Does Accrual Management Impair the Performance of Earnings‐Based Valuation Models?

This study examines how accrual manipulations affect firm valuation in the years surrounding the passage of the Sarbanes‐Oxley Act (SOX). We compare the absolute percentage pricing errors of RIM and DCF valuation models for a group of US firms suspected to have engaged in accrual manipulations to avoid a small loss or a small earnings decline vs. ‘Normal’ firms matched on industry, year and size. We find that RIM can better estimate intrinsic value than DCF for the matched Normal firms in the pre‐SOX period, but not so for accrual manipulators, and that SOX mitigates the harmful effect of accrual manipulations, completely eliminating the difference in RIM's accuracy advantage over DCF between Normal firms and accrual manipulators. As a further analysis, we redefine Suspect firms as real‐activity manipulators and find a significant across‐group difference in accuracy wedge in both sample periods, implying that SOX has prompted firms to favor real‐activity manipulations over accrual manipulations.     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

关于集团公司投资监管的若干问题

本文主要介绍了资本投入、运营及收益的监管问题。同时介绍了投资监管的实现方式问题。     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.     

Cox Point Processes: Why One Realisation Is Not Enough

We review a rich class of point process models, Cox point processes, and illustrate the necessity of more than one observation (point patterns) in performing parameter estimation. Furthermore, we introduce a new Cox point process model by treating the intensity function of the underlying Poisson point process as a random mixture of normal components. The behaviour and performance of the new model are compared with those of popular Cox point process models. The new model is exemplified with an application that involves a single point pattern corresponding to earthquake events in California, USA.     

Mobile payment and online to offline retail business models

Mobile payments are services that use mobile devices to make payments. When digitalization moves across channel boundaries, online to offline channel retail will expand. Online to offline retailing will become the future retail owner stream and retail operators will move from cross-channel or multi-channel to omni-channel. This study investigates a market survey in Taiwan developing a data mining analytics including clustering analysis and association rules based on a snowflake schema database design. The role of mobile payment is determined in terms of new retail payment mechanism that promotes a better consumer purchase experience in an online to offline business environment.     

Modeling asymmetric price transmission in the European food market

In this study, we employ a multivariate panel error correction model (PVECM) to investigate asymmetric price transmission among the farm, processor, and retail segments of the European food supply chain for the 2005–2016 period. The results indicate that, in both the long- and short-run, retail prices respond more strongly to processor price increases than decreases and the same occurs for processor prices due to farm price changes. Thus, the findings demonstrate the presence of positive asymmetric price transmission in the European food supply chain. Finally, the results of the present study indicate that the food price pass-through varies greatly across product category and across countries, and that the pass-through to producer prices is greater than that to consumer prices.     

DSGE forecasts of the lost recovery

The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.     

Time-changed Lévy LIBOR market model: Pricing and joint estimation of the cap surface and swaption cube

We propose a novel time-changed Lévy LIBOR (London Interbank Offered Rate) market model for jointly pricing of caps and swaptions. The time changes are split into three components. The first component allows matching the volatility term structure, the second generates stochastic volatility, and the third accommodates for stochastic skew. The parsimonious model is flexible enough to accommodate the behavior of both caps and swaptions. For the joint estimation we use a comprehensive data set spanning the financial crisis of 2007–2010. We find that, even during this period, neither market is as fragmented as suggested by the previous literature.     

Variance swaps valuation under non-affine GARCH models and their diffusion limits

In this article, we investigate the pricing and convergence of general non-affine non-Gaussian GARCH-based discretely sampled variance swaps. Explicit solutions for fair strike prices under two different sampling schemes are derived using the extended Girsanov principle as the pricing kernel candidate. Following standard assumptions on time-varying GARCH parameters, we show that these quantities converge respectively to fair strikes of discretely and continuously sampled variance swaps that are constructed based on the weak diffusion limit of the underlying GARCH model. An empirical study which relies on a joint estimation using both historical returns and VIX data indicates that an asymmetric heavier tailed distribution is more appropriate for modelling the GARCH innovations. Finally, we provide several numerical exercises to support our theoretical convergence results in which we further investigate the effect of the quadratic variation approximation for the realized variance, as well as the impact of discrete versus continuous-time modelling of asset returns.