North American Journal of Economics and Finance最新文献

This paper studies the impact of volatility risk premium of SSE 50 ETF options on the price volatility of the underlying securities. After dividing options into different attributes including different types, market conditions and moneyness, we find that options contain implicit volatility information for the underlying securities and volatility risk premium has a significantly positive impact on the realized volatility. We further discuss the effect of options with different attributes on good and bad volatilities. The empirical results show that volatility risk premium has significant influence on both of them. In particular, the impact of volatility risk premium on good volatility is significantly stronger than that of bad volatility. In addition, we investigate the out-of-sample forecast performance of the volatility risk premium on realized volatility. The results show that the implied information content of deep out-of-the-money options has the highest prediction accuracy for weekly good realized volatility.

Synchronization is a phenomenon in which a pair of fluctuations adjusts their rhythms when interacting with each other. We measure the degree of synchronization between the U.S. dollar (USD) and euro exchange rates and between the USD and Japanese yen exchange rates based on purchasing power parity (PPP) over time. We employ a method of synchronization analysis using the Hilbert transform and find that the degree of synchronization is high most of the time, which suggests the establishment of PPP. The degree of synchronization does not remain high across periods with asymmetric economic events such as the U.S. real estate bubble.

This paper investigates the frequency and quantile connectedness between oil market shocks and sovereign credit risk of seven major oil exporting countries: Saudi Arabia, Russia, the United Arab Emirates, Norway, the United States, Brazil, and Mexico. We apply the time-domain approach of Diebold & Yılmaz (2012), the frequency-domain approach of Baruník & Křehlík (2018), and the quantile-based connectedness approach of Ando et al. (2018). Empirical results indicate that (i) spillover effects vary significantly across different investment horizons, with Mexico, Brazil, and Saudi Arabia emerging as key transmitters of credit risk volatility, (ii) the United Arab Emirates consistently appears as a major net receiver of these risks, highlighting its vulnerability to external shocks, (iii) in both short-term and long-term horizon, demand shocks stand out as the most influential determinants of volatility in sovereign credit risks, and (iv) during periods of heightened credit risk perception, the exacerbating role of oil demand shocks becomes more pronounced..

This paper focuses on the pricing problem of generalized variance swaps with jump risk in the underlying asset price under a stochastic liquidity model. We obtain a pricing formula of generalized variance swap in a jump–diffusion model with stochastic liquidity risk by the joint moment generating function (MGF) generated by solving the partial integral differential equation (PIDE). Using asymptotic analysis, we also demonstrate that as the sampling interval approaches zero, the pricing formula of discretely sampled generalized variance swap tends to be that of continuously sampled generalized variance swap. Finally, to verify the feasibility of the pricing formula of the generalized variance swap presented in this paper, we conduct some numerical experiments, including a comparison with the results of Monte Carlo (MC) simulation, the impact of various model parameters on the delivery prices of generalized variance swaps, and empirical research using actual market data.

We apply the nonparametric methodology of Jiang (2003) to test the market liquidity timing skills across individual equity mutual funds in three countries (the US, UK, and China). We calculate the monthly stock market liquidity using simple averages (across stocks) as well as the asymptotic principal component analysis (APCA) method based on six stock liquidity measures. Using an across-measure of market liquidity from APCA, we find a relatively small number of funds demonstrate statistically positive liquidity timing skills at a 5% significance level for the period of 2000–2021. After controlling for lagged market liquidity information, we still find a small number of mutual funds that have conditional liquidity timing ability using the nonparametric method.

This study developed a green financial indicator system based on a Principal Component Analysis (PCA) approach to investigate the impacts of green finance on carbon emissions by using the panel data from 30 Chinese provinces and municipalities from 2014 to 2021. The key findings conclude that green investments and debts are effective at reducing emissions, while green equity has a limited impact. Empirical results demonstrate that green finance enhances innovation efficiency, which in turn facilitates reductions in emissions. Additionally, the Spatial Durbin Model indicates significant spatial spillover effects, with progress in one province benefiting neighboring areas. The policy implications of the study suggest that enhancing green equity and adapting finance strategies to regional conditions could significantly advance the carbon reduction objectives for China.

Financial time series are dynamic and influenced by different types of information from the market. In this study, we propose new models for SPX and VIX options using the Hawkes process, jump process with stochastic intensity, and tempered stable process to capture these changes in financial time series based on three distinct characteristics of market information. We calculate the VIX option pricing formula using these models and find that the simplified VIX model based on VIX characteristics has significantly less pricing error than the consistent VIX model derived from the SPX model. Additionally, our findings suggest that the tempered stable process effectively models the volatility of VIX, sparse large jumps, and infinitesimal jumps. It also shows potential as an alternative to Brownian motion for representing volatility. Conversely, jump processes with stochastic jump intensities adeptly describe asymmetric jumps, and their integration with Brownian motion provides a more accurate depiction of the VIX’s volatility and jump dynamics. Finally, the introduction of jump processes into mean-reverting models for the VIX indicates a relatively low correlation between volatility magnitudes and the current VIX levels. This research contributes to the theory of SPX and VIX options and offers guidance for the development of other information-driven economic models.

The asset pricing literature has been producing increasingly complex and computationally intensive models of stock returns. Separately, professional analysts’ forecast stock returns. Are the sophisticated methods found in the asset pricing literature achieving different forecasts to those of analysts?’ Do the two forecasts’ even capture the same information? In this paper, I hypothesize that analyst forecasts and forecasts constructed using option prices will be different because they place different weights on available information. Using hypothesis tests and quantile regressions, I find that option-based forecasts are statistically significantly different from analyst forecasts at every level of the forecast distribution. Using cross-sectional regressions, I find that the difference originates in the weighting structure of the information sets used to create the forecasts: option-based forecasts incorporate information about the probability of extreme events more heavily while analyst forecasts focus on information about firm and macroeconomic fundamentals.

In this paper, we examine the dynamic behaviour of the US stock market due to the subsequent impact of the COVID-19 outbreak and the war in Ukraine. To that end, we analyse daily data of Dow Jones Industrial Average returns from 2 January 1900 to 31 October 2022. Firstly, we identify past crisis episodes similar to the current situation. Then, we compare the volatility dynamics, variation-fluctuation correlation functions, and correlation with uncertainty indicators with those induced by the COVID-19 epidemic and the subsequent Russo-Ukrainian conflict. Our findings suggest that the consecutive occurrence of these unexpected events has had more severe adverse effects on the US stock market than those recorded in similar past episodes. Additionally, we found that the events are highly correlated with indicators of economic policy uncertainty and financial market fear.

In light of the intricate nature of global fund markets, investors need securities that enable them to manage the values of foreign funds adjusted by exchange rates, commonly referred to as quanto fund values. This paper delves into the development of contracts that protect quanto fund values through partial lookback participation and their valuations. In order to accomplish this, we derive a generalized analytical expected value of a function of state variables and partial extreme, which serves to streamline the process of developing and pricing exotic quanto fund protections. These pricing formulas are useful in determining fair participation rates for a preferred return during a monitoring period. Numerical experiments that showcase the properties of the proposed contracts are provided.