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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Adaptive Testing for Alphas in High-dimensional Factor Pricing Models
This paper proposes a new procedure to validate the multi-factor pricing
theory by testing the presence of alpha in linear factor pricing models with a
large number of assets. Because the market's inefficient pricing is likely to
occur to a small fraction of exceptional assets, we develop a testing procedure
that is particularly powerful against sparse signals. Based on the
high-dimensional Gaussian approximation theory, we propose a simulation-based
approach to approximate the limiting null distribution of the test. Our
numerical studies show that the new procedure can deliver a reasonable size and
achieve substantial power improvement compared to the existing tests under
sparse alternatives, and especially for weak signals
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
We study the trade-off between expectation and tail risk for regret
distribution in the stochastic multi-armed bandit problem. We fully
characterize the interplay among three desired properties for policy design:
worst-case optimality, instance-dependent consistency, and light-tailed risk.
We show how the order of expected regret exactly affects the decaying rate of
the regret tail probability for both the worst-case and instance-dependent
scenario. A novel policy is proposed to characterize the optimal regret tail
probability for any regret threshold. Concretely, for any given and , our policy achieves a worst-case expected regret
of (we call it -optimal) and an instance-dependent
expected regret of (we call it -consistent), while
enjoys a probability of incurring an regret
( in the worst-case scenario and in the
instance-dependent scenario) that decays exponentially with a polynomial
term. Such decaying rate is proved to be best achievable. Moreover, we discover
an intrinsic gap of the optimal tail rate under the instance-dependent scenario
between whether the time horizon is known a priori or not. Interestingly,
when it comes to the worst-case scenario, this gap disappears. Finally, we
extend our proposed policy design to (1) a stochastic multi-armed bandit
setting with non-stationary baseline rewards, and (2) a stochastic linear
bandit setting. Our results reveal insights on the trade-off between regret
expectation and regret tail risk for both worst-case and instance-dependent
scenarios, indicating that more sub-optimality and inconsistency leave space
for more light-tailed risk of incurring a large regret, and that knowing the
planning horizon in advance can make a difference on alleviating tail risks
Multi-Attribute Utility Preference Robust Optimization: A Continuous Piecewise Linear Approximation Approach
In this paper, we consider a multi-attribute decision making problem where
the decision maker's (DM's) objective is to maximize the expected utility of
outcomes but the true utility function which captures the DM's risk preference
is ambiguous. We propose a maximin multi-attribute utility preference robust
optimization (UPRO) model where the optimal decision is based on the worst-case
utility function in an ambiguity set of plausible utility functions constructed
using partially available information such as the DM's specific preferences
between some lotteries. Specifically, we consider a UPRO model with two
attributes, where the DM's risk attitude is multivariate risk-averse and the
ambiguity set is defined by a linear system of inequalities represented by the
Lebesgue-Stieltjes (LS) integrals of the DM's utility functions. To solve the
maximin problem, we propose an explicit piecewise linear approximation (EPLA)
scheme to approximate the DM's true unknown utility so that the inner
minimization problem reduces to a linear program, and we solve the approximate
maximin problem by a derivative-free (Dfree) method. Moreover, by introducing
binary variables to locate the position of the reward function in a family of
simplices, we propose an implicit piecewise linear approximation (IPLA)
representation of the approximate UPRO and solve it using the Dfree method.
Such IPLA technique prompts us to reformulate the approximate UPRO as a single
mixed-integer program (MIP) and extend the tractability of the approximate UPRO
to the multi-attribute case. Furthermore, we extend the model to the expected
utility maximization problem with expected utility constraints where the
worst-case utility functions in the objective and constraints are considered
simultaneously. Finally, we report the numerical results about performances of
the proposed models.Comment: 50 pages,18 figure
Implementing and Evaluating Security in O-RAN: Interfaces, Intelligence, and Platforms
The Open Radio Access Network (RAN) is a networking paradigm that builds on
top of cloud-based, multi-vendor, open and intelligent architectures to shape
the next generation of cellular networks for 5G and beyond. While this new
paradigm comes with many advantages in terms of observatibility and
reconfigurability of the network, it inevitably expands the threat surface of
cellular systems and can potentially expose its components to several cyber
attacks, thus making securing O-RAN networks a necessity. In this paper, we
explore the security aspects of O-RAN systems by focusing on the specifications
and architectures proposed by the O-RAN Alliance. We address the problem of
securing O-RAN systems with an holistic perspective, including considerations
on the open interfaces used to interconnect the different O-RAN components, on
the overall platform, and on the intelligence used to monitor and control the
network. For each focus area we identify threats, discuss relevant solutions to
address these issues, and demonstrate experimentally how such solutions can
effectively defend O-RAN systems against selected cyber attacks. This article
is the first work in approaching the security aspect of O-RAN holistically and
with experimental evidence obtained on a state-of-the-art programmable O-RAN
platform, thus providing unique guideline for researchers in the field.Comment: 7 pages, 5 figures, 1 table, submitted to IEEE Network Magazin
Demand Response Applications for the Operation of Smart Natural Gas Systems
This chapter discusses different aspects related to the operation of natural gas systems in the framework of the new configuration of energy systems based on the smart grid concept. First of all, different experiences performed worldwide regarding the application of demand response principles to increase the efficiency and operability of natural gas networks are presented. Next, the characteristics of the natural gas system to be configured according to the smart grid architecture are discussed, including the necessary agents for the proper functioning of such infrastructure. After that, the current state of installation of gas smart meters in some European countries is presented, according to the massive rollout process promoted by the European Union. Barriers that prevent the full exploitation of demand response resources related to natural gas systems are presented in the next section. After that, technical constraints which may be solved by using demand response are presented. Finally, last tendencies related to the development of natural gas systems, such as the injection of hydrogen, are considered
Discovering the hidden structure of financial markets through bayesian modelling
Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole.
We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors.
Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces
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