Regimes in CDS Spreads: A Markov Switching Model of iTraxx Europe Indices

This paper investigates the determinants of the iTraxx CDS Europe indices, finding strong evidence that they are regime dependent. During volatile periods credit spreads become highly sensitive to stock volatility and more sensitive to this than to stock returns. They are also almost immune to interest rates changes. During tranquil periods credit spreads are more sensitive to stock returns than to volatility and most indices are sensitive to interest rate moves. However for companies in the financial sector interest rates have no significant influence in either regime. We also found some evidence that raising interest rates can decrease the probability of credit spreads entering a volatile period. Our findings are useful for policy makers and, since equity hedge ratios based on single-state models cannot capture the regime dependent behaviour of credit spreads, our results may also help traders to improve the efficiency of hedging credit default swaps. Finally, the volatility clustering and autocorrelation that we have identified in the price dynamics of iTraxx indices should prove useful for pricing the iTraxx options that are now being actively traded over-the-counter.iTraxx, Credit Default Swap Index, Markov Switching, Credit Spreads

Equity index variance: evidence from flexible parametric jump–diffusion models

This paper analyzes a wide range of flexible drift and diffusion specifications of stochastic-volatility jump-diffusion models for daily S&P 500 index returns. We find that model performance is driven almost exclusively by the specification of the diffusion component whereas the drift specifications is of second-order importance. Further, the variance dynamics of non-affine models resemble popular non-parametric high-frequency estimates of variance, and their outperformance is mainly accumulated during turbulent market regimes. Finally, we show that jump diffusion models yield more reliable estimates for the expected return of variance swap contracts

The Role of Binance in Bitcoin Volatility Transmission

We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance

Option pricing of earnings announcement risks

This paper uses option prices to learn about the equity price uncertainty surrounding information released on earnings announcement dates. To do this, we introduce reduced-form models and estimators to separate price uncertainty regarding earnings announcements from normal day-to-day volatility. Empirically, we find strong support for the importance of earnings announcements. We find that the anticipated price uncertainty is quantitatively large, varies across time, and is informative about the future return volatility. Finally, we quantify the impact of earnings announcements on formal option pricing models

VIX derivatives, hedging and vol-of-vol risk

We study the empirical hedging performance of alternative VIX option pricing models. Recent advances in the literature find evidence of asymmetric volatility-of-volatility (similar to the leverage effect in equity markets), stochastic mean-reversion and jumps. Using such findings in our model framework, we show that while sophisticated models have superior pricing performance and can explain a range of stylized facts in the VIX derivatives market, their hedging performance is inferior to a simple Black model hedge. We also study the empirical performance of regime-dependent hedge ratio adjustments commonly applied in equity markets

A parsimonious parametric model for generating margin requirements for futures

Major exchanges employ the Standard Portfolio Analysis of Risk (SPAN) software to measure maintenance margins. However, its methodology has become cumbersome and opaque, having evolved over several decades and by now it requires that several hundred parameter values are re-set every day. We present a new, parsimonious parametric model for calculating margin requirements for futures which has a rigorous econometric foundation, being derived entirely from the median tail loss (MTL) of the returns distribution. This facilitates maximum likelihood volatility model calibration and state-of-the-art backtests. Then the parameters of the margin scheme which overlays the MTL may be calibrated using a variety of objectives. We examine three such objectives, including two which are designed to generate margins which mimic SPAN

Price impact versus bid-ask spreads in the index option market

We investigate the puzzle of why bid-ask spreads of options are so large by focussing on the price impact component of the spread. We propose a structural vector autoregressive model for trades in the option market to analyze whether they move the underlying price and/or the underlying’s volatility. Our model captures cross-option strategies by pooling order flows across contracts after a decomposition into exposure to the underlying asset and its volatility. While our estimates confirm that S&P500 option trades indeed significantly move the underlying and the volatility, the economic magnitudes are very small. Hence, large bid-ask spreads of options remain a puzzle

Continuous-time VIX dynamics: on the role of stochastic volatility of volatility

This paper examines the ability of several different continuous-time one- and two-factor jump-diffusion models to capture the dynamics of the VIX volatility index for the period between 1990 and 2010. For the one-factor models we study affine and non-affine specifications, possibly augmented with jumps. Jumps in one-factor models occur frequently, but add surprisingly little to the ability of the models to explain the dynamic of the VIX. We present a stochastic volatility of volatility model that can explain all the time-series characteristics of the VIX studied in this paper. Extensions demonstrate that sudden jumps in the VIX are more likely during tranquil periods and the days when jumps occur coincide with major political or economic events. Using several statistical and operational metrics we find that non-affine one-factor models outperform their affine counterparts and modeling the log of the index is superior to modeling the VIX level directly

Variance-of-variance risk premium

This article explores the premium for bearing the variance risk of the VIX index, called the variance-of-variance risk premium. I find that during the sample period from 2006 until 2014 trading strategies exploiting the difference between the implied and realized variance of the VIX index yield average excess returns of − 24.16% per month, with an alpha of − 16.98% after adjusting for Fama–French and Carhart risk factors as well as accounting for variance risk (both highly significant). The article provides further evidence of risk premium characteristics using corridor variance swaps and compares empirical results with the predictions of reduced-form and structural benchmark models

Model complexity and out-of-sample performance: evidence from S&P 500 index returns

We apply a range of out-of-sample specification tests to more than forty competing stochastic volatility models to address how model complexity affects out-of-sample performance. Using daily S&P 500 index returns, model confidence set estimations provide strong evidence that the most important model feature is the non-affinity of the variance process. Despite testing alternative specifications during the turbulent market regime of the global financial crisis of 2008, we find no evidence that either finite- or infinite-activity jump models or other previously proposed model extensions improve the out-of-sample performance further. Applications to Value-at-Risk demonstrate the economic significance of our results. Furthermore, the out-of-sample results suggest that standard jump diffusion models are misspecified