use a calendar spread of log-contracts on the S&P 500. We analyze for which smiles the classical bounds are sharp and how they can be improved when they are not. In particular, we introduce a family of functionally generated portfolios which often improves the classical bounds while still being tractable; more precisely, they are determined by a single concave/convex function on the line. Numerical experiments on market data and SABR smiles show that the classical lower bound can be improved dramatically, whereas the upper bound is often close to optimal.     

How well does a small structural model with sticky prices and wages fit postwar U.S. data?

In this paper, we ask whether a small structural model with sticky prices and wages, embedding various modelling devices designed to increase the degree of strategic complementarity between price-setters, can fit postwar U.S. data. To answer this question, we resort to a two-step empirical evaluation of our model. In a first step, we estimate the model by minimizing the distance between theoretical autocovariances of key macroeconomic variables and their VAR-based empirical counterparts. In a second step, we resort to Watson's [Watson, M.W., 1993. Measures of fit for calibrated models. Journal of Political Economy 101, 1011–1041.] procedure [Measures of fit for calibrated models. Journal of Political Economy 101 (6), 1011.1041] to quantify the model's goodness-of-fit. Our main result is that the combination of sticky prices and sticky wages is central in order to obtain a good empirical fit. Our analysis also reveals that a model with only sticky wages does not perform well according to Watson's criterion [Watson, M.W., 1993. Measures of fit for calibrated models. Journal of Political Economy 101, 1011–1041.].