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The purpose of this paper is to explore the possible roles of active tails for steady-state legged locomotion, focusing on a design principle which simplifies control by decoupling different control objectives.

A series of simple models are proposed which capture the dynamics of an idealized running system with an active tail. These models suggest that the overall control problem can be simplified and effectively decoupled via a proper tail design. This design principle is further explored in simulation using trajectory optimization. The results are then validated in hardware using a one degree-of-freedom active tail mounted on the quadruped robot Cheetah-Cub.

The results of this paper show that an active tail can greatly improve both forward velocity and reduce body-pitch per stride while adding minimal complexity. Further, the results validate the design principle of using long, light tails compared to shorter heavier ones.

This paper builds on previous results, with a new focus on steady-state locomotion and in particular deals directly with stance phase dynamics. A novel design principle for tails is proposed and validated.

In this paper, the aim is to investigate the tail behavior of daily stock returns for three emerging stock in the Gulf region (Bahrain, Oman, and Saudi Arabia) over the period 1998‐2005. In addition, the aim is also to test whether the distributions are similar across these markets.

Following McNeil and Frey, Wanger and Marsh, and Bystrom, extreme value theory (EVT) methods are utilized to examine the asymptotic distribution of the tail for daily returns in the Gulf region. As a first step and to obtain independent and identically distributed residuals series, the returns are prefiltered with an ordinary time‐series model, taking into account the observed Gulf return dynamics. Then, the “Peaks‐Over‐Threshold” (POT) model is applied to estimate the tails of the innovational distribution.

Not only is the heavy tail found to be a facial appearance in these markets, but also POT method of modelling extreme tail quantiles is more accurate than conventional methodologies (historical simulation and normal distribution models) in estimating the tail behavior of the Gulf markets returns. Across all return series, it is found that left and right tails behave very different across countries.

The results show that risk models that are able to exploit tail behavior could lead to more accurate risk estimates. Thus, participants in the Gulf equity markets can rely on EVT‐based risk model when assessing their risks.

The paper extends previous studies in two aspects. First, it extends the classical unconditional extreme value approach by first filtering the data by using AR‐FIAPARCH model to capture some of the dependencies in the stock returns, and thereafter applying ordinary extreme value techniques. Second, it provides a broad analysis of return dynamics of the Gulf markets.

The last few years in the financial markets have shown great instability and high volatility. In order to capture the amount of risk a financial firm takes on in a single trading day, risk managers use a technology known as value‐at‐risk (VaR). There are many methodologies available to calculate VaR, and each has its limitations. Many past methods have included a normality assumption, which can often produce misleading figures as most financial returns are characterized by skewness (asymmetry) and leptokurtosis (fat‐tails). The purpose of this paper is to provide an overview of VaR and describe some of the most recent computational approaches.

This paper compares the Student‐t, autoregressive conditional heteroskedastic (ARCH) family of models, and extreme value theory (EVT) as a means of capturing the fat‐tailed nature of a returns distribution.

Recent research has utilized the third and fourth moments to estimate the shape index parameter of the tail. Other approaches, such as extreme value theory, focus on the extreme values to calculate the tail ends of a distribution. By highlighting benefits and limitations of the Student‐t, autoregressive conditional heteroskedastic (ARCH) family of models, and the extreme value theory, one can see that there is no one particular model that is best for computing VaR (although all of the models have proven to capture the fat‐tailed nature better than a normal distribution).

This paper details the basic advantages, disadvantages, and mathematics of current parametric methodologies used to assess value‐at‐risk (VaR), since accurate VaR measures reduce a firm's capital requirement and reassure creditors and investors of the firm's risk level.

The purpose of this paper is to consider the existence and significance of heavy‐tailed – and in particular, infinite‐mean – insurance losses.

Three specific questions are addressed in turn. First, how do infinite‐mean insurance losses arise in the real world? Second, can infinite‐mean losses exist even in the presence of insurance policy limits (caps)? Third, why are infinite‐mean losses so infrequently discussed by practitioners and regulators?

The paper first shows that heavy‐tailed – and in particular, infinite‐mean – insurance losses can be generated by simple modifications of gamma (exponential) random variables. It then finds that the property of infinite means cannot be prevented by the imposition of policy limits (caps). Finally, the paper argues that the statistical contagion and financial intractability of infinite‐mean losses generate a political fear among practitioners and regulators analogous to that associated with a “dread disease.”

The paper explores an important insurance phenomenon – heavy‐tailed/infinite‐mean losses – that is insufficiently discussed.

The proliferation of terrorism worldwide raises the risk that terrorist strategies could evolve from conventional methods (e.g. suicide attacks) to biological, chemical and even radioactive and nuclear attacks (commonly abbreviated as CBRN) which are potentially much more dangerous. The authors make three contributions toward a better understanding of this risk and how it responds to counterterrorism measures.

The authors develop a game that captures the terrorists’ potential strategic substitution between conventional and CBRN-type attacks; the authors calibrate the parameters of the game to real data using a novel calibration method and a partially unique dataset; they estimate the heavy-tailed distribution of attack severity and thus the probability of a successful attack, the underlying effort to launch an attack and the intrinsic difficulty of launching different types of attacks.

The authors find that in equilibrium, CBRN attacks, though less likely and more difficult to execute, are more deadly. In the end, the trade-off between, on one hand, the greater difficulty of carrying out a CBRN attack, and on the other, the greater deadliness of such an attack, points to a level of optimal counterterrorism spending by governments that weighs toward defending against CBRN attacks. The authors discuss these results and compare them with the actual level of counterterrorism spending by the US Government.

The framework of the game allows for substitution between the conventional and CBRN weapon types. These aspects of this paper, together with the unique calibration methodology, and the use of some unique terrorism data for the first time, are what distinguish this work from similar game theoretic papers in this area.

This paper researches a bidirectional volatility transmission effect between stocks and exchange rate markets in the six East European and Eurasian countries.

Research process involves creation of transitory and permanent volatilities via optimal component generalized autoregressive heteroscedasticity (CGARCH) model, while these volatilities are subsequently embedded in Markov switching model.

This study’s results indicate that bidirectional volatility transmission exists between the markets in the selected countries, whereas the effect from exchange rate to stocks is stronger than the other way around in both short-term and long-term. In particular, the authors find that long-term spillover effect from exchange rate to stocks is stronger than the short-term counterpart in all countries, which could suggest that flow-oriented model better explains the nexus between the markets than portfolio-balance approach. On the other hand, short-term volatility transfer from stock to exchange rate is stronger than its long-term equivalent.

This suggests that portfolio-balance theory also has a role in explaining the transmission effect from stock to exchange rate market, but a decisive fact is from which direction spillover effect is observed.

This paper is the first one that analyses the volatility nexus between stocks and exchange rate in short and long term in the four East European and two Eurasian countries.

The authors test the granularity hypothesis to international inflation spillovers using annual exports and inflation data for 138 countries from 1991 to 2020. This study aims to discuss the aforementioned objective.

First, the authors quantify the power law for the right tail of the export volumes distribution and discuss its implications. Then, the authors compute the granular residual, a measure of shocks to the largest countries.

The authors find export volumes across countries are not Gaussian-distributed but follow a power law. This finding means the largest countries disproportionately impact world inflation. In addition, the authors find that countries with higher relative weight in international trade determine a portion of international spillovers greater than their trade share. Moreover, eight big grains are responsible for the bulk of inflation spillovers.

The policy implication is that other countries' central banks should closely monitor the eight big grains when conducting their domestic monetary policy.

This is the first study spotting the problem of granular inflation spillovers.

This study aims to investigate the benefits of commodity hedging in the global stock index, bond and foreign currency (FX) portfolios.

The authors compare various hedging strategies and factor transaction costs. The authors analyze equally weighted, dynamic hedging ratio, risk parity and reward to risk timing strategies. Volatilities are estimated using historical, GARCH(1,1), and APARCH(1,1) methods. In addition, the authors evaluate the portfolio's hedging performance (HP) based on four different dimensions: volatility (annualized standard deviation), Sharpe ratio (SR), HP, and high-low ratio (HL).

The authors observe different benefits of the commodity hedging strategy among financial assets (stocks, bonds or FX).The authors find that commodity hedging in the stock markets is the best option, if the authors optimize the hedging ratio using dynamic hedging from historical data. The authors also document that for stock portfolio managers, adding commodities will generate a more conservative strategy, whereas for bond and/or FX portfolio managers, adding commodities will generate a more aggressive strategy.

This study contributes to the literature by investigating commodity hedging in the global stock index, bond and FX portfolios. First, the authors provide details on the diversification benefits in the commodities. Second, the authors document the hedging strategy that is the best as a part of the diversification strategy by adding commodities. Third, the authors provide a practical analysis by reporting the financial assets portfolio that is appropriate for commodity hedging following the portfolio managers' objectives (e.g. reducing risks or improving the risk-reward ratio).

This paper aims to identify and quantify directional predictability between returns and volume in major cryptocurrencies markets.

The empirical analysis relies on the cross-quantilogram approach that allows one to assess the temporal (lag-lead) association between two stationary time series at different parts of their joint distribution. The data are daily prices and trading volumes from four markets (Bitcoin, Ethereum, Ripple and Litecoin).

Extreme returns either positive or negative tend to lead high volume levels. Low levels of trading activity have in general no information content about future returns; high levels, however, tend to precede extreme positive returns.

This is the first work that uses the cross-quantilogram approach to assess the temporal association between returns and volume in cryptocurrencies markets. The findings provide new insights about the informational efficiency of these markets and the traders’ strategies.

Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include: