Risk management of precious metals

This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested, and the best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs. The risk-minimizing portfolio weights and dynamic hedge ratios between different metal groups are also analyzed.risk management;value-at-risk;conditional volatility;precious metals

It Pays to Violate: How Effective are the Basel Accord Penalties?

The internal models amendment to the Basel Accord allows banks to use internal models to forecast Value-at-Risk (VaR) thresholds, which are used to calculate the required capital that banks must hold in reserve as a protection against negative changes in the value of their trading portfolios. As capital reserves lead to an opportunity cost to banks, it is likely that banks could be tempted to use models that underpredict risk, and hence lead to low capital charges. In order to avoid this problem the Basel Accord introduced a backtesting procedure, whereby banks using models that led to excessive violations are penalised through higher capital charges. This paper investigates the performance of five popular volatility models that can be used to forecast VaR thresholds under a variety of distributional assumptions. The results suggest that, within the current constraints and the penalty structure of the Basel Accord, the lowest capital charges arise when using models that lead to excessive violations, thereby suggesting the current penalty structure is not severe enough to control risk management. In addition, this paper suggests an alternative penalty structure that is more effective at aligning the interests of banks and regulators.GARCH;risk management;forecasting;Value-at-Risk (VaR);Basel accord penalties;simulations;violations

Statistical Modelling of Extreme Rainfall in Taiwan

In this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model. The non-stationary model means that the parameter of location of the GEV distribution is formulated as linear and quadratic functions of time to detect temporal trends in the maximum rainfall. Future behavior refers to the return level and the return period of the extreme rainfall. The 10, 20, 50 and 100-years return levels and their 95% confidence intervals of the return levels stationary models are provided. The return period is calculated based on the record-high (ranked 1st) extreme rainfall brought by the top 10 typhoons for each station in Taiwan. The estimates show that non-stationary model with increasing trend is suitable for the Kaohsiung, Hengchun, Taitung and Dawu stations. The Kaohsing and Hengchun stations have greater trends than the other two stations, showing that the positive trend extreme rainfall in the southern region is greater than in the eastern region of Taiwan. In addition, the Keelung, Anbu, Zhuzihu, Tamsui, Yilan, Taipei, Hsinchu, Taichung, Alishan, Yushan and Tainan stations are fitted well with the Gumbel distribution, while the Sun Moon Lake, Hualien and Chenggong stations are fitted well with the GEV distributio

Structure and Asymptotic theory for Nonlinear Models with GARCH Errors

Nonlinear time series models, especially those with regime-switching and conditionally heteroskedastic errors, have become increasingly popular in the economics and finance literature. However, much of the research has concentrated on the empirical applications of various models, with little theoretical or statistical analysis associated with the structure of the processes or the associated asymptotic theory. In this paper, we first derive necessary conditions for strict stationarity and ergodicity of three different specifications of the first-order smooth transition autoregressions with heteroskedastic errors. This is important, among other reasons, to establish the conditions under which the traditional LMlinearity tests based on Taylor expansions are valid. Second, we provide sufficient conditions for consistency and asymptotic normality of the Quasi- Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH errors

Modelling and Simulation: An Overview

The papers in this special issue of Mathematics and Computers in Simulation cover the following topics: improving judgmental adjustment of model-based forecasts, whether forecast updates are progressive, on a constrained mixture vector autoregressive model, whether all estimators are born equal: the empirical properties of some estimators of long memory, characterising trader manipulation in a limit-order driven market, measuring bias in a term-structure model of commodity prices through the comparison of simultaneous and sequential estimation, modelling tail credit risk using transition matrices, evaluation of the DPC-based inclusive payment system in Japan for cataract operations by a new model, the matching of lead underwriters and issuing firms in the Japanese corporate bond market, stochastic life table forecasting: a time-simultaneous fan chart application, adaptive survey designs for sampling rare and clustered populations, income distribution inequality, globalization, and innovation: a general equilibrium simulation, whether exchange rates affect consumer prices: a comparative analysis for Australia, China and India, the impacts of exchange rates on Australia's domestic and outbound travel markets, clean development mechanism in China: regional distribution and prospects, design and implementation of a Web-based groundwater data management system, the impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence, and coercive journal self citations, impact factor, journal influence and article influence

How Volatile is ENSO for Global Greenhouse Gas Emissions and the Global Economy?

This paper analyzes two indexes in order to capture the volatility inherent in El Niños Southern Oscillations (ENSO), develops the relationship between the strength of ENSO and greenhouse gas emissions, which increase as the economy grows, with carbon dioxide being the major greenhouse gas, and examines how these gases affect the frequency and strength of El Niño on the global economy. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately, and that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for Sea Surface Temperature (SST) are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with even stronger El Nino or La Nina effects in the future if global greenhouse gas emissions continue to increase unabated

Statistical Modelling of Recent Changes in Extreme Rainfall in Taiwan

This paper has two primary purposes. First, we fit the annual maximum daily rainfall data for 6 rainfall stations, both with stationary and non-stationary generalized extreme value (GEV) distributions for the periods 1911-2010 and 1960-2010 in Taiwan, and detect the changes between the two phases for extreme rainfall. The non-stationary model means that the location parameter in the GEV distribution is a linear function of time to detect temporal trends in maximum rainfall. Second, we compute the future behavior of stationary models for the return levels of 10, 20, 50 and 100-years based on the period 1960-2010. In addition, the 95% confidence intervals of the return levels are provided. This is the first investigation to use generalized extreme value distributions to model extreme rainfall in Taiwan

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

We introduce a new approach for computing optimal equilibria and mechanisms via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensive-form zero-sum game. This reformulation allows us to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning