Browsing by Author "Kabir MH"

Now showing 1 - 3 of 3
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    Adjusted air pollution exposure and corporate innovation investment: Evidence from China
    (Wiley, 2025-06-19) Liu J; Chi J; Kabir MH; Hafeez B
    Using a novel measure of air pollution exposure adjusted for the heterogeneity of exposures and the extent of local air pollution, we find a significant negative relationship between adjusted air pollution exposure and corporate innovation investment. This finding still holds after controlling for endogeneity and conducting a series of robustness tests. While the relationship is mediated through net operating cash flows and debt financing costs, we also find that firms with high adjusted air pollution exposure might have deteriorated productivity of R&D personnel, which ultimately hinders innovation input and output. However, state ownership appears to mitigate this adverse effect of adjusted air pollution exposure. Furthermore, the adverse effects of air pollution exposure on innovation investment are more pronounced among firms that disclose environmental information, exhibit low managerial risk tolerance, operate in non-polluting industries, or are located in developed and less polluted regions. Additionally, the negative impact is particularly evident in the subsample of firms after the signing of the 2015 Paris Agreement. This study sheds light on the importance of adjusted air pollution exposure and its influence on corporate investment in China.
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    An evaluation of the adequacy of Lévy and extreme value tail risk estimates
    (BioMed Central Ltd on behalf of the Southwestern University of Finance and Economics, 2024-12) Mozumder S; Hassan MK; Kabir MH
    This study investigates the simplicity and adequacy of tail-based risk measures—value-at-risk (VaR) and expected shortfall (ES)—when applied to tail targeting of the extreme value (EV) model. We implement Lévy–VaR and ES risk measures as full density-based alternatives to the generalized Pareto VaR and the generalized Pareto ES of the tail-targeting EV model. Using data on futures contracts of S&P500, FTSE100, DAX, Hang Seng, and Nikkei 225 during the Global Financial Crisis of 2007–2008, we find that the simplicity of tail-based risk management with a tail-targeting EV model is more attractive. However, the performance of EV risk estimates is not necessarily superior to that of full density-based relatively complex Lévy risk estimates, which may not always give us more robust VaR and ES results, making the model inadequate from a practical perspective. There is randomness in the estimation performances under both approaches for different data ranges and coverage levels. Such mixed results imply that banks, financial institutions, and policymakers should find a way to compromise or trade-off between “simplicity” and user-defined “adequacy”.
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    On practitioners closed-form GARCH option pricing
    (Elsevier Inc, 2024-07) Mozumder S; Frijns B; Talukdar B; Kabir MH
    This paper proposes a practitioner version of Heston and Nandi's (2000) (HN) model, which we term the Practitioner's Heston Nandi, or PHN model. We compare the option pricing and hedging performance of the PHN model vis-à-vis the HN model. Instead of using a one-period ahead volatility forecast for all options used in calibrations at any given time, the PHN model proposes using forward-looking ad-hoc volatilities (implied by market option prices) for each individual option and maturity in calibration and hedging. Since the proposed PHN model uses only option price data, it renders historical stock price data redundant, cutting the data requirement in derivative valuation. We employ options traded at CBOE for the period January 1, 2016 to December 31, 2018 and show that the proposed PHN model yields quick calibration and significantly improves pricing and hedging for European-style options.