Does OTC derivatives reform incentivize central clearing?

Regulatory changes in the over-the-counter (OTC) derivatives market seek to reduce systemic risk. The reforms require that standardized derivatives be cleared through central counterparties (CCPs), and they set higher capital and margin requirements for non-centrally cleared derivatives. We investigate whether these requirements create a cost incentive in favor of central clearing, as intended. We compare the total capital and collateral costs when banks transact fully bilaterally and when they clear all contracts through CCPs. We calibrate our model using data on the OTC market collected by the Federal Reserve. We find that the cost incentive may not favor central clearing. The main factors driving the cost comparison are netting benefits, the margin period of risk, and CCP guarantee fund requirements. Lower guarantee fund requirements lower the cost of clearing but make CCPs less resilient.     

The Value of Central Clearing

I study a contracting innovation that suddenly insulated traders of hedging contracts against counterparty risk: central clearing counterparties (CCPs) for derivatives. The first CCP was created in Le Havre (France) in 1882, in the coffee futures market. Using triple difference-in-differences estimation, I show that central clearing changed the geography of trade flows Europe-wide, to the benefit of Le Havre. Inspecting the mechanism using trader-level data, I find that the CCP solved both a “missing market” problem and adverse selection issues. Central clearing also facilitated entry of new traders in the market. The successful contracting innovation quickly spread to other exchanges.     

Risk management strategies for banks

We analyze optimal risk management strategies of a bank financed with deposits and equity in a one period model. The bank’s motivation for risk management comes from deposits which can lead to bank runs. In the event of such a run, liquidation costs arise. The hedging strategy that maximizes the value of equity is derived. We identify conditions under which well known results such as complete hedging, maximal speculation or irrelevance of the hedging decision are obtained. The initial debt ratio, the size of the liquidation costs, regulatory restrictions, the volatility of the risky asset and the spread between the riskless interest rate and the deposit rate are shown to be the important parameters that drive the bank’s hedging decision. We further extend this basic model to include counterparty risk constraints on the forward contract used for hedging.     

模糊厌恶下股指期货风险对冲策略设计及实证分析

投资者对股指期货与现货有着不同的模糊厌恶,本文首先将此假设条件引入带交易成本的Garleanu and Pederson (2013)投资模型中,并以指数基金对冲策略为例,构建了一个股指期货动态对冲的理论模型。与非对冲策略相比,基于上述模型设计的对冲策略投资绩效更好,动态最优成交额占目标交易额的比例更小,目标成交额对收益率预测因子的敏感性更大。借助上述模型,本文选取2010年4月至2021年6月的中国ETF指数基金和股指期货数据,并以2015年9月股指期货管理措施实施为界进行区间划分,实证研究发现:(1)中国A股市场的ETF投资组合进行股指期货对冲显著提升了投资绩效,但股指期货管理会削弱该作用;(2)投资绩效改善主要来源于交易成本的下降与目标成交额因子敏感性的提升,该机制受到股指期货管理的约束;(3)与Garleanu and Pederson (2013)、Zhang et al. (2017)相比,本文对冲策略保留“抗跌”特点的同时增加了“易涨”特性。本文研究结果表明,在当前大力发展机构投资者的背景下应不断丰富股指期货、股指期权产品谱系,降低股指期货交易成本并完善持仓约束。     

Optimal hedging in an extended binomial market under transaction costs

We develop an approach to optimal hedging of a contingent claim under proportional transaction costs in a discrete time financial market model which extends the binomial market model with transaction costs. Our model relaxes the binomial assumption on the stock price ratios to the case where the stock price ratio distribution has bounded support. Non-self-financing hedging strategies are studied to construct an optimal hedge for an investor who takes a short position in a European contingent claim settled by delivery. We develop the theoretical basis for our optimal hedging approach, extending results obtained in our previous work. Specifically, we derive a no-arbitrage option price interval and establish properties of the non-self-financing strategies and their residuals. Based on the theoretical foundation, we develop a computational algorithm for optimizing an investor relevant criterion over the set of admissible non-self-financing hedging strategies. We demonstrate the applicability of our approach using both simulated data and real market data.     

A new computational tool for analysing dynamic hedging under transaction costs

This paper highlights a framework for analysing dynamic hedging strategies under transaction costs. First, self-financing portfolio dynamics under transaction costs are modelled as being portfolio affine. An algorithm for computing the moments of the hedging error on a lattice under portfolio affine dynamics is then presented. In a number of circumstances, this provides an efficient approach to analysing the performance of hedging strategies under transaction costs through moments. As an example, this approach is applied to the hedging of a European call option with a Black–Scholes delta hedge and Leland's adjustment for transaction costs. Results are presented that demonstrate the range of analysis possible within the presented framework.     

(S,s)-adjustment Strategies and Hedging under Markovian Dynamics

We study the destabilizing effect of hedging strategies under Markovian dynamics with transaction costs. Once transaction costs are taken into account, continuous portfolio rehedging is no longer an optimal strategy. Using a non-optimizing (local in time) strategy for portfolio rebalancing, explicit dynamics for the price of the underlying asset are derived, focusing in particular on excess volatility and feedback effects of these portfolio insurance strategies. Moreover, it is shown how these latter depend on the heterogeneity of the insured payoffs. Finally, conditions are derived under which it may be still reasonable, from a practical viewpoint, to implement Black–Scholes strategies.     

Asymptotic replication with modified volatility under small transaction costs

We consider the dynamic hedging of a European option under a general local volatility model with small proportional transaction costs. Extending the approach of Leland, we introduce a class of continuous strategies of finite cost that asymptotically (super-)replicate the payoff. An associated central limit theorem for the hedging error is proved. We also obtain an explicit trading strategy minimizing the asymptotic error variance.     

THE ECONOMIC VALUE OF USING REALIZED VOLATILITY IN FORECASTING FUTURE IMPLIED VOLATILITY

We examine the economic benefits of using realized volatility to forecast future implied volatility for pricing, trading, and hedging in the S&P 500 index options market. We propose an encompassing regression approach to forecast future implied volatility, and hence future option prices, by combining historical realized volatility and current implied volatility. Although the use of realized volatility results in superior performance in the encompassing regressions and out-of-sample option pricing tests, we do not find any significant economic gains in option trading and hedging strategies in the presence of transaction costs.     

Efficient analytic approximation of the optimal hedging strategy for a European call option with transaction costs

One of the most successful approaches to option hedging with transaction costs is the utility-based approach, pioneered by Hodges and Neuberger [Rev. Futures Markets, 1989, 8, 222–239]. Judging against the best possible trade-off between the risk and the costs of a hedging strategy, this approach seems to achieve excellent empirical performance. However, this approach has one major drawback that prevents the broad application of this approach in practice: the lack of a closed-form solution. We overcome this drawback by presenting a simple yet efficient analytic approximation of the solution. We provide an empirical testing of our approximation strategy against the asymptotic and some other well-known strategies and find that our strategy outperforms all the others.     

CCPs and network stability in OTC derivatives markets

Among the reforms to OTC derivative markets since the global financial crisis is a commitment to collateralize counterparty exposures and to clear standardized contracts via central counterparties (CCPs). The reforms aim to reduce interconnectedness and improve counterparty risk management in these important markets. At the same time, however, the reforms necessarily concentrate risk in one or a few nodes in the financial network and also increase institutions’ demand for high-quality assets to meet collateral requirements. This paper looks more closely at the implications of increased CCP clearing for both the topology and stability of the financial network. Building on Heath et al. (2013) and Markose (2012), the analysis supports the view that the concentration of risk in CCPs could generate instability if not appropriately managed. Nevertheless, maintaining CCP prefunded financial resources in accordance with international standards and dispersing any unfunded losses widely through the system can limit the potential for a CCP to transmit stress even in very extreme market conditions. The analysis uses the Bank for International Settlements Macroeconomic Assessment Group on Derivatives (MAGD) data set on the derivatives positions of the 41 largest bank participants in global OTC derivative markets in 2012.     

Deep hedging

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show in Section 4 that the set of constrained trading strategies used by our algorithm is large enough to ε-approximate any optimal solution. Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available. We illustrate our approach by an experiment on the S&P500 index and by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard ‘complete-market’ solution.     

Hedging errors with Leland's option model in the presence of transaction costs

Nonzero transaction costs invalidate the Black–Scholes [1973. Journal of Political Economy 81, 637–654] arbitrage argument based on continuous trading. Leland [1985. Journal of Finance 40, 1283–1301] developed a hedging strategy which modifies the Black–Scholes hedging strategy with a volatility adjusted by the length of the rebalance interval and the rate of the proportional transaction cost. Kabanov and Safarian [1997. Finance and Stochastics 1, 239–250] calculated the limiting hedging error of the Leland strategy and pointed out that it is nonzero for the approximate pricing of an European call option, in contradiction to Leland's claim. As a further contribution, we first identify the mathematical flaw in the argument of Leland's claim and then quantify the expected percentage of hedging losses in terms of the hedging frequency and the level of the option strike price.     

Efficient discretization of stochastic integrals

Sharp asymptotic lower bounds on the expected quadratic variation of the discretization error in stochastic integration are given when the integrator admits a predictable quadratic variation and the integrand is a continuous semimartingale with nondegenerate local martingale part. The theory relies on inequalities for the kurtosis and skewness of a general random variable which are themselves seemingly new. Asymptotically efficient schemes which attain the lower bounds are constructed explicitly. The result is directly applicable to a practical hedging problem in mathematical finance; for hedging a payoff which is replicated by a continuous-time trading strategy, it gives an asymptotically optimal way to choose discrete rebalancing dates and portfolios with respect to transaction costs. The asymptotically efficient strategies in fact reflect the structure of the transaction costs. In particular, a specific biased rebalancing scheme is shown to be superior to unbiased schemes if the transaction costs follow a convex model. The problem is discussed also in terms of exponential utility maximization.     

Bank monitoring incentives and optimal ABS

The paper examines a continuous-time delegated monitoring problem between competitive investors and an impatient bank monitoring a pool of long-term loans subject to Markovian “contagion.” Moral hazard induces a foreclosure bias unless the bank is compensated with the right incentive-compatible contract. Fees are paid when the bank’s performance is on target and liquidation arises when the bank’s performance is sufficiently poor. I show that the optimal contract can be implemented with a whole loan sale involving both credit risk retention based on ABS credit default swaps and credit enhancement in the form of a reserve account. The optimal securitization bears out rulemaking recently proposed in the wake of the Dodd-Frank Act on a number of controversial provisions. I argue that further efficiency gains could be reaped by extending the role of the “premium capture” account into a liquidity buffer capturing performance-based compensation as a way of increasing skin in the game over the life of the transaction.     

Dynamic hedging of single and multi-dimensional options with transaction costs: a generalized utility maximization approach

We propose a new methodology for discrete time dynamic hedging with transaction costs that has three key performance features. First, the methodology can accommodate the use of a wide range of objective functions, from the use of many types of utility functions to the more traditional objectives of hedging error minimization. Second, our methodology can significantly outperform traditional dynamic hedging methodologies across a range of objective functions. Third, our methodology can be applied to both single and multi-dimensional options while analytical methods typically can only be applied to single dimensional options.     

Agency Costs, Risk Management, and Capital Structure

The joint determination of capital structure and investment risk is examined. Optimal capital structure reflects both the tax advantages of debt less default costs (Modigliani and Miller (1958, 1963)), and the agency costs resulting from asset substitution (Jensen and Meckling (1976)). Agency costs restrict leverage and debt maturity and increase yield spreads, but their importance is small for the range of environments considered. Risk management is also examined. Hedging permits greater leverage. Even when a firm cannot precommit to hedging, it will still do so. Surprisingly, hedging benefits often are greater when agency costs are low.     

Commodities momentum: A behavioral perspective

The growth in commodity-related investments has sparked interest in the performance of momentum strategies in these markets. This paper introduces a behavioral proxy of the 52-week high and low momentum that explains a significant proportion of the variation of conventional momentum returns after controlling for commodity specific risk factors. Our findings show that the 52-week high strategy generates significant profits after accounting for transaction costs. We report that the 52-week high strategy is a better predictor of returns than conventional momentum. Our findings suggest that term structure and hedging pressure risk factors provide only a partial explanation of the results.     

Hedging efficiently under correlation

We show that when a derivative portfolio has different correlated underlyings, hedging using classical greeks (first-order derivatives) is not the best possible choice. We first show how to adjust greeks to take correlation into account and reduce P&L volatility. Then we embed correlation-adjusted greeks in a global hedging strategy that reduces cost of hedging without increasing P&L volatility, by optimization of hedge re-adjustments. The strategy is justified in terms of a balance between transaction costs and risk-aversion, but, unlike more complex proposals from previous literature, it is completely defined by observable parameters, geometrically intuitive, and easy to implement for an arbitrary number of risk factors. We test our findings on a CVA hedging example. We first consider daily re-hedging: in this test, correlation-adjusted greeks allow the reduction of P&L volatility by more than 30% compared to standard deltas. Then we apply our general strategy to a context where a CVA portfolio is exposed to both credit and interest rate risk. The strategy keeps P&L volatility in line with daily standard delta-hedging, but with massive cost-saving: only six rebalances of the illiquid credit hedge are performed, over a period of six months.     

Robust hedging with proportional transaction costs

A duality for robust hedging with proportional transaction costs of path-dependent European options is obtained in a discrete-time financial market with one risky asset. The investor’s portfolio consists of a dynamically traded stock and a static position in vanilla options, which can be exercised at maturity. Trading of both options and stock is subject to proportional transaction costs. The main theorem is a duality between hedging and a Monge–Kantorovich-type optimization problem. In this dual transport problem, the optimization is over all probability measures that satisfy an approximate martingale condition related to consistent price systems, in addition to an approximate marginal constraint.