Stratégies reproductives et sex-ratio chez le termite Cavitermes tuberosus (Emerson, 1925) -- Biologie des Organismes et Ecologie

Recently shown in some termites, Asexual Queen Succession (AQS) is a reproductive strategy in which the primary queen is replaced by numerous parthenogenetically-produced neotenic queens that mate with the primary king. In contrast, the workforce and alate dispersers are produced sexually. If the primary king is replaced by a sexually-produced neotenic son, the matings between neotenic male and females beget asymmetries in the reproductive value of alates, promoting a female-biased alate sex-ratio. Cavitermes tuberosus (Termitidae: Termitinae) is a soil-feeding tropical species, which shows parthenogenetically-produced neotenics and an AQS syndrome. Our work aims to characterize the reproductive strategies in this species by determining (i) the developmental scheme, (ii) the genetic origin of sexuals, (iii) the level of genetic structure (analysis of 65 nests distributed in 14 sites) and (iv) the alate sex-ratio.Our results show that (i) neotenic females develop from the third or fourth nymphal instar; (ii) the majority of neotenic females (82%) are parthenogenetically-produced while only 2% of female alates are so; (iii) nests are differentiated within sites, indicating that the foundation of new nests mainly occurs by nuptial flights; (iv) numerical sex-ratio of alate-destined sexuals is balanced (SRN=0.509, IC95%=0.497-0.522) while investment sex-ratio is slightly female-biased (SRE=0.529, IC95%=0.517-0.542). Altogether, our results demonstrate AQS and its implications in C. tuberosus, and reveal particularities compared to other species in which AQS has been demonstrated: neotenic-headed nests are less frequent than primary-headed ones and neotenic females never become physogastric. AQS is found in various ecological contexts and seems phylogenetically more widespread than previously thought. This strategy shows some evolutionary advantages but these seem to differ depending on species.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.