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Margin requirements for derivative contracts serve as a buffer against the transmission of losses through the financial system by protecting one party to a contract against default by the other party. However, if margin levels are proportional to volatility, then a spike in volatility leads to potentially destabilizing margin calls in times of market stress. Risk-sensitive margin requirements are thus procyclical in the sense that they amplify shocks. We use a GARCH model of volatility and a combination of theoretical and empirical results to analyze how much higher margin levels need to be to avoid procyclicality while reducing counterparty credit risk. Our analysis compares the tail decay of conditional and unconditional loss distributions with comparable stable and risk-sensitive margin requirements. Greater persistence and burstiness in volatility leads to a slower decay in the tail of the unconditional distribution and a higher buffer needed to avoid procyclicality. The tail decay drives other measures of procyclicality as well. Our analysis points to important features of price time series that should inform “antiprocyclicality” measures but are missing from current rules. This paper was accepted by Noah Gans, stochastic models and simulation.

On-grid integration of renewable energy, also called “green power”, is attracting more and more attention nowadays. Green power futures can be effective in increasing returns to suppliers and increasing market liquidity. However, compared to traditional futures, green power feed-in tariffs may be subject to integrity problems due to lower power factors; therefore, existing margin calculation methods for the futures market are no longer applicable. A SPAN-based margin calculation method that considers the power factor risk is proposed in this paper. The method provides the classification policies of the green power futures, based on the historical power factors of green power suppliers, and allows the margin amount to be adjusted as per the classification. To verify the effectiveness of the proposed method, empirical validation is presented by applying actual transaction data. Results prove that the proposed method can reduce the margin amount while covering the risk effectively.

We model how leveraged trading activities constrained by dynamic funding availability affect financial stability. In the market, customers trade based on the fundamental value of the risky asset and make full payment for their transactions, while speculators take trading position based on margin, which is constantly adjusted by the financier, the fund provider, according to the price volatility. As a result of equilibrium price discontinuity triggered by dynamic margin requirements, trivial shocks to external supply, wealth or fundamental value can be transmitted into asset price crashes or jumps. We find that tightening margin requirements improves (mitigates) the market liquidity in the bull (bear) market, and that imposing short sale constraints helps prevent the price from falling further when the asset is sufficiently under-priced and accelerate price collapse when the asset is over-priced.

Price limits are artificial boundaries established by regulators to establish the maximum price movement permitted in a single day. We propose using a new censoring method that incorporates the effect of price limits on the futures price distribution and investigates how to set an appropriate daily margin level using single-stock futures in Taiwan. We compare our estimations with those obtained using the method in Longin (J Bus 69:383–408, 1999 ). The results show that (1) the margin levels derived from the Longin method, which ignore price limits in the estimation, are lower than those in our censoring method; and (2) the legal margin for single-stock futures set at 13.5 % by the Taiwan Futures Exchange to avoid default risk appears to be too high.

We provide a model that links an asset's market liquidity (i.e., the ease with which it is traded) and traders' funding liquidity (i.e., the ease with which they can obtain funding). Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and margin requirements, depends on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to \"flight to quality,\" and (v) co-moves with the market. The model provides new testable predictions, including that speculators' capital is a driver of market liquidity and risk premiums.

Between 1934 and 1974, the Federal Reserve changed the initial margin requirement for the U.S. stock market 22 times. I use this variation to show that investors' leverage constraints affect the pricing of risk. Consistent with earlier theoretical predictions, I find that tighter leverage constraints result in a flatter relation between betas and expected returns. My results provide strong empirical support for the idea that the constraints investors face may help explain the empirical failure of the capital asset pricing model.

In a model with heterogeneous-risk-aversion agents facing margin constraints, we show how securities' required returns increase in both their betas and their margin requirements. Negative shocks to fundamentals make margin constraints bind, lowering risk-free rates and raising Sharpe ratios of risky securities, especially for high-margin securities. Such a funding-liquidity crisis gives rise to \"bases,\" that is, price gaps between securities with identical cash-flows but different margins. In the time series, bases depend on the shadow cost of capital, which can be captured through the interest-rate spread between collateralized and uncollateralized loans and, in the cross-section, they depend on relative margins. We test the model empirically using the credit default swap—bond bases and other deviations from the Law of One Price, and use it to evaluate central banks' lending facilities.

A major credit shock can induce large intraday variation margin payments between counterparties in derivatives markets, which may force some participants to default on their payments. These payment shortfalls become amplified as they cascade through the network of exposures. Using detailed Depository Trust & Clearing Corporation data, we model the full network of exposures, shock-induced payments, initial margin collected, and liquidity buffers for about 900 firms operating in the U.S. credit default swaps market. We estimate the total amount of contagion, the marginal contribution of each firm to contagion, and the number of defaulting firms for a systemic shock to credit spreads. A novel feature of the model is that it allows for a range of behavioral responses to balance sheet stress, including delayed or partial payments. The model provides a framework for analyzing the relative effectiveness of different policy options, such as increasing margin requirements or mandating greater liquidity reserves. This paper was accepted by Karl Diether, finance.

The scientific community has recently seen a fast-growing number of publications tackling the topic of fractional-order controllers in general, with a focus on the fractional order PID. Several versions of this controller have been proposed, including different tuning methods and implementation possibilities. Quite a few recent papers discuss the practical use of such controllers. However, the industrial acceptance of these controllers is still far from being reached. Autotuning methods for such fractional order PIDs could possibly make them more appealing to industrial applications, as well. In this paper, the current autotuning methods for fractional order PIDs are reviewed. The focus is on the most recent findings. A comparison between several autotuning approaches is considered for various types of processes. Numerical examples are given to highlight the practicality of the methods that could be extended to simple industrial processes.

Corporate insiders frequently borrow from lending institutions and pledge their personal equity as collateral for the loan. This borrowing, or pledging, potentially affects shareholder risk through changing managerial incentives or contingency risk. Using an exogenous shock to lending supply, we document a significant increase in risk arising from pledging. Difference-in-differences regressions indicate that insider pledging corresponds with a 16.5% relative increase in risk despite unchanged firm fundamentals. The empirical analysis supports contingency risk in linking pledging to volatility. Overall, our findings suggest that pledging allows influential insiders to extract private benefits of control at the expense of outside shareholders.