War-time military service can affect partisan preferences

Does war-time military service affect partisan preferences? We argue that military service increases the salience and potential costs of war. Therefore, soldiers who serve during mismanaged wars will associate the ruling party with incompetence and be less likely to support the ruling party in the future. To test our argument, we analyze almost 50 years of Israel National Election Studies. Employing a regression discontinuity design, we show that compared with respondents who were too young to serve in the Yom Kippur war, respondents just old enough to serve report lower support for the Labor party well after the war ended. This effect is likely driven by soldiers’ unwillingness to support a party they associate with security incompetence. We further show that the negative effect of military service does not materialize in well-managed wars, contributing to the literature on the political consequences of war and attitude formation

Systemic Risk and Hedge Funds

Systemic risk is commonly used to describe the possibility of a series of correlated defaults among financial institutions---typically banks---that occur over a short period of time, often caused by a single major event. However, since the collapse of Long Term Capital Management in 1998, it has become clear that hedge funds are also involved in systemic risk exposures. The hedge-fund industry has a symbiotic relationship with the banking sector, and many banks now operate proprietary trading units that are organized much like hedge funds. As a result, the risk exposures of the hedge-fund industry may have a material impact on the banking sector, resulting in new sources of systemic risks. In this paper, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise.

Adaptive Markets and the New World Order

In the adaptive markets hypothesis (AMH) intelligent but fallible investors learn from and adapt to changing economic environments. This implies that markets are not always efficient but are usually competitive and adaptive, varying in their degree of efficiency as the environment and investor population change over time. The AMH has several implications, including the possibility of negative risk premiums, alpha converging to beta, and the importance of macro factors and risk budgeting in asset allocation policies

High frequency statistical arbitrage via the optimal thermal causal path

We consider the problem of identifying similarities and causality relationships in a given set of financial time series data streams. We develop further the “Optimal Thermal Causal Path” method, which is a non-parametric method proposed by Sornette et al. The method considers the mismatch between a given pair of time series in order to identify the expected minimum energy path lead-lag structure between the pair. Traders may find this a useful tool for directional trading, to spot arbitrage opportunities. We add a curvature energy term to the method and we propose an approximation technique to reduce the computational time. We apply the method and approximation technique on various market sectors of NYSE data and extract the highly correlated pairs of time series. We show how traders could exploit arbitrage opportunities by using the method

The Pitfalls of Central Clearing in the Presence of Systematic Risk

Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives

Dyamic Risk Exposure in Hedge Funds

Working paper, Dipartimento di Scienze Economiche, Università di Venezi

Calculating VaR for Hedge Funds

It is well known that hedge funds implement dynamic strategies; therefore, the exposure of hedge funds to various risk factors is nonlinear. In this chapter, we propose to analyze hedge fund tail event behavior conditional on nonlinearity in factor loadings. In particular, we calculate VaR for different hedge fund strategies conditional on different states of the market risk factor. Specifically, we are concentrating on dynamic risk factors that\ud are switching from a market regime or state that we call normal to two other regimes that could be identified as “crisis” and “bubble” and that are usually characterized, respectively, by (1) largely low returns and high volatility and (2) high returns. We are proposing a factor model that allows for regime switching in expected returns and volatilities and compare the VaR determined with this methodology with the other VaR approaches like\ud GARCH(1,1), IGARCH(1,1), and Cornish Fisher

Dynamic Risk Exposure in Hedge Funds

A regime-switching beta model is proposed to measure dynamic risk exposures of hedge funds to various risk factors during different market volatility conditions. Hedge fund exposures strongly depend on whether the equity market (S&P 500) is in the up, down, or tranquil regime. In the down-state of the market, when market volatility is high and returns are very low, S&P 500, Small–Large, Credit Spread, and VIX are common risk factors for most of the hedge fund strategies. This suggests that hedge fund exposures to the market, liquidity, credit, and volatility risks change depending on market conditions, and these risks are potentially common factors for the hedge fund industry in the down-state of the market