Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

金融行业对实体行业的尾部风险传导研究——基于实体企业金融化视角

本文基于我国金融资本超额回报率的事实,在市场套利分析框架下实证检验了实 体企业金融化是抑制还是加剧了尾部金融风险溢出。数值模拟结果表明：从杠杆率来看,实体 企业金融化对杠杆率具有“双刃剑”效应,然而,实证结果发现,从长期经济后果来看,实体 企业金融化却显著提高了杠杆率,基于Altman-Z值的风险分析进一步发现企业破产风险显著 上升,从而加剧了尾部金融风险溢出。文章的研究结论有利于全面观察实体企业金融化带来的 的实际效果,也揭示了金融行业对实体行业的风险传导过程中的一个风险源,对于实体经济和 金融领域关于企业金融化效应的研究具有参考价值。     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Bailout uncertainty in a microfounded general equilibrium model of the financial system

This paper develops a micro-founded general equilibrium model of the financial system composed of ultimate borrowers, ultimate lenders and financial intermediaries. The model is used to investigate the impact of uncertainty about the likelihood of governmental bailouts on leverage, interest rates, the volume of defaults and the real economy. The distinction between risk and uncertainty is implemented by applying the multiple priors framework to beliefs about the probability of bailout.Results of the analysis include: (i) An unanticipated increase in bailout uncertainty raises interest rates, the volume of defaults in both the real and financial sectors and may lead to a total drying up of credit markets. (ii) Lower exante bailout uncertainty is conducive to higher leverage, which in turn raises moral hazard and makes the economy more vulnerable to expost increases in bailout uncertainty. (iii) Bailout uncertainty affects the likelihood of bubbles, the amplitude of booms and busts as well as the banking and the credit spreads. (iv) Higher bailout uncertainty is associated with higher returns’ variability in diversified portfolios and higher systemic risks, (v) Pre-crisis expansionary monetary policy reinforces those effects by inducing higher aggregate leverage levels. (vi) The larger the change in bailout uncertainty and the change in aversion to this uncertainty, the stronger the pre-crisis buildup and the deeper the ensuing crisis.A central policy implication of the analysis is that the vaguest is bailout policy prior to a crisis, the lower is the magnitude of investments destroyed or missed due to errors in evaluating bailout and other intervention policies. On the other hand, the clearer is bailout policy upon the eruption of a crisis, the smaller the contraction of credit and the destruction of investment activity.     

The composite iteration algorithm for finding efficient and financially fair risk-sharing rules

We consider the problem of finding an efficient and fair ex-ante rule for division of an uncertain monetary outcome among a finite number of von Neumann–Morgenstern agents. Efficiency is understood here, as usual, in the sense of Pareto efficiency subject to the feasibility constraint. Fairness is defined as financial fairness with respect to a predetermined pricing functional. We show that efficient and financially fair allocation rules are in one-to-one correspondence with positive eigenvectors of a nonlinear homogeneous and monotone mapping associated to the risk sharing problem. We establish relevant properties of this mapping. On the basis of this, we obtain a proof of existence and uniqueness of solutions via nonlinear Perron–Frobenius theory, as well as a proof of global convergence of the natural iterative algorithm. We argue that this algorithm is computationally attractive, and discuss its rate of convergence.     

The Effects of Productivity Shocks,Financial Shocks,and Monetary Policy on Exchange Rates: An Application of the Currency Crisis Model and Implications for Emerging Market Crises

What kind of shock affects exchange rate dynamics? How much of an effect does the monetary policy have on exchange rates? To answer these questions empirically based on the currency crisis model, I use panel data on 51 emerging countries from 1980 to 2011, identify shocks, and apply instrumental variable methods. I found that both productivity shocks and shocks to a country’s risk premium affect exchange rates and a 1 percentage point increase in the policy interest rate is associated with a 1 percentage point appreciation of domestic currency. I further apply this method to Asian and Latin-American crises.     

Semiparametric estimation of multi-asset portfolio tail risk

When correlations between assets turn positive, multi-asset portfolios can become riskier than single assets. This article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets. The estimator captures simultaneously the information contained in each individual asset return that composes the portfolio, and the interrelation between assets. Noticeably, the accuracy of the estimates does not deteriorate when the number of assets in the portfolio increases. The implementation is as easy for a large number of assets as it is for a small number. We estimate the probability distribution of large losses for the American stock market considering portfolios with ten, fifty and one hundred assets of stocks with different market capitalization. In either case, the approximation for the portfolio tail risk is very accurate. We compare our results with well known benchmark models.     

Cost-benefit analysis of airport security: Are airports too safe?

This paper assesses the risks and cost-effectiveness of measures designed to further protect airport terminals and associated facilities such as car parks from terrorist attack in the U.S., Europe, and the Asia-Pacific area. The analysis considers threat likelihood, the cost of security measures, hazard likelihood, risk reduction and expected losses to compare the costs and benefits of security measures to decide the optimal security measures to airports. Monte-Carlo simulation methods were used to propagate hazard likelihood, risk reduction and loss uncertainties in the calculation of net benefits that also allows probability of cost-effectiveness to be calculated. It is found that attack probabilities had to be much higher than currently observed to justify additional protective measures. Overall, then, it is questionable whether special efforts to further protect airports are sensible expenditures. Indeed, some relaxation of the measures already in place may well be justified.     

Risk regulation,community pressure and the use of management accounting in managing climate change risk: Australian evidence

This exploratory study is amongst the first to investigate how companies perceive the regulation of carbon emissions and the pressure exerted by the community in an environment characterised by risk and uncertainty. Semi-structured interviews were conducted among 39 executives who were directly involved in carbon emissions management in 18 large listed Australian companies. Consistent with Prospect Theory, we find that decision-makers are threat biased and are more likely to take immediate actions when climate change issues are framed as threats as opposed to opportunities. From the interview data, it is seen that managers use management accounting techniques as a risk management tool in mitigating risks associated climate change issues. Furthermore, this use of management accounting appears to be driven primarily by the protection of economic interests, regulatory pressure and reputational pressure. The study provides insights into how perceptions of climate change uncertainties and external pressure for disclosure of emissions information influence companies to use management accounting in managing climate change risk.     

REAL OPTIONS AND THE CROSS‐SECTION OF EXPECTED STOCK RETURNS

This paper surveys the theoretical literature investigating the effect of firms’ investment flexibility on the cross‐section of expected stock returns. Real options analysis derives firms’ value‐maximizing investment policies as functions of exogenous fundamental drivers of profitability and calculates firms’ market values as functions of the same variables. These functions yield the relationship between expected stock returns and firm fundamentals. Several plausible explanations for the value premium – the high average stock returns earned by firms with high book‐to‐market ratios – emerge from this literature.