Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19.

In this paper, reparameterization and student-t are applied to Stochastic Volatility (SV) model. We aim to reduce the amount of autocorrelation of the SV parameters and to introduce heavy-tailed model via the Bayesian computation of the Markov Chain Monte Carlo (MCMC) samplers. This research paper helps support better MCMC estimation of the SV model for volatile Asian FX series during Covid-19.

Rare Disaster Events, Growth Volatility, and Financial Liberalization: International Evidence

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consump-tion) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

Systemic Risk of China's Financial Industry during the Spread of the COVID-19 Epidemic and the Breakdown of Crude Oil Negotiation

This research first adopts three indicators to measure the systemic risk of different financial industries in China. Second, we employ the Time Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-SV-VAR) model to investigate the time-varying relationship among COVID-19 epidemic, crude oil price, and financial systemic risk. The results herein not only help us grasp the current level of systematic risk in China, but also can assist at improving the early warning risk indicators and enhance the risk management system. Lastly, this research can also help investors to make reasonable asset planning.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility. © 2022

The European Central Bank and green finance: How would the green quantitative easing affect the investors' behavior during times of crisis?

In July 2021, the European central bank (ECB) announced the application of new environmental criteria to purchase private assets as part of its Quantitative Easing (QE) program. Using a Bayesian VAR model with time varying parameters and stochastic volatility (TVP-BVAR-SV), we investigate the transmission of Green bond shocks to the stock market during the pre-and-post COVID-19 pandemic. We document a nonlinear relation between the green bonds and the green equities. Our findings suggest that the ECB's Green QE can drive investors towards green investment in the stock market through the green bond market during the non-crisis period. However, we show that the proper transmission of Green QE shocks to the stock market depends on the economic conditions and could not be effective during the crisis period. Our results also support previous findings that state the growing demand for sustainable investing after COVID-19. These findings have important implications for investment professionals, policymakers, and environmentally concerned actors. © 2022 Elsevier Inc.

The Impact of Stochastic Volatility on Initial Margin and MVA for Interest Rate Derivatives

In this research we investigate the impact of stochastic volatility on future initial margin (IM) and margin valuation adjustment (MVA) calculations for interest rate derivatives. An analysis is performed under different market conditions, namely during the peak of the Covid-19 crisis when the markets were stressed and during Q4 of 2020 when volatilities were low. The Cheyette short-rate model is extended by adding a stochastic volatility component, which is calibrated to fit the EUR swaption volatility surfaces. We incorporate the latest risk-free rate benchmarks (RFR), which in certain markets have been selected to replace the IBOR index. We extend modern Fourier pricing techniques to accommodate the RFR benchmark and derive closed-form sensitivity expressions, which are used to model IM profiles in a Monte Carlo simulation framework. The various results are compared to the deterministic volatility case. The results reveal that the inclusion of a stochastic volatility component can have a considerable impact on nonlinear derivatives, especially for far out-of-the-money swaptions. The effect is particularly pronounced if the market exhibits a substantial skew or smile in the implied volatility curve. This can have severe consequences for funding cost valuation and risk management. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility.

The macroeconomic effect of COVID-induced economic policy uncertainty in Nigeria: a DSGE approach

Purpose This study examined the macroeconomic effects of COVID-19-induced economic policy uncertainty (EPU) in Nigeria. The study considered the effects of three related shocks: EPU, COVID-19 and correlated economic policy uncertainty and COVID-19 shock. Design/methodology/approach First, the study presented VAR evidence that fiscal and monetary policy uncertainty depresses real output. Thereafter, a nonlinear DSGE model with second-moment fiscal and monetary policy shocks was solved using the third-order Taylor approximation method. Findings The authors found that EPU shock is negligible and expansionary. By contrast, COVID-19 shocks have strong contractionary effects on the economy. The combined shocks capturing the COVID-19-induced EPU shock were ultimately recessionary after an initial expansionary effect. The implication is that the COVID-19 pandemic-induced EPU adversely impacted macroeconomic outcomes in Nigeria in a non-trivial manner. Practical implications The result shows the importance of policies to cushion the effect of uncertain fiscal and monetary policy path in the aftermath of COVID-19. Originality/value The originality of the paper lies in examining the impact of COVID-19 induced EPU in the context of a developing economy using the DSGE methodology.

Nowcasting GDP with a pool of factor models and a fast estimation algorithm

We propose a novel mixed-frequency dynamic factor model with time-varying parameters and stochastic volatility for macroeconomic nowcasting and develop a fast estimation algorithm. This enables us to generate forecast densities based on a large space of factor models. We apply our framework to nowcast US GDP growth in real time. Our results reveal that stochastic volatility seems to improve the accuracy of point forecasts the most, compared to the constant-parameter factor model. These gains are most prominent during unstable periods such as the Covid-19 pandemic. Finally, we highlight indicators driving the US GDP growth forecasts and associated downside risks in real time.

Total and Net-Directional Connectedness of Cryptocurrencies During the Pre- and Post-COVID-19 Pandemic

This paper presents how volatility propagates through the cryptocurrency market. Our paper provides evidence for volatility connectedness on cryptocurrencies. The different econometric techniques, including the stochastic volatility (SVOL) model and time-varying parameter VAR models using a quasi-Bayesian local likelihood (QBLL), are applied to measure the volatility of the cryptocurrency market. Using high-frequency, intra-day data of the largest cryptocurrencies over 2018-2021, we detect the great volatility of the cryptocurrency market are the beginning of 2019, the beginning of 2020, and throughout the year of 2021. The total connectedness values suggest that the cryptocurrency market becomes volatile as the new strains of the COVID-19 appear at the end of 2021. However, by using directional connectedness, we reveal that there are negative and positive spillovers from a specific cryptocurrency to other cryptocurrencies. The great fluctuations in the period before the COVID-19 health crisis stem from the positive resonance (symmetric) between the volatility of each cryptocurrency, while this health crisis leads to substantially positive and negative spillovers (asymmetric) of cryptocurrencies, and this makes market volatility weaker than it actually is.

A closed-form approximation formula for pricing European options under a three-factor model

The double-mean-reverting model, introduced by Gatheral [(2008). Consistent modeling of SPX and VIX options. In The Fifth World Congress of the Bachelier Finance Society London, July 18], is known to be a successful three-factor model that can be calibrated to both CBOE Volatility Index (VIX) and S&P 500 Index (SPX) options. However, the calibration of this model may be slow because there is no closed-form solution formula for European options. In this paper, we use a rescaled version of the model developed by Huh et al. [(2018). A scaled version of the double-mean-reverting model for VIX derivatives. Mathematics and Financial Economics 12: 495-515] and obtain explicitly a closed-form pricing formula for European option prices. Our formulas for the first and second-order approximations do not require any complicated calculation of integral. We demonstrate that a faster calibration result of the double-mean revering model is available and yet the practical implied volatility surface of SPX options can be produced. In particular, not only the usual convex behavior of the implied volatility surface but also the unusual concave down behavior as shown in the COVID-19 market can be captured by our formula.

Measuring Trend Inflation in India

Central to monetary policy is the concept of trend inflation to which actual inflation outcomes are expected to converge after short run fluctuations die out. Accordingly, the inflation target needs to be fixed in alignment with trend inflation to avoid unhinging inflation expectations and flattening the aggregate supply curve or imparting a deflationary bias to the economy. Results from a regime switching model applied to a hybrid New Keynesian Philips curve suggest a steady decline in trend inflation since 2014 to 4.1-4.3 per cent just before COVID-19 struck. This points to maintaining the inflation target at 4 per cent for India.

Amending the heston stochastic volatility model to forecast local motor vehicle crash rates: A case study of Washington, D.C

Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subject to peaks and troughs in traffic volume and crash events owing to exogenous events – for example, extreme weather – rather than typical commute patterns. One such area that is particularly exposed to exogenous events is Washington, D.C., which has seen a large rise in crash events between 2009 and 2020. In this study, we adopt a forecasting model that embeds heterogeneity and temporal instability in its estimates in order to improve upon forecasting models currently used in transportation and road safety research. Specifically, we introduce a stochastic volatility model that aims to capture the nuances associated with crash rates in Washington, D.C. We determine that this model can outperform conventional forecasting models, but it does not perform well in light of the unique travel patterns exhibited throughout the COVID-19 pandemic. Nevertheless, its adaptability to the idiosyncrasies of Washington, D.C. crash rates demonstrates its ability to accurately simulate localised crash rates processes, which can be further adapted in public policy contexts to form road safety targets. © 2022 The Author(s)

Stress Testing Option Sensitivities in a Stochastic Market

The Heston stochastic volatility model aims to parameterise the equity market with 5 specific parameters. It is arguably one of the most popular models used in option pricing, since it relaxes the Black–Scholes assumption of constant volatility, and can capture the observed equity skew. Another reason for its popularity is the fact that it has an analytical solution for European options and associated option sensitivities called the Greeks. In this paper, we analyse the sensitivity of the three main option sensitivities: Delta, Gamma, and Vega, to changes in market conditions. We specifically test what happens to each option sensitivity in a bear market—as we currently face in the wake of COVID-19. We find that the option sensitivities are linked to the Heston model parameters;therefore, the Heston model parameters should give market makers an idea of future option behaviour. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The COVID-19 pandemic haunting the transmission of the quantitative easing to the exchange rate.

The sudden spread of the COVID-19 pandemic disturbed the entire macroeconomic system and overturned the expectations of financial market participants and decision-makers. Using a TVP-BVAR-SV model, I investigate the transmission of the quantitative easing (QE) to the exchange rate and the business credit in the Eurozone during the pre-and post-COVID-19 outbreak. I find that the responses of the exchange rate EUR/USD to monetary policy shocks vary over time. In particular, I show that the QE policy does not generate the expected effect on the exchange rate during the COVID-19 pandemic period. The results imply that the unforeseen COVID-19 crisis has disturbed and modified investors' behavior.