Stochastic interest rate modelling using a single or multiple curves: an empirical performance analysis of the Lévy forward price model

In current financial markets negative interest rates have become rather persistent, while in theory it is often common practice to discard such rates as incredible and irrelevant. However, from a risk management perspective, it is crucially important to financial institutions to properly account for this phenomenon in their Asset Liability Management (ALM) studies. In this paper, we develop a coherent framework on how to best incorporate negative interest rates in these studies through a single curve stochastic term structure model and compare it to its multiple curve analogue. It turns out that, from the wide range of available single curve models, especially the Lévy Forward Price model (LFPM) of Eberlein and Özkan [The Lévy LIBOR model. Financ. Stoch., 2005, 9, 327–348] seems appropriate for ALM purposes. This paper describes an optimisation routine for calibrating this LFPM under the risk-neutral measure in both the single and multiple curve framework to the market prices of interest rate caplets with different strike rates, maturities and tenors. In addition, an empirical performance analysis is made of the single and multiple curve LFPM, where we include four deterministic volatility specifications and provide an explicit parametrisation of a piecewise homogeneity restriction with both deterministic and random breakpoints. This comparative analysis indicates that both the single and multiple curve LFPM is best adopted with the Linear-Exponential Volatility (LEV) specification and that deterministic breakpoints should be included, rather than random breakpoints.     

Improved recession dating using stock market volatility

We offer an improved dating of U.S. business cycle turning points both retrospectively and in real time. This improvement is made possible by augmenting existing Markov-switching dynamic factor models with additional information on the stock return volatility. The model improves the prediction of the state of the economy using fully revised data significantly. Real-time identification can be made noticeably earlier than the NBER announcements, beating both the peak and trough announcements for recent recessions by several months.     

A comparison principle between rough and non-rough Heston models—with applications to the volatility surface

We present a number of related comparison results, which allow one to compare moment explosion times, moment generating functions and critical moments between rough and non-rough Heston models of stochastic volatility. All results are based on a comparison principle for certain non-linear Volterra integral equations. Our upper bound for the moment explosion time is different from the bound introduced by Gerhold, Gerstenecker and Pinter [Moment explosions in the rough Heston model. Decisions in Economics and Finance, 2019, 42, 575–608] and tighter for typical parameter values. The results can be directly transferred to a comparison principle for the asymptotic slope of implied variance between rough and non-rough Heston models. This principle shows that the ratio of implied variance slopes in the rough versus non-rough Heston model increases at least with power-law behavior for small maturities.     

When do informed traders acquire and trade on informational advantage? Evidence from Federal Reserve stress tests

Using Federal Reserve bank stress test announcements, we examine when option traders acquire informational advantage and when they exploit it. We find consistent evidence of informed options trading around announcements. However, when test results are announced in successive weeks we find high abnormal option volume, considerably positive abnormal returns and significant return predictability in the first week, but not the following week. This suggests that informed option traders are able to anticipate upcoming news events and skillfully process public information but it also suggests that trading on acquired information is conditioned on the level of information asymmetry in the market.     

Systemic risk in global volatility spillover networks: Evidence from option-implied volatility indices

With option-implied volatility indices, we identify networks of global volatility spillovers and examine time-varying systemic risk across global financial markets. The U.S. stock market is the center of the network and plays a dominant role in the spread of volatility spillover to other markets. The global systemic risks have intensified since the Federal Reserve exited from quantitative easing, hiked interest rate, and shrank its balance sheet. We further show that the U.S. monetary tightening is an important catalyst for the intensifying global systemic risk. Our findings highlight the pernicious effects of monetary tightening after an era of cheap money.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

The term structure of volatility predictability

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper aims to fill these gaps in the literature. We begin this paper by introducing the notions of spot and forward predicted volatilities and propose describing the term structure of volatility predictability by spot and forward forecast accuracy curves. Then, we perform a comprehensive study of the term structure of volatility predictability in stock and foreign exchange markets. Our results quantify the volatility forecast accuracy across horizons in two major markets and suggest that the horizon of volatility predictability is significantly longer than that reported in earlier studies. Nevertheless, the aforesaid horizon is observed to be much shorter than the longest maturity of traded derivative contracts.     

Informed options trading on the implied volatility surface: A cross-sectional approach

This study investigates the cross-sectional implication of informed options trading across different strikes and maturities. We explore the term structure perspective of the one-way information transmission from options markets to stock markets by adopting well-known option-implied volatility measures to examine stock return predictability. Using equity options data for U.S. listed stocks spanning 2000–2013, we find that the shape of the long-term implied volatility curve exhibits extra predictive power for stock returns of subsequent months even after orthogonalizing the short-term components. Our findings indicate that the inter-market information asymmetry rapidly disappears before the expiration of long-term option contracts.     

A threshold stochastic volatility model with explanatory variables

In this paper, we introduce a threshold stochastic volatility model with explanatory variables. The Bayesian method is considered in estimating the parameters of the proposed model via the Markov chain Monte Carlo (MCMC) algorithm. Gibbs sampling and Metropolis–Hastings sampling methods are used for drawing the posterior samples of the parameters and the latent variables. In the simulation study, the accuracy of the MCMC algorithm, the sensitivity of the algorithm for model assumptions, and the robustness of the posterior distribution under different priors are considered. Simulation results indicate that our MCMC algorithm converges fast and that the posterior distribution is robust under different priors and model assumptions. A real data example was analyzed to explain the asymmetric behavior of stock markets.     

Flow toxicity of high-frequency trading and its impact on price volatility: Evidence from the KOSPI 200 futures market

We examine the relation between high-frequency trading, flow toxicity, and short-term volatility during both normal and stressful periods. Using transaction data for the Korea Composite Stock Price Index 200 (KOSPI 200) futures, we find the Volume-Synchronized Probability of Informed Trading (VPIN) useful in measuring flow toxicity as it predicts short-term volatility effectively. We further show that high-frequency trading is negatively related to VPIN and short-term volatility during normal times but has a positive association during stressful periods. Finally, we advocate the use of bulk-volume classification (BVC) by presenting evidence that the initiator identified by BVC trades at more favorable prices than the true trade initiator.