Exploit Arbitrage Opportunities Research Articles

How to Miss a Free Lunch

This month look at different ways � some failing � to exploit arbitrage opportunities

Neural networks and arbitrage in the VIX

The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors’ sentiment, representing the market’s expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential strategy to exploit it requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors’ knowledge, this the first paper, that describes a new methodology on how to predict the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.

Neural Networks and Arbitrage in the VIX

The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors' sentiment, representing the market's expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. Over the last few years, many claims about possible VIX manipulations have been brought up by market participants. The increased attention on the VIX has been revived again by unusual trading patterns, which were observed on the market, on the 5th of February and 18th of April, 2018. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential replication strategy requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors' knowledge, this the first paper, that describes a new methodology on how to replicate the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.

Evolving Efficiency of Exchange Rate Movement: An Evidence from Indian Foreign Exchange Market

This study empirically verifies the evolving and time-varying efficiency of Indian foreign exchange market using the framework of adaptive market hypothesis (AMH). Whether market efficiency is time varying or static, and if time varying, identification of possible events causing such time-varying efficiency are the two major agenda of this study. We employ a set of recent methods which are robust and possess stronger power properties. Moreover, we follow a fixed-length rolling window approach to explore time-varying nature of market efficiency and to avoid data-snooping bias. Our overall findings suggest that market efficiency is not an all-or-nothing condition; it varies over time. We also find that episodes of efficiency coincide with emergence of major events and market microstructure issues. Particularly, changes in exchange rate regime, financial turbulence, major central bank interventions and trade volume are the prominent causes for time-varying efficiency in INR–USD exchange rate. The evidence of swing between efficiency and inefficiency can prompt currency traders to exploit arbitrage opportunities that emerge with different market conditions.

Virtual Bidding and Financial Transmission Rights: An Equilibrium Model for Cross-Product Manipulation in Electricity Markets

Virtual transactions are financial positions that allow market participants to exploit arbitrage opportunities arising when day-ahead electricity prices are predictably higher or lower than expected real-time prices. Unprofitable virtual transactions may be used to move day-ahead prices in a direction that enhances the value of related positions, like financial transmission rights (FTRs). This constitutes cross-product manipulation, and has emerged as a policy concern of the Federal Energy Regulatory Commission in recent years. Absent control over real-time prices, what economic conditions enable a financial market participant to manipulate day-ahead electricity prices? We develop a three-stage equilibrium model to study cross-product manipulation in FTR and two-settlement energy markets, and evaluate its effects on price convergence and other market outcomes using numerical simulations. Our model accounts for demand uncertainty, the likelihood of congestion in both day-ahead and real-time, and transmission capacity constraints at all stages in the game. Numerical results in a two-node setting show that day-ahead price manipulation through virtual transactions to profit from FTR positions is sustained only when generators and traders compete in a Cournot game in the day-ahead market. In contrast, this type of manipulation fails when the FTR holder is the only market participant acting strategically, and generation capacity and credit requirement constraints for other parties are not binding. In the Appendix, we derive conditions for the existence of the day-ahead equilibria and the single-leader multi-follower equilibrium of the three-stage game.

Neural Networks and Arbitrage in the VIX – A Deep Learning Approach for the VIX

The Chicago Board Options Exchange (CBOE) Volatility Index, often referred to as VIX Volatility Index (VIX), is considered by many market participants as a common measure of market risk and investors' sentiment. It is also sometimes called the fear index. In general, the VIX represents the market's expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. Over the last few years, many claims about possible VIX manipulations have been brought up by market participants. The increased attention on the VIX has been revived again by unusual trading patterns, which were observed on the market, on February 5 and April 18, 2018. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. In theory, such large deviations might lead to arbitrage opportunities on the VIX market. However, it is hard to exploit as the potential replication strategy requires buying several hundred out-of-the-money (put and call) options on the S&P 500 index. In addition, the potential list of options used for building the replication strategy constantly changes due to underlying price movements, making it difficult to implement it in real-time. Finally, in most cases, the theoretical replication strategy involves high transaction costs which are driven by illiquid options. This paper discusses a novel approach to replicating and predicting the VIX by using just a subset of the most liquid options. The presented approach is based on a recurrent neural network, more precisely on a long short-term model (LSTM) and it uses intraday data of S&P 500 options and the VIX. The results can be used to find a much more cost-efficient way of replicating the VIX and exploiting any arbitrage opportunities. To the best of the authors' knowledge, this the first paper, that describes a new methodology on how to replicate the VIX (to potentially exploit arbitrage opportunities using VIX futures) and applies most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the ongoing discussions about possible market manipulations, help other investors to better understand the market and support regulators to investigate market inefficiencies.

Feedback trading in stock index futures: Evidence from South Africa

This study tests whether investors and speculators in stock index futures contracts on the South African stock market use feedback trading strategies. Feedback trading can be destabilizing and impede on the risk mitigation and price discovery functions of futures contracts. Using the Sentana and Wadhwani (1992) model, and accounting for the global financial crisis, we find no evidence of feedback trading in the Top40 futures index or the Top40 mini futures index contracts. Our findings have important implications for investors who wish to use index futures to mitigate risk or exploit arbitrage opportunities and regulators concerned about the destabilizing effects of futures trading.

Arbitrage Opportunities for Pumped Storage Power Plants in Switzerland

Over the past decade Switzerland has invested significantly in pumped storage plants (PSP). They aim to exploit arbitrage opportunities by pumping water from a lower reservoir to an upper reservoir to store electricity in the form of hydraulic potential energy when prices are low and generating when prices are high. The changing generation-mix in Switzerland threatens these arbitrage opportunities in the long-term. To study this, we develop a heuristic to endogenise PSP bidding decisions, and integrate it in [1]’s simulation model of the Swiss electricity market. Our results show that initially the increase of photovoltaic capacity encourages pumping, but the nuclear phase-out and the expiration of long-term import contracts significantly decrease the available energy, leading to a decline in pumping. Although those changes in the capacity-mix increase the difference between peak and off-peak prices significantly, PSP are unable to exploit these because of the low availability of cheap energy to pump. This situation severely limits arbitrage opportunities in the long-term. We conclude that large scale arbitrage requires the availability of cheap excess energy. This can be achieved either by demand management or by supporting base load technologies.

Optimal scheduling of independently operated, locally controlled energy storage systems as dispatchable assets in a competitive electricity market

Independently operated energy storage can exploit arbitrage opportunities available due to inter-temporal variation of electricity prices. Storage can be utilised as a dispatchable or non-dispatchable asset. In this study, a new optimal scheduling algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by independent system operators (ISOs). Storage in this case is referred to as dispatchable storage. In addition, a new index is proposed to measure the storage dispatchability. While the operation of locally controlled storage is optimally scheduled at the owner's end, using the proposed algorithm, storage is fully dispatchable at the ISO's end. Dispatchable storage units have great potential to enhance the flexibility of electric grids and are key elements envisioned to enable smart grid realisation. The proposed algorithm outperforms previous algorithms in which storage is either locally controlled at the owner's end and cannot optimally accept ISO's instructions; or storage is centrally controlled by the grid operator to achieve some technical objectives. The efficacy and feasibility of the proposed algorithm are validated using real-world data. It is demonstrated that the proposed algorithm can enable the storage to accept all ISO's instructions. Revenue values of dispatchable and non-dispatchable storage are computed and compared.

A simulation framework for uneconomic virtual bidding in day-ahead electricity markets

About two thirds of electricity consumers in the United States are served by Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs). One of their primary responsibilities is the operation of organized auctions for purchasing and selling electricity that have a two-settlement structure with coordinated day-ahead (DA) and real-time (RT) energy markets. The DA market takes place on the day before the actual power dispatch, and creates a financial obligation to deliver and withdraw power from the transmission grid. In contrast, the RT energy market is a physical market where predicted and actual supply and demand of electricity are balanced on the delivery day. Purely financial transactions, known as virtual bids, were introduced in wholesale electricity markets to allow participants (including energy traders that do not control generation assets or serve load) to exploit arbitrage opportunities arising from expected price differences between day-ahead and real-time energy markets and to enhance convergence between DA and RT prices. More specifically, virtual demand (supply) bids are financial positions for the purchase (sale) of energy in the DA market, which are settled with a countervailing offer to sell (buy) at the RT price without the bidder taking title to physical electricity. Virtual demand bids are typically referred to as DECs, while virtual supply bids are known as INCs. Virtual bids clear with generation and load bids in the DA market, and may set the DA market-clearing price. Virtual bids have strong interactions with other elements of the electricity market design. For instance, Financial Transmission Rights (FTRs) are financial contracts to hedge transmission congestion between two nodes in the transmission network (a source and a sink defined in the contract), and entitle their holders the right to collect a payment when day-ahead congestion arises between the source and the sink [1]. Since FTRs settle at the day-ahead prices, virtual bids could be placed in the day-ahead energy market in order to affect day-ahead electricity prices in a direction that enhances the value of the FTRs. In our study, we consider a model of the DA electricity market at any node in the network. Market participants include power generators and loads submitting physical bids, and financial players placing virtual bids. Virtual bids affect the DA market clearing prices, but we assume that they have no impact on RT prices. Theoretical results on interior Nash equilibria are given, assuming that virtual bidders can perfectly predict RT prices and hold no FTRs [2] sinking at the node. We then adopt a hypergame framework [3] to model the DA market, assuming imperfect prediction of RT prices by different virtual bidders. When no market participant holds FTRs, virtual bidders help achieve convergence between DA and RT nodal prices, as expected [4]. In this setting, we also allow one virtual bidder to hold a FTR position sinking at the node. Our numerical results show that, with FTR as another source of revenue, the larger the FTR position, the greater the incentive for the FTR holder to place uneconomic virtual bids at the FTR sink to enhance the value of her financial position, in line with [5, 6]. We also show that the manipulation causes not only losses for other virtual bidders, but also the divergence between DA and RT prices. Methods for detecting such uneconomic bidding are also investigated. Our technical report is available at http://www.cse.psu.edu/research/publications/tech-reports/2016/CSE-16-003.pdf.

Cross-border commuting and consuming: an empirical investigation

ABSTRACTThis article contributes to our understanding of cross-border activity in general and the determinants of cross-border trade in particular by focusing on the part of cross-border sales that arise due to work-related cross-border crossings of households. We analyse empirically how cross-border consumption expenditures vary across product and services categories as well as household characteristics using representative household survey data of cross-border commuters to Luxembourg. In total, these households spend an estimated 17% of their gross annual income across the border, contributing about 10% to the total household final consumption expenditure (HFCE) in Luxembourg. Cross-border expenditure is linked to individual- and household-related characteristics and to distance between home and work. Cross-border commuters systematically exploit arbitrage opportunities that arise because of existing price level (index) differences between the country of work and the country of residence.

Online Adaptive Real-Time Optimal Dispatch of Privately Owned Energy Storage Systems Using Public-Domain Electricity Market Prices

This paper aims to evaluate and improve the usefulness of publicly available electricity market prices for real-time optimal dispatching (RTOD) of a privately owned energy storage system (ESS) in a competitive electricity market. The RTOD algorithm seeks to maximize the revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day-ahead market. The pre-dispatch prices, issued by the Ontario independent electricity system operator, and the corresponding ex-post hourly Ontario energy prices are employed as the forecast and the actual prices. A compressed-air ESS is sized and employed for evaluations due to its lower capital expenditure and its ability to be positively influenced by the availability of waste heat. First, the conventional RTOD algorithm is developed by formulating a mixed integer linear programming problem. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices available before real-time. The outcomes reveal that the proposed adaptive RTOD can significantly increase the ESS revenue compared to the conventional algorithm as well as the back-casting method proposed in prior studies.

A practical approach to constructing price-based funding liquidity factors

The paper proposes a computationally convenient and parsimonious approach for creating a funding liquidity factor, building on work that relates the lack of funding of financial institutions to their ability to exploit arbitrage opportunities. Fontaine and Garcia (2012) propose a price-based funding liquidity factor from mispricing of bonds of similar characteristics but different ages. However, their arbitrage-free Nelson–Siegel framework requires the use of a non-linear Kalman filter, which is computationally intensive in practice. The novelty of this paper is to suggest an easier method for constructing an alternative liquidity factor that retains much of the same properties. Our proposed method for constructing this proxy liquidity factor relaxes the arbitrage-free assumption in the specification of the term structure model and bases it on a simple and flexible term structure specification. We demonstrate that this parsimonious liquidity factor fits the data well. The constructed factor is highly correlated with the Fontaine and Garcia (2012) liquidity factor and other funding liquidity measures, such as a liquidity factor by Hu et al. (2012), the TED-spread and the CP-spread.

Real-Time Optimal Dispatch and Economic Viability of Cryogenic Energy Storage Exploiting Arbitrage Opportunities in an Electricity Market

In this paper, the economic viability and profitability of a newly emerging storage technology, i.e., cryogenic energy storage (CES), is investigated. A real-time optimal dispatching algorithm is proposed and developed to optimally dispatch a privately owned CES unit to generate revenue by exploiting arbitrage opportunities in the day-ahead/week-ahead electricity market. Due to its special characteristics, CES can provide significantly more financial and technical benefits in a weekly scheduling compared with common daily scheduling. The electricity price modulation is proposed as a new approach to competitively offer subsidy by the utility regulator to CES owners to fill the gap between current and a stable expected rate of return. Using real-world price data from the Ontario wholesale electricity market, the method is validated. The results reveal significant benefits of weekly usage as compared to daily usage of CES. The efficacy and feasibility of the proposed approach to subsidize CES owners are validated through simulation studies.

Too big to ignore? Hedge fund flows and bond yields

Abstract This paper investigates the information content of aggregate hedge fund flow and its predictive power with respect to bond yields. Using a sample of 9725 hedge funds from 1994 to 2012, we find that fund flow is negatively related to the changes in 10-year Treasury and Moody’s Baa bond yields one month ahead. This relation is still pronounced after controlling for other determinants of yield changes, including the amount of arbitrage capital available in the economy, suggesting a non-trivial effect of flow-induced hedge fund trading on bond yields. Flow impact on corporate bonds is further amplified during periods of decreasing market liquidity, consistent with a fire-sale hypothesis. Hedge fund flow also predicts convergence between constant maturity swap rate and constant maturity Treasury rate, as well as between the TIPS and Treasury bond yields, suggesting that hedge funds exploit arbitrage opportunities in these fixed-income markets.

Risk Measure Based Robust Bidding Strategy for Arbitrage Using a Wind Farm and Energy Storage

This paper proposes the use of a risk measure based robust optimization bidding strategy for dispatching a wind farm in combination with energy storage. Through coordination with energy storage devices, variable wind generators can be utilized as dispatchable energy producers in the deregulated electricity market. The total profit from sale of electricity can be increased by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day ahead market. A case study is presented to show that as the forecast error in electricity price increases, the robust optimization based bidding strategy has an increasing probability of yielding better economic performance than a deterministic optimization based bidding strategy. The uncertainty set for robust optimization is selected based on the coherent risk measure conditional value at risk (CVaR). Uncertainties in electricity price forecasting and wind power forecasting are considered. The resulting robust optimization based bidding strategy is evaluated using Monte Carlo simulation for different choices of uncertainty sets.

Market timing and trading strategies using asset rotation: non-neutral market positioning for exploiting arbitrage opportunities

We present empirical results on the statistical and economic viability of a market timing and trading strategy that is based on a pairwise rotation between two risky assets. Using data on equity exchange traded funds, and models for both the returns and the volatility of the underlying assets, we compare the performance of the suggested models with the standard benchmarks of a buy-and-hold strategy and an equally weighted portfolio. The underlying intuition for the use of such a strategy rests with literature on sign and volatility predictability. The rotation strategy, as we apply it in this paper, is not risk-neutral and assumes the presence of arbitrage opportunities in the markets and short-term trends. Furthermore, the model specification uses the interplay between relative returns and relative volatilities in picking-up the asset with the highest return. Our results show that even a naive model that is based on a moving average of relative returns can outperform both benchmarks and that more elaborate specifications for the rotation model may yield additional performance gains. We also find that, in many cases, the rotation strategy yields statistically significant sign predictions of the relative returns and volatility. While our results are conditional on the data that we have used in our analysis they, nevertheless, support the market-timing literature and show that an active trading strategy can be based on the concept of rotation.

Global Imbalances, Risk and the Great Recession

This paper describes a new analytical framework for the quantitative assessment of international external positions. The framework links each country’s current net foreign asset position to its current trade flows, forecasts of future trade flows, and expectations concerning future returns on foreign assets and liabilities in an environment where countries cannot run Ponzi schemes or exploit arbitrage opportunities in world financial markets. It provides guidance on how external positions should be measured in the data, and on how the sustainability of a country’s current position can be assessed. To illustrate its usefulness, I study the external positions of 12 countries (Australia, Canada, China, France, Germany, India, Italy, Japan, South Korea, Thailand, the United States and the United Kingdom) between 1970 and 2011. In particular, I examine how changes in the perceived risk associated with future returns across world financial markets contributed to evolution of external positions before the 2008 financial crisis, and during the ensuing Great Recession.

A Circulation Network Model for the Exchange Rate Arbitrage Problem

In this article we present a circulation network model for the detection of arbitrage opportunities in the currencies and securities markets. As an illustration we present its application to the interest rate of the Mexican and American bond market, the interbank loan rate of both countries, as well as to the deposits rate of US and Canada reported in Bloomberg. Deviations of covered interest rate parity imply that there exist a series of transactions that can be carried out to obtain riskless profits by exploiting arbitrage opportunities. The problem of finding arbitrage opportunities is modeled via a generalized maximum flow problem. The maximum flow over the generalized circulation network represents profits from arbitrage, and it’s obtained through the application of a minimum cost flow algorithm.

Study of the Tail Dependence Structure in Global Financial Markets Using Extreme Value Theory

Abstract: The presence of tail dependencies invalidates the multivariate normality assumptions in portfolio risk management. The identification of tail (in)dependencies has drawn major attention in empirical financial studies. Yet it is still a challenging issue both theoretically and practically. Previous studies based on either a restrictive model or the null hypothesis of tail (perfect) dependence does not well describe or interpret extreme co-movements in financial markets. This paper examines tail dependence structures underlying a broad range of financial asset classes employing the newly developed tail quotient correlation coefficients. In theory, the original tail quotient correlation coefficient proposed in (Zhang 2008) is adapted to incorporate cases with varying data driven random thresholds. Our empirical results demonstrate different tail dependence structures underlying various global financial markets. Either omission or unanimous treatment of the tail dependence structures for different financial markets will lead to erroneous conclusions or suboptimal investment choices. The multivariate extreme value theory framework in this study has the potential to serve as an useful tool in exploiting arbitrage opportunities, optimizing asset allocations, and building robust risk management strategies.