Spectral estimation for mixed causal-noncausal autoregressive models

This paper investigates new ways of estimating and identifying causal, noncausal, and mixed causal-noncausal autoregressive models driven by a non-Gaussian error sequence. We do not assume any parametric distribution function for the innovations. Instead, we use the information of higher-order cumulants, combining the spectrum and the bispectrum in a minimum distance estimation. We show how to circumvent the nonlinearity of the parameters and the multimodality in the noncausal and mixed models by selecting the appropriate initial values in the estimation. In addition, we propose a method of identification using a simple comparison criterion based on the global minimum of the estimation function. By means of a Monte Carlo study, we find unbiased estimated parameters and a correct identification as the data depart from normality. We propose an empirical application on eight monthly commodity prices, finding noncausal and mixed causal-noncausal dynamics

Spectral identification and estimation of mixed causal-noncausal invertible-noninvertible models

This paper introduces new techniques for estimating, identifying and simulating mixed causal-noncausal invertible-noninvertible models. We propose a framework that integrates high-order cumulants, merging both the spectrum and bispectrum into a single estimation function. The model that most adequately represents the data under the assumption that the error term is i.i.d. is selected. Our Monte Carlo study reveals unbiased parameter estimates and a high frequency with which correct models are identified. We illustrate our strategy through an empirical analysis of returns from 24 Fama-French emerging market stock portfolios. The findings suggest that each portfolio displays noncausal dynamics, producing white noise residuals devoid of conditional heteroscedastic effects

The Stock Market Reaction to Mergers and Acquisitions: Evidence from the Banking Industry

Mergers and acquisitions (M&As) are mainly a mechanism used in the Latin American banking industry to carry out business consolidation. This paper focuses on the effect of M&A announcements on stocks of Latin American banks and their rivals between 2000 and 2019. We evaluate two impacts of M&A announcements: impacts on cumulative abnormal returns (CAR) and impacts on event-induced variance (EIV). We use the GARCH-based event-study method. We find that acquirers and target banks have a statistically significant CAR, however, the sign is inconclusive. Rivals of acquirers and targets are not affected by M&A announcements. In general, we observe that EIV is negative for acquirers, targets, and rivals. Finally, we estimate a multivariate GARCH model to isolate the effects of co-movements of volatility between the acquirer and the target, and we find that the results remain qualitatively equal

A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets

The transition from traditional energy to cleaner energy sources has raised concerns from companies and investors regarding, among other things, the impact on financial downside risk. This article implements backtesting techniques to estimate and validate the value-at-risk (VaR) and expected shortfall (ES) in order to compare their performance among four renewable energy stocks and four traditional energy stocks from the WilderHill New Energy Global Innovation and the Bloomberg World Energy for the period 2005-2016. The models used to estimate VaR and ES are AR(1)-GARCH(1,1), AR(1)-EGARCH(1,1), and AR(1)-APARCH(1,1), all of them under either normal, skew-normal, Student's t, skewed-t, Generalized Error or Skew-Generalized Error distributed innovations. Backtesting performance is tested through traditional Kupiec and Christoffersen tests for VaR, but also through recent backtesting ES techniques. The paper extends these tests to the skewed-t, skew-normal and Skew-Generalized Error distributions and applies it for the first time in traditional and renewable energy markets showing that the skewed-t and the Generalized Error distribution are an accurate tool for risk management in those markets. Our findings have important implications for portfolio managers and regulators in terms of capital allocation in renewable and traditional energy stocks, mainly to reduce the impact of possible extreme loss events

Board Long-Term Orientation, Earnings Management, Disclosure and Risk

Short-term thinking continues to dominate corporate decision making due to the pressure to achieve expected quarterly earnings. As such, strategic goals take a back seat to short-term performance among the prime objectives of CEOs, the board of directors and management teams. Be that as it may, shareholders and stakeholders expect corporate leaders to pay equal attention to the long-term health of the corporate enterprise. An empirical study is conduced to test how long-term oriented board of directors diminish earnings management, increase disclosure and reduce risk. The results show that a long-term board orientation decreases earnings smoothing, stock price synchronicity and downside risk. To study this relationship, we construct a panel data from 2004 to 2015 comprising of 2834 OECD country firms. We conclude that board independence, board expertise and board audit committee activity increase long-term firm orientation. We find that boards with these characteristics are prone to the implementation of executives' long-term incentives, suggesting that a long-term orientation is beneficial not only to increase firms' transparency and disclosure but also to reduce firms' downside risk. Firms with long-term orientation reveal enough information to avoid stock price synchronicity, prevent the use of earnings management to conceal real firm performance and reduce downside risk - all decreasing the chance of financial failure. The results of the study not only nullify the arguments that there is no impact of long-term orientation and long-term incentives but also bolster and enrich the stream of literature that supports these variables ' impact on earnings management, stock price synchronicity and downside risk. Within the context of the international setting of the paper, we have substantiated the external validity of the results across geographies and country-wide regulations