Abderrahim Taamouti
Biographic Data
| ID | 8920151 |
|---|---|
| NAME | Abderrahim Taamouti |
| GIVEN NAMES | Abderrahim |
| FAMILY NAME | Taamouti |
| SIGNATURE | TAAMOUTI A |
| AFFILIATIONS | Department of Economics and Finance, Durham University Business School, Durham DH1 3LB, UK () |
| ORCID | 0000-0002-1360-8803 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Value‐at‐Risk under Measurement Error
We propose a method for estimating Value‐at‐Risk that corrects for the effect of measurement errors in stock prices. We show that the presence of measurement errors might pose serious problems for estimating risk measures. In particular, when stock prices are contaminated, existing estimators of Value‐at‐Risk are inconsistent and might lead to an underestimation of risk, which can result in extreme leverage ratios within the held portfolios. Usin…
Testing for Asymmetric Comovements
This paper aims to provide non‐parametric tests for asymmetric comovements between random variables. We consider the popular Cramér‐von Mises and Kolmogorov–Smirnov test statistics based on the distance between positive and negative joint conditional exceedance distribution functions. These tests can capture both linear and nonlinear dependence in the data and do not require selecting kernel functions and bandwidths. We derive the asymptotic dist…
Covid‐19 Control and the Economy
Hard lockdowns have left policymakers to face the ethical dilemma of choosing between saving lives and saving the economy. However, massive testing could have helped to respond more effectively to Covid‐19 crisis. In this paper, we study the trade‐off between infection control, lockdown and testing. The aim is to understand how these policies can be effectively combined to contain Covid‐19 without damaging the economy. An extended SIR epidemic mo…
Measuring Granger Causality in Quantiles
We consider measures of Granger causality in quantiles, which detect and quantify both linear and nonlinear causal effects between random variables. The measures are based on nonparametric quantile regressions and defined as logarithmic functions of restricted and unrestricted expectations of quantile check loss functions. They can consistently be estimated by replacing the unknown expectations of check loss functions by their nonparametric kerne…
Measuring Nonlinear Granger Causality in Mean
We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametri…
Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality
This article proposes a new nonparametric test for conditional independence that can directly be applied to test for Granger causality. Based on the comparison of copula densities, the test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the time series data. In fact, to apply the test, only a bandwidth is neede…
No prominent works on this page.
Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality
This article proposes a new nonparametric test for conditional independence that can directly be applied to test for Granger causality. Based on the comparison of copula densities, the test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the time series data. In fact, to apply the test, only a bandwidth is neede…
Measuring Nonlinear Granger Causality in Mean
We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametri…
Covid‐19 Control and the Economy
Hard lockdowns have left policymakers to face the ethical dilemma of choosing between saving lives and saving the economy. However, massive testing could have helped to respond more effectively to Covid‐19 crisis. In this paper, we study the trade‐off between infection control, lockdown and testing. The aim is to understand how these policies can be effectively combined to contain Covid‐19 without damaging the economy. An extended SIR epidemic mo…
Measuring Granger Causality in Quantiles
We consider measures of Granger causality in quantiles, which detect and quantify both linear and nonlinear causal effects between random variables. The measures are based on nonparametric quantile regressions and defined as logarithmic functions of restricted and unrestricted expectations of quantile check loss functions. They can consistently be estimated by replacing the unknown expectations of check loss functions by their nonparametric kerne…
Testing for Asymmetric Comovements
This paper aims to provide non‐parametric tests for asymmetric comovements between random variables. We consider the popular Cramér‐von Mises and Kolmogorov–Smirnov test statistics based on the distance between positive and negative joint conditional exceedance distribution functions. These tests can capture both linear and nonlinear dependence in the data and do not require selecting kernel functions and bandwidths. We derive the asymptotic dist…
Value‐at‐Risk under Measurement Error
We propose a method for estimating Value‐at‐Risk that corrects for the effect of measurement errors in stock prices. We show that the presence of measurement errors might pose serious problems for estimating risk measures. In particular, when stock prices are contaminated, existing estimators of Value‐at‐Risk are inconsistent and might lead to an underestimation of risk, which can result in extreme leverage ratios within the held portfolios. Usin…
Econometrics (5 works) · Financial Risk and Volatility Modeling (5 works) · Mathematics (5 works) · Statistics (5 works) · Economics (4 works) · Estimator (3 works) · Monte Carlo method (3 works) · Nonparametric statistics (3 works) · Computer Science (2 works) · Granger causality (2 works)