# Quant News

The latest research and news for quantitative traders

### Headlines:

Soros Helping to Turn Back Tide of Hedge-Fund Outflows in Europe (bloomberg.com)

CBOE confirms Bats acquisition (automatedtrader.net)

Hedge fund Tudor said to close Singapore trading desk (straitstimes.com)

Star trader Rokos eclipses his old Brevan Howard fund (ft.com)

Maker-Taker Under the Microscope (tradersmagazine.com)

### Trending academic research:

See also: Most popular SSRN papers

Volatility Risk abstract In this paper we consider the generation of implied volatility risk scenarios, with a special focus on swaption implied volatility smile, e.g., as modeled by a displaced SABR model.

The generation of implied volatility risk scenarios is much more demanding than other risk factors, like interest rates curve, since a volatility surface (or cube) usually involves more sophisticated model assumptions and restrictions (e.g., the no-arbitrage constrains).

The generation risk scenarios is related to the question specification of a well behaved interpolation scheme, e.g., how to interpolate smile curves of different maturities or tenors. The link between scenario generation and interpolation is given by the quest for "independent" risk factors, describing independent properties of the volatility curve (or surface or cube).

For swaption implied volatility smile, scenario generation and interpolation may be conducted in different coordinate frames, e.g., implied lognormal volatilities, implied normal volatilities, model parameters like SABR parameters}, implied volatilities offsets or properties like skew and smile.

In addition we may distinguish between the input values and calibrated values.

With respect to model parameters, we consider the displaced SABR models (and a variant of it). Using the analytic approximation of Berestycki, Busca and Florent we derive explizit transformations (formulas) to shape properties like skew and smile. We give some examples where a naive parameter interpolation generates unexpected and/or undesired results.

Hyperbolic Discounting and Life-Cycle Portfolio Choice abstract This paper studies how hyperbolic discounting affects stock market participation, asset allocation, and saving decisions over the life cycle in an economy with Epstein-Zin preferences. Hyperbolic discounting affects saving and portfolio decisions through at least two channels: (1) it lowers desired saving, which decreases financial wealth relative to future earnings; and (2) it lowers the incentive to pay a fixed cost to enter the stock market. We find that hyperbolic discounters accumulate less wealth relative to their geometric counterparts and that they participate in the stock market at a later age. Because they have lower levels of financial wealth relative to future earnings, hyperbolic discounters who do participate in the stock market tend to hold a higher share of equities, particularly in the retirement years. We find that increasing the elasticity of intertemporal substitution, hold- ing risk aversion constant, greatly magnifies the impact of hyperbolic discounting on all of the model’s decision rules and simulated levels of participation, allocation, and wealth. Finally, we introduce endogenous financial knowledge accumulation and find that hyperbolic discounting leads to lower financial literacy and inefficient stock market investment.

Liquidity-Risk-Premium Free Price Models of Government Bonds and Currency Forwards in Persistently Informed Markets abstract We introduce a situation in government bonds and currency forwards markets where all participants have their own private information and expectations. Informed dealers are so persistent that market maker has to map their expectations. Later we deduce that these fluctuating expectations impose price change, order flow imbalance and liquidity measures.

Gated Neural Networks for Option Pricing: Rationality by Design abstract We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model's predictions, and provides econometrically useful byproduct such as risk neutral density.

Collateralized Borrowing and Increasing Risk abstract This paper uses a general equilibrium model with collateralized borrowing to show that increases in risk can have ambiguous effects on leverage, loan margins, loan amounts, and asset prices. Increasing risk about future payoffs and endowments can lead to riskier loans with larger balances and lower spreads even when lenders are risk- averse and borrowers can default. As well, increasing the covariance of either agents’ endowments with the asset payoff can have ambiguous consequences for equilibrium. Though the effects are ambiguous, key determinants of how increased risk translate into changes in prices and allocations are the covariance of agents’ endowments with the asset payoff, agents’ risk aversion, and the location of increased risk in the distribution of future states. Some restricted changes in the borrower’s or lender’s endowments can have unambiguous but asymmetric effects on equilibrium.

Information Aggregation for Stock Return Predictability abstract The literature on stock return predictability has identified macroeconomic and technical predictors that when combined, leads to out-of-sample outperformance relative to the historical mean null. This paper investigates a new method for aggregating information beyond using forecast combination or principal components. By sequentially layering groups of information, the predictive performance of this new approach outperforms that of prior methods. Applying layering to volatility forecasting yields more mixed results. In all, a mean-variance investor investing in monthly stock returns gains from this new method as much as 4.5% per year.

The Drift Burst Hypothesis abstract The Drift Burst Hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent US equity and treasury flash crashes can be viewed as two high profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude are an expected and regular occurrence in financial markets that can arise through established mechanisms such as feedback trading. At a theoretical level, we show how to build drift bursts into the continuous-time Itô semi-martingale model in such a way that the fundamental arbitrage-free property is preserved. We then develop a non-parametric test statistic that allows for the identification of drift bursts from noisy high-frequency data. We apply this methodology to a comprehensive set of tick data and show that drift bursts form an integral part of the price dynamics across equities, fixed income, currencies and commodities. We find that the majority of identified drift bursts are accompanied by strong price reversals and these can therefore be regarded as “flash crashes” that span brief periods of severe market disruption without any material longer term price impacts.

The Curious Case of Negative Volatility abstract Investors mostly perceive their own portfolio as no more volatile than the market portfolio. Taking into account observed portfolio betas, this implies a belief in very low idiosyncratic portfolio volatility, which is even negative for a considerable fraction of the population. Possible explanations are extreme overconfidence in combination with a misunderstanding how market and portfolio volatility are related. The identified bias strongly contributes to underdiversification, as a belief in negative idiosyncratic volatility conceals the benefit of diversification.

The Relationship between VIX Futures Term Structure and S&P500 Returns abstract The current paper tests and documents the relationship between the term structure of VIX futures and the underlying equity returns. Furthermore, it investigates the signaling effects of VIX futures term structure in respect to future stock index movements. The objective of this empirical analysis is to verify if a steep upward-sloping term structure indicates a late phase of a bullish trend and conversely if an extreme negative term structure suggests an over-sold market, as certain market participants believe.

The empirical findings of this study suggest that there is a strong statistical significant positive contemporaneous relationship between the changes in the VIX futures term structure and the returns of the underlying equity index. Finally, the econometric analysis lends some support to the hypothesis that the term structure of VIX futures can be used as a contrarian indicator for investing in the equity market.

Optimal Mean-Reversion Strategy in the Presence of Bid-Ask Spread and Delays in Capital Allocations abstract We consider a portfolio optimization problem for an investor who trades T-bills and a mean-reverting stock in the presence of proportional transaction costs and delays in capital allocations. We find that proportional transaction costs have a relatively weak effect on the expected return and the Sharpe ratio of the investor's portfolio. Meantime, the presence of delays in capital allocations can have a dramatic impact on the expected return and the Sharpe ratio of investor's portfolio as long as the mean-reverting security offers affluent investment opportunities. We also find the robust optimal strategy in the presence of model uncertainty and show that the latter increases the effective risk aversion of the investor and slows the reversion of the stock price.

Capability Satisficing in High Frequency Trading

Risk Parity Portfolios with Skewness Risk: An Application to Factor Investing and Alternative Risk Premia abstract This article develops a model that takes into account skewness risk in risk parity portfolios. In this framework, asset returns are viewed as stochastic processes with jumps or random variables generated by a Gaussian mixture distribution. This dual representation allows us to show that skewness and jump risks are equivalent. As the mixture representation is simple, we obtain analytical formulas for computing asset risk contributions of a given portfolio. Therefore, we define risk budgeting portfolios and derive existence and uniqueness conditions. We then apply our model to the equity/bond/volatility asset mix policy. When assets exhibit jump risks like the short volatility strategy, we show that skewness-based risk parity portfolios produce better allocation than volatility-based risk parity portfolios. Finally, we illustrate how this model is suitable to manage the skewness risk of long-only equity factor portfolios and to allocate between alternative risk premia.

The Performance of Relative-Value Equity Strategies abstract This paper examines investment strategies that combine time-series and cross-sectional stock sorts based on scaled price-to-earnings ratios, which I describe as relative-value strategies. Relative-value strategies buy (short) stocks that are below (above) their long-run historical absolute or market-relative valuation. I test the performance of relative-value strategies versus a traditional value strategy. I find that a relative-value effect exists, but is dwarfed by the traditional value effect. On average, the tested relative-value strategies deliver long-short returns of 1.25% p.a. and long-only excess returns of 0.69% p.a. over 1990 to 2014. The traditional value strategy produces long-short returns of 5.50% p.a. and long-only excess returns of 4.33% p.a. Relative-value strategies offer an alternative but not necessarily superior source of returns. I evaluate returns after adjusting for common risk factors. I explore the effect on returns from alternative portfolio formation and rebalancing rules. I discuss possible explanations for the findings and implications for investors.

On the Value of Virtual Currencies abstract This paper develops an economic framework to analyze the exchange rate of virtual currency. Three components are important. First, the current use of virtual currency to make payments. Second, the decision of forward-looking investors to buy virtual currency (thereby effectively regulating its supply). Third, the elements that jointly drive future consumer adoption and merchant acceptance of virtual currency. The model predicts that, as virtual currency becomes more established, the exchange rate will become less sensitive to the impact of shocks to speculators’ beliefs. This undermines the notion that excessive exchange rate volatility will prohibit widespread use of virtual currency.

Data-driven nonlinear expectations for statistical uncertainty in decisions abstract In stochastic decision problems, one often wants to estimate the underlying probability measure statistically, and then to use this estimate as a basis for decisions. We shall consider how the uncertainty in this estimation can be explicitly and consistently incorporated in the valuation of decisions, using the theory of nonlinear expectations.

A Latent Mechanism for Generating Wealth Inequality in 401(k) Retirement Plans abstract The introduction of qualified retirement accounts, and in particular the direct contribution 401(k) plan in the late 1970’s, brought about an institutional shift in the relationship between private employers and workers with regard to the financial risk and decision making involved with their retirement savings and financial well-being in older age. Rather than having neutral effect, this shift has resulted in a number of documented problems such as the inclusion of compounding fees, problems with getting people to enroll in a plan based on employee demographic, and unfairness caused by their tax treatment. This paper identifies an otherwise unnoticed latent mechanism for generating inequality in 401(k) plans based solely on differences in how employees with different incomes (who have already elected to join a plan) self-select either riskier or less risky portfolios. Lower income employees, as a whole, allocate to less risky investments than higher income earners, which results in real differences in outcomes favoring the wealthier.

Costs of Corporate Bond Issue in Coal Mining Companies abstract In Poland, more than 90% of electricity production is based on coal fuel. Meanwhile, the financial situation of the mining industry is quite challenging. Companies in this sector are in debt, generating losses caused by a sharp drop in coal prices and a simultaneous increase in extraction costs that result from descending into lower levels of coal deposits. At the same time, banks are reluctant to loan money because of the risk of a borrowing entity’s default. Increasingly, companies are turning to bond issue to maintain their liquidity and finance development projects. However, bondholders impose conditions in the form of covenants that are often difficult to satisfy, and the strictest relate to the level of a company’s indebtedness and ability-to-repay-debt financial ratios. This article discusses bond issue costs. The authors analyze the bond issue programs of three of the four mining companies operating in Poland. The fourth company did not issue any bonds. Bond issue costs are composed of interest payable to investors, issue preparation and support costs, collateralization costs, and the cost of recording and organizing the sale. The main cost involved in bond issuance is the coupon cost, which depends on the company’s financial health and its level of indebtedness, the purpose of the issue, its volume, and the type and quality of the safeguards against the risk of loss of funds invested by bondholders. Bonds issued by coal mining companies are assumed mainly by banks, which demand high interest due to the poor financial condition of the issuers. In addition to interest, companies also pay a capital commitment fee, an arrangement fee and a fee for early redemption. Altogether, in relation to the costs of capital raised through a bank loan, the bond issue results in significantly higher costs of raising capital.

Multivariate GARCH for a large number of stocks abstract The problems related to the application of multivariate GARCH models to a market with a large number of stocks are solved by restricting the form of the conditional covariance matrix. It contains one component describing the market and a second simple component to account for the remaining contribution to the volatility. This allows the analytical calculation of the inverse covariance matrix. We compare our model with the results of other GARCH models for the daily returns from the S&P500 market. The description of the covariance matrix turns out to be similar to the DCC model but has fewer free parameters and requires less computing time. As applications we use the daily values of $\beta$ coefficients available from the market component to confirm a transition of the market in 2006. Further we discuss properties of the leverage effect.

Innovations and Stock Price Crash Risk abstract We study the influence of the innovations, proxied by the number of patent grants and citations as well as the R&D investments on the stock price crash risk. Using a large sample of U.S. firms, we show that the innovation related activities reduce the likelihood to experience future stock price crashes with significant magnitude. This finding is consistent with the argument that the innovations facilitate information dissemination and decrease the probability of hoarding negative news. Further, we find that such influence on stock price crash risk is more pronounced for firms with weak external monitoring mechanism and high information asymmetry.

Low-Latency Trading and Price Discovery: Evidence from the Tokyo Stock Exchange in the Pre-Opening and Opening Periods abstract We study whether the presence of low-latency traders (including high-frequency traders (HFTs)) in the pre-opening period contributes to market quality, defined by price discovery and liquidity provision, in the opening auction. We use a unique dataset from the Tokyo Stock Exchange (TSE) based on server-IDs and find that HFTs dynamically alter their presence in different stocks and on different days. In spite of the lack of immediate execution, about one quarter of HFTs participate in the pre-opening period, and contribute significantly to market quality in the pre-opening period, the opening auction that ensues and the continuous trading period. Their contribution is largely different from that of the other HFTs during the continuous period.

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