Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis

Abstract The Scenario Weights for Importance Measurement (SWIM) package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti et al. (2019). SWIM provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-At-Risk and Expected Shortfall. SWIM operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. SWIM does not require additional evaluations of the simulation model or explicit knowledge of its underlying statistical and functional relations; hence, it is suitable for the analysis of black box models. The capabilities of SWIM are demonstrated through a case study of a credit portfolio model.

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Risk Analysis of Gold Prices in Pakistan Using Extreme Value Theory

Mathematical Problems in Engineering ◽

10.1155/2021/2704142 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Ghulam Raza Khan ◽

Alanazi Talal Abdulrahman ◽

Osama Alamri ◽

Zahid Iqbal ◽

Maqsood Ahmad

Keyword(s):

Extreme Value Theory ◽

Value At Risk ◽

Risk Measures ◽

Value Theory ◽

Extreme Value ◽

Return Level ◽

Extreme Stress ◽

Daily Data ◽

Peak Over Threshold ◽

The Impact

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution.

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Using sensitivity analysis to identify key factors for the propagation of a plant epidemic

Royal Society Open Science ◽

10.1098/rsos.171435 ◽

2018 ◽

Vol 5 (1) ◽

pp. 171435 ◽

Cited By ~ 6

Author(s):

Loup Rimbaud ◽

Claude Bruchou ◽

Sylvie Dallot ◽

David R. J. Pleydell ◽

Emmanuel Jacquot ◽

...

Keyword(s):

Sensitivity Analysis ◽

Stochastic Model ◽

Management Strategies ◽

Sensitivity Analyses ◽

Plum Pox Virus ◽

Design Management ◽

Strong Impact ◽

Key Factors ◽

Range Analysis ◽

The Impact

Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by plum pox virus , in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly connected patches whose infection could trigger serious epidemics.

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The impact of distribution on value-at-risk measures

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2011.06.053 ◽

2013 ◽

Vol 58 (9-10) ◽

pp. 1670-1676 ◽

Cited By ~ 10

Author(s):

David L. Olson ◽

Desheng Wu

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Measures ◽

The Impact

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VAR assessment under nongaussian distribution of returns

Российский технологический журнал ◽

10.32362/2500-316x-2020-8-2-67-84 ◽

2020 ◽

Vol 8 (2) ◽

pp. 67-84

Author(s):

A. Е. Barysheva ◽

A. S. Markov ◽

A. A. Mitcel

Keyword(s):

Value At Risk ◽

Empirical Distribution ◽

Risk Measures ◽

Statistical Tests ◽

Asset Returns ◽

Likelihood Method ◽

Significance Level ◽

Portfolio Returns ◽

True Value ◽

The Impact

The study aims to assess the impact of violation of the assumption about normality of the investment portfolio returns on its risk measures. The article is focused on the Value at Risk (VaR) metric required by major regulatory authorities for bank risk assessment. Using historical share prices of several Russian companies it is shown that the assumption about returns normality is not supported by statistical tests. It is also shown that the empirical distribution of the assets returns is described by Johnson’s distribution. The Kolmogorov-Smirnov test supports the obtained results. The tests proposed by the authors allow estimating the loss in accuracy in parameters calibration of the autoregressive model, obtained by using the maximum likelihood method when the asset returns have non-gaussian distribution. It was found that the loss in the accuracy lies in the range [22%, 26%] for absolute returns and in the range [33%, 38%] for relative returns depending on the autoregression parameter which varies in the range [–0.9, 0.9]. The error of ten-day VaR estimation was calculated for 1% (99%) and 5% (95%) significance levels. At a significance level of 5% (95%) the VaR metric obtained under the assumption that the asset returns have normal distribution is lower than the true value by 7% (6%) for absolute returns and 4% (13%) for relative returns, which indicates strong underestimation of the portfolio risk. At a significance level of 1% the metric is conservative exceeding the true value by 12.5%.

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Extreme Risk, Value-At-Risk And Expected Shortfall In The Gold Market

International Business & Economics Research Journal (IBER) ◽

10.19030/iber.v14i1.9035 ◽

2014 ◽

Vol 14 (1) ◽

pp. 107

Author(s):

Knowledge Chinhamu ◽

Chun-Kai Huang ◽

Chun-Sung Huang ◽

Delson Chikobvu

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Measures ◽

Effective Means ◽

Value Theory ◽

Expected Shortfall ◽

Extreme Stress ◽

Extreme Risk ◽

Gold Market ◽

The Impact

Extreme value theory (EVT) has been widely applied in fields such as hydrology and insurance. It is a tool used to reflect on probabilities associated with extreme, and thus rare, events. EVT is useful in modeling the impact of crashes or situations of extreme stress on investor portfolios. It describes the behavior of maxima or minima in a time series, i.e., tails of a distribution. In this paper, we propose the use of generalised Pareto distribution (GPD) to model extreme returns in the gold market. This method provides effective means of estimating tail risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). This is confirmed by various backtesting procedures. In particular, we utilize the Kupiec unconditional coverage test and the Christoffersen conditional coverage test for VaR backtesting, while the Bootstrap test is used for ES backtesting. The results indicate that GPD is superior to the traditional Gaussian and Students t models for VaR and ES estimations.

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The Association Between –Market Risk Disclosure Reporting And Firm Risk: The Impact Of SEC FRR No. 48

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v26i4.304 ◽

2010 ◽

Vol 26 (4) ◽

Cited By ~ 4

Author(s):

Chen-Miao Lin ◽

Wanda Lee Owens ◽

James E. Owers

Keyword(s):

Sensitivity Analysis ◽

Value At Risk ◽

Market Risk ◽

Risk Disclosure ◽

Cost Of Equity ◽

Total Risk ◽

Firm Risk ◽

Specific Risk ◽

Risk Cost ◽

The Impact

SEC FRR No. 48 requires that all firms report their market risk exposures using one or more of three alternative formats for disclosure: tabular format, sensitivity analysis, or Value at Risk (VaR). In this paper we examine how the method chosen affects a firm’s risk as measured by total risk, the cost of equity, and firm specific risk. We find that firms using VaR have higher total risk and firm specific risk than firms using sensitivity analysis. Conversely, firms employing tabular disclosure generally have lower but not statistically significant lower total risk, cost of equity, and firm specific risk than firms using sensitivity.

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Supplemental Material for The Impact of Measurement Error and Omitting Confounders on Statistical Inference of Mediation Effects and Tools for Sensitivity Analysis

Psychological Methods ◽

10.1037/met0000345.supp ◽

2020 ◽

Keyword(s):

Sensitivity Analysis ◽

Measurement Error ◽

Statistical Inference ◽

Mediation Effects ◽

The Impact

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Implicit and explicit corrective feedback and feedback exposure conditions

Instructed Second Language Acquisition ◽

10.1558/isla.38113 ◽

2020 ◽

Vol 4 (1) ◽

pp. 3-48

Author(s):

Takehiro Iizuka ◽

Kimi Nakatsukasa

Keyword(s):

Explicit Knowledge ◽

Corrective Feedback ◽

Significant Difference ◽

Oral Corrective Feedback ◽

Post Test ◽

Picture Description ◽

Potential Factors ◽

Implicit And Explicit ◽

Instructional Settings ◽

The Impact

This exploratory study examined the impact of implicit and explicit oral corrective feedback (CF) on the development of implicit and explicit knowledge of Japanese locative particles (activity de, movement ni and location ni) for those who directly received CF and those who observed CF in the classroom. Thirty-six college students in a beginning Japanese language course received either recast (implicit), metalinguistic (explicit) or no feedback during an information-gap picture description activity, and completed a timed picture description test (implicit knowledge) and an untimed grammaticality judgement test (explicit knowledge) in a pre-test, immediate post-test and delayed post-test. The results showed that overall there was no significant difference between CF types, and that CF benefited direct and indirect recipients similarly. Potential factors that might influence the effectiveness of CF, such as instructional settings, complexity of target structures and pedagogy styles, are discussed.

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