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Half-metallicity (HM) offers great potential for engineering spintronic applications, yet only few magnetic materials present metallicity in just one spin channel. In addition, most HM systems become magnetically disordered at temperatures well below ambient conditions, which further hinders the development of spin-based electronic devices. Here, we use first-principles methods based on density functional theory (DFT) to investigate the electronic, magnetic, structural, mixing, and vibrational properties of 90 XYZhalf-Heusler (HH) alloys (X=Li, Na, K, Rb, Cs; Y=V, Nb, Ta; Z=Si, Ge, Sn, S, Se, Te). We disclose a total of 28 new HH compounds that are ferromagnetic, vibrationally stable, and HM, with semiconductor band gaps in the range of 1–4 eV and HM band gaps of 0.2–0.8 eV. By performing Monte Carlo simulations of a spin Heisenberg model fitted to DFT energies, we estimate the Curie temperature, TC, of each HM compound. We find that 17 HH HM remain magnetically ordered at and above room temperature, namely, 300≤TC≤450K, with total magnetic moments of 2 and 4 μB. A further materials sieve based on zero-temperature mixing energies let us to conclude 5 overall promising HH HM that remain magnetically ordered at and above room temperature: NaVSi, RbVTe, CsVS, CsVSe, and RbNbTe. We also predict 2 semiconductor materials that are ferromagnetic at ambient conditions: LiVSi and LiVGe.

The purpose of this paper is to assess unbiased options pricing predictions via ad hocBlack–Scholes model approaches. This paper investigates a number of technical issues when fitted values of implied volatility from linear regression are plugged into the Black–Scholes model, which leads to biased estimation. First, the study observes that the implied volatility linear regression can yield a negative outcome, which is meaningless. Therefore, a logarithmic transformation is applied to the linear function to ensure that the forecast is positive. Second, the retransformation from log to original metric to fitted values of implied volatility and the nonlinearity of Black–Scholes to implied volatility yields biased forecasts. A smearing technique has been applied in this study to correct this bias. Finally, the smearing estimation method also provides biased results if there is heteroscedasticity in the OLS estimation residuals. This study applies the weighted least square regression technique in order to avoid heteroscedasticity. According to the performance measures such as mean bias (MB), mean absolute error (MAE) and mean absolute relative error, the study concludes that the smearing method is the most effective to correct the bias in ad hocBlack–Scholes approaches as well as that an absolute smile approach is better than a relative smile approach without the smearing technique, but with smearing methods, relative smile performs superior to absolute.