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Trading VIX derivatives : trading and hedging strategies using VIX futures, options, and exchange-traded notes
\"Trading VIX Derivatives will be a comprehensive book covering all aspects of the Chicago Board Options Exchange stock market volatility index. The book will explain the mechanics and strategies associated with trading VIX options, futures, exchange trading notes and options on exchange traded notes. Known as the \"fear index\" the VIX provides a snapshot of expectations about future stock market volatility and generally moves inversely to the overall stock market. As such, many market participants look at the VIX to help understand market sentiment and predict turning points. With a slew of VIX index trading products now available, there are a variety of strategies traders use to speculate outright on the direction of market volatility or to use the products in conjunction with other instruments to create spread trades or hedge their overall risk. A top instructor at the CBOE's Options Institute, the author will reflect the wide range of uses associated with the VIX and will make the book useful to both new traders and seasoned professionals\"-- Provided by publisher.
Book
Banking on Deposits: Maturity Transformation without Interest Rate Risk
We show that maturity transformation does not expose banks to interest rate risk—it hedges it. The reason is the deposit franchise, which allows banks to pay deposit rates that are low and insensitive to market interest rates. Hedging the deposit franchise requires banks to earn income that is also insensitive, that is, to lend long term at fixed rates. As predicted by this theory, we show that banks closely match the interest rate sensitivities of their interest income and expense, and that this insulates their equity from interest rate shocks. Our results explain why banks supply long-term credit.
Journal Article
European Option Pricing under Sub-Fractional Brownian Motion Regime in Discrete Time
In this paper, the approximate stationarity of the second-order moment increments of the sub-fractional Brownian motion is given. Based on this, the pricing model for European options under the sub-fractional Brownian regime in discrete time is established. Pricing formulas for European options are given under the delta and mixed hedging strategies, respectively. Furthermore, European call option pricing under delta hedging is shown to be larger than under mixed hedging. The hedging error ratio of mixed hedging is shown to be smaller than that of delta hedging via numerical experiments.
Journal Article
Discovering interlinkages between major cryptocurrencies using high-frequency data: New evidence from COVID-19 pandemic
Through the application of the VAR-AGARCH model to intra-day data for three cryp-tocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.
Journal Article
Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging
\"Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python -- Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts\"-- Provided by publisher.
Book
Financial and energy exchange traded funds futures: an evidence of spillover and portfolio hedging
This paper examines spillover from financial exchange-traded funds (ETF) futures to energy ETF futures using adjusted daily data extending from April 2, 2009, to November 23, 2020. We also explore the portfolio hedging-based conditional variance and co-variance derived from dynamic conditional correlation. The proxies for the financial ETF futures are financial select sector SPDR fund (XLF) and generic 1st S&P 500 index futures (SP1) while generic 1st crude oil WTI futures (CL1), generic 1st natural gas futures (NG1), and energy select SPDR fund (XLE) are proxies of energy ETF futures. The results obtained from Granger causality indicate that there is unidirectional causality from RXLF to RSP1 while bidirectional causality between RXLF and RCL1 at a 5% significance level. Further, dynamic conditional correlation indicates the spillover effect from RXLF to RCL1, RXLF to RXLE, RSP1 to RCL1, and RSP1 to RXLE both in the short-run and long run. The spillover from RXLF to RNG1 is witnessed only in the short run while the spillover from RSP1 to RNG1 is present in long run. The present study corroborates with the studies of Chang et al. (Int J Finan Stud 6(2): 1–24, 2018) and Lau et al. (Int Rev Finan Anal 52: 316-332, 2017). We notice that the average optimal hedge ratio of the RXLF/RNG1 pair is the most expensive while the cheapest hedging strategy is of RSP1/RCL1 pair.
Journal Article
Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?
This paper investigates out-of-sample performance of the naïve hedging strategy relative to that of the minimum variance hedging strategy, in which the covariance parameters are estimated from 18 econometric models. Hedging performance is compared across 24 futures markets. Our main findings suggest that it is difficult to find a strategy under the minimum variance framework that outperforms the naïve hedging strategy both consistently and significantly. Our findings are robust to different sample periods, estimation windows, and hedging horizons and can be partly explained by the effects of estimation error and model misspecification.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2014.2028
.
This paper was accepted by Itay Goldstein, finance.
Journal Article