Annals of Financial Economics

This study investigates the dynamic relationship between exchange rates, oil prices, and stock prices in the Indian market, considering bearish, bullish, and neutral market states. By utilizing the Quantile ARDL method, we explore long- and short-run relationships in differing market states. The variations in sensitivities of oil prices and exchange rates with stock prices across diverse quantiles reflect significant variations in the Indian equity markets. Importantly, our study provides actionable insights for policymakers and stakeholders within India, empowering them with specific strategies for managing currency, stock prices, and oil prices under diverse market states. These insights are not just theoretical but directly applicable to real-world economic and financial decision-making, enhancing the ability of policymakers and stakeholders to navigate the complex Indian market.

We have read many papers in the literature and found that some papers report results of regressing a stationary time series on a non-stationary time series (we call it the $IOI1$ model). However, very few studies, if there are any, examine the $IOI1$ model and the robustness of inference in such settings remains an open question. To bridge the gap in the literature, in this paper, we investigate whether regressing a stationary time series, $Y_t$, on a non-stationary time series, $X_t$ (that is, $Y_t=\alpha +\beta X_t+u_t$) could get any meaningful result. To do so, we first conduct a simulation and find regressing a stationary time series on a non-stationary time series could be spurious. Thereafter, we develop the estimation and testing theory for the $I0I1$ model and find that the statistics $T_N^{\beta }$ for testing $H_0^{\beta }:\beta ={\beta }_0$ versus $H_1^{\beta }:\beta \ne {\beta }_0$ from the traditional regression model (we call it $IOI0$ model) does not have any asymptote distribution with $E(T_N^{\beta })\to \infty$ and $\mathrm{\text{Var}}(T_N^{\beta })\to \infty$ as $N\to \infty$, and thus, it cannot be used for the $I0I1$ model. We have found other interesting results as shown in our paper. Thus, our paper extends the spurious regression literature to cover a previously unexplored case, thereby contributing to a more comprehensive understanding of time series modeling and inference.

In this study, we investigate how adding Bitcoin can influence the investment portfolios. For this purpose, we consider a portfolio including Bitcoin and the five major sector indices of the Tehran Stock Exchange (TSE). At first, the asset returns are predicted through an estimation model based on higher moments. In the second step, the properties of Bitcoin in the face of other assets in a portfolio are studied by the asymmetric dynamic conditional correlation (ADCC) model. Then, the optimal weights in the portfolios are estimated. Accordingly, we used four portfolio optimization models with different objective functions, including a hybrid function of the higher moments, predicted risk from the ADCC model, and maximizing Sharpe and Sortino ratios. The out-of-sample results showed the relative efficiency of the proposed model in predicting the asset returns in Tehran Stock Exchange. In addition, the results of the ADCC model showed that Bitcoin plays a risk-hedging role for the pharmaceutical and banking sectors in TSE. We also know Bitcoin as a safe haven for the banking, petrochemical, metals, automobile, and pharmaceutical sectors. The results of portfolio selection also prove the effectiveness of adding Bitcoin with a maximum weight of 10% in the investment portfolios.

Deposit insurance is a critical instrument in modern financial systems for protecting depositors’ interests and promoting financial stability. The deposit insurance finance model, which is based on the standard power option pay-off, has been considered in this paper using the Picard–Lindelöf Iteration Method (PIM). Utilizing the repetitive framework of the Picard–Lindelöf iteration approach offers insights into the dynamic behavior of deposit insurance premiums and risk assessments. The study highlights the significance of using the Picard iteration technique to better understand deposit insurance pricing and its implications for financial institutions and regulatory bodies.

In this paper, we establish a stochastic integral, relying on the order-complete vector lattices’ properties. This stochastic integral is alike to the Riemann integral on bounded real-valued functions. This integral’s properties provide stochastic integration of stochastic processes beyond integral, with respect to some Brownian motion. The Arrhenius equation, which is actually an essential equation in Physical Chemistry, does provide a capital transfer model between two assets in a specific portfolio. The Arrhenius equation is actually modified under this stochastic integral. The stochastic term of such a stochastic process is alike to the introduction of the “white noise” in stochastic integration, with respect to some Brownian motion.

This paper examines how changes in two measures of market fear in the US — the VIX (volatility) index and the dollar index — affect equity returns in emerging markets from March 11, 2005, to May 5, 2023. We analyze daily data from two exchange-traded funds (ETFs) representing major emerging countries from different continents, which have not been studied before. We observe that during times of global uncertainty, investors often turn to the US dollar as a safe haven. This shift leads to capital leaving emerging markets, resulting in lower stock returns and increased market volatility. Our findings show that a higher VIX index significantly and asymmetrically reduces average returns in emerging markets, while a lower VIX index has a comparatively smaller positive impact. In contrast, the influence of changes in the US dollar index on emerging markets is consistent and symmetric, regardless of which ETF is considered. Moreover, our results indicate that considering both indices together provides a more accurate prediction of equity returns in emerging markets.