This paper contributes to the debate on the impact of accounting measurement rules for financial assets. We examine the association between fair value accounting for financial assets and market price volatility for nonfinancial firms in an experimental setting. One group of participants was provided with financial statements where held-for-trading securities were reported at fair market value (FVA). Another group received financial statements with investments reported at historical cost (HCA). Controlling for accounting data, we find no systematic difference between FVA and HCA for three different measures of market price volatility, despite higher earnings volatility and marginally heavier trading under FVA.

business ethics, corporate citizenship, prizes, rankings, social and ethical auditing and reporting,

Because of the disparate data, reported in collaborative analyses of reference samples of rooks, various methods have been proposed for deriving “best values”. This work compares those methods and several additional ones. Included are two simplified estimates of “mode” which yield values close to those of the Dominant Cluster and Gamma Transformation methods. An example is also cited of the hazards that may result from too superficial reading of raw data.

No abstract

Purpose Previous studies have shown the VIX futures tend to roll-down the term structure and converge towards the spot as they grow closer to maturity. The purpose of this paper is to propose an approach to improve the volatility index fear factor-level (VIX-level) prediction. Design/methodology/approach First, the authors use a forward-looking technique, the Heath–Jarrow–Morton (HJM) no-arbitrage framework to capture the convergence of the futures contract towards the spot. Second, the authors use principal component analysis (PCA) to reduce dimensionality and save substantial computational time. Third, the authors validate the model with selected VIX futures maturities and test on value-at-risk (VAR) computations. Findings The authors show that the use of multiple factors has a significant impact on the simulated VIX futures distribution, as well as the computations of their VAR (gain in accuracy and computing time). This impact becomes much more compelling when analysing a portfolio of VIX futures of multiple maturities. Research limitations/implications The authors’ approach assumes the variance to be stationary and ignores the volatility smile. Nevertheless, they offer suggestions for future research. Practical implications The VIX-level prediction (the fear factor) is of paramount importance for market makers and participants, as there is no way to replicate the underlying asset of VIX futures. The authors propose a procedure that provides efficiency to both pricing and risk management. Originality/value This paper is the first to apply a forward-looking method by way of a HJM framework combined with PCA to VIX-level prediction in a portfolio context.