Strategic Timing of IPOs and Disclosure: A Dynamic Model of Multiple Firms

We study a dynamic timing game between multiple firms, who decide when to go public in the presence of possible information externalities. A firm's IPO pricing is a function of its privately observed idiosyncratic type and the level of investor sentiment, which follows a stochastic, mean-reverting process. Firms may wish to delay their IPOs in order to observe the market reception of the offerings of their peers. We characterize the unique symmetric threshold equilibrium, whereby pioneer firms with high idiosyncratic types endogenously emerge. The results provide novel implications regarding variation in IPO timing, sequential clustering, IPO droughts, the composition of new issues over time, and how IPO volume fluctuates over time. These include, among others, that in more populated industries, a lower proportion of firms emerge as industry pioneers, but follower IPO volume is intensified. Additionally, heightened uncertainty over investor sentiment exacerbates delay and leads to lower IPO volume.

The impact of COVID-19 on the Spanish tourism sector

We examine in this note the impact of COVID-19 on the Spanish tourism sector by using a strong dependence model. Daily data from five equity markets are used and we find that the coronavirus crisis has increased the persistence in the data, moving in some of the series from a mean reverting process to a non-mean reverting one. Thus, shocks that were expected to be transitory have become permanent, implying the need of strong policy measures to come the series back to their long-term projections.

Volatility in International Stock Markets: An Empirical Study during COVID-19

Predicting volatility is a must in the finance domain. Estimations of volatility, along with the central tendency, permit us to evaluate the chances of getting a particular result. Financial analysts are frequently challenged with the assignment of diversifying assets in order to form efficient portfolios with a higher risk to reward ratio. The objective of this research is to analyze the influence of COVID-19 on the return and volatility of the stock market indices of the top 10 countries based on GDP using a widely applied econometric model—generalized autoregressive conditional heteroscedasticity (GARCH). For this purpose, the daily returns of market indices from January 2019 to June 2020 were taken into consideration. The results reveal daily negative mean returns for all market indices during the COVID period (January 2020 to June 2020). Though the second quarter of the COVID period reflects a bounce back for all market indices with altered strengths, the volatility remains higher than in normal periods, signaling a bearish tendency in the market. The COVID variable, as an exogenous variance regressor in GARCH modeling, is found to be positive and significant for all market indices. Furthermore, the results confirmed the mean-reverting process for all market indices.

Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models

This study is set out to model and forecast the cryptocurrency market by concentrating on several stylized features of cryptocurrencies. The results of this study assert the presence of an inherently nonlinear mean-reverting process, leading to the presence of asymmetry in the considered return series. Consequently, nonlinear GARCH-type models taking into account distributions of innovations that capture skewness, kurtosis and heavy tails constitute excellent tools for modelling returns in cryptocurrencies. Finally, it is found that, given the high volatility dynamics present in all cryptocurrencies, correct forecasting could help investors to assess the unique risk-return characteristics of a cryptocurrency, thus helping them to allocate their capital.

Dynamic Modeling of Generic Body Temperatures with Mean-Reverting Process and a Martingale Estimation Function

In this paper a stochastic differential equation (SDE) model of generic body temperature (such as axilla, mouth, anus, etc.) fluctuation is developed. We consider a mean-reverting SDE process and use zero-mean martingale estimation function to get the parameters. Subsequently we use data generated from another dynamic model of core body temperature a ground truth for comparison with test our SDE model.

Pricing Vulnerable European Options under Lévy Process with Stochastic Volatility

This paper considers the pricing issue of vulnerable European option when the dynamics of the underlying asset value and counterparty’s asset value follow two correlated exponential Lévy processes with stochastic volatility, and the stochastic volatility is divided into the long-term and short-term volatility. A mean-reverting process is introduced to describe the common long-term volatility risk in underlying asset price and counterparty’s asset value. The short-term fluctuation of stochastic volatility is governed by a mean-reverting process. Based on the proposed model, the joint moment generating function of underlying log-asset price and counterparty’s log-asset value is explicitly derived. We derive a closed-form solution for the vulnerable European option price by using the Fourier inversion formula for distribution functions. Finally, numerical simulations are provided to illustrate the effects of stochastic volatility, jump risk, and counterparty credit risk on the vulnerable option price.

Are These Shocks for Real? Sensitivity Analysis of the Significance of the Wavelet Response to Some CKLS Processes

The CKLS process (introduced by Chan, Karolyi, Longstaff, and Sanders) is a typical example of a mean-reverting process. It combines random fluctuations with an elastic attraction force that tends to restore the process to a central value. As such, it is widely used to model the stochastic behaviour of various financial assets. However, the calibration of CKLS processes can be problematic, resulting in high levels of uncertainty on the parameter estimates. In this paper we show that it is still possible to draw solid conclusions about certain qualitative aspects of the time series, as the corresponding indicators are relatively insensitive to changes in the CKLS parameters.

Electricity Price Forecasting Using Monte Carlo Simulation: The Case of Lithuania

The main purpose of this article is to determine the practical use of the Monte Carlo simulations in electricity markets for forecasting future prices. First, we review the structure of the electricity markets – how they work, what implications do they have and how they’ve evolved during the last decades. Second, we discover that there are only few researches that have been made on this topic as well as there haven’t being made any researches regarding the Lithuanian electricity market. Then, we will carry out an analysis on how to use a Monte Carlo simulation approach in electricity markets. A Mean-Reverting process method will be introduced, which, at first, was used to predict oil prices. Also, we analyze the essence of price spikes and find a solution on how to predict them.