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on Market Microstructure |
| By: | Assa Cohen; Mahyar Kargar; Benjamin Lester; Pierre-Olivier Weill |
| Abstract: | We develop a search-theoretic model of a dealer-intermediated over-the-counter market. Our key departure from the literature is to assume that, when a customer meets a dealer, the dealer can sell only assets that it already owns. Hence, in equilibrium, dealers choose to hold inventory. We derive the equilibrium relationship between dealers’ costs of holding assets on their balance sheets, their optimal inventory holdings, and various measures of liquidity, including bid-ask spreads, trade size, volume, and turnover. Using transaction-level data from the corporate bond market, we calibrate the model to quantitatively assess the impact of post-crisis regulations on dealers’ inventory costs, liquidity, and welfare. |
| Keywords: | Over-the-counter markets; intermediation; liquidity; dealer inventory; financial regulation |
| JEL: | G11 G12 G21 |
| Date: | 2024–12–09 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:99245 |
| By: | Munipalle, Pravith |
| Abstract: | Bot trading, or algorithmic trading, has transformed modern financial markets by using advanced technologies like artificial intelligence and machine learning to execute trades with unparalleled speed and efficiency. This paper examines the mechanisms and types of trading bots, their impact on market liquidity, efficiency, and stability, and the ethical and regulatory challenges they pose. Key findings highlight the dual nature of bot trading—enhancing market performance while introducing systemic risks, such as those observed during the 2010 Flash Crash. Emerging technologies like blockchain and predictive analytics, along with advancements in AI, present opportunities for innovation but also underscore the need for robust regulations and ethical design. To provide deeper insights, we conducted an experiment analyzing the performance of different trading bot strategies in simulated market conditions, revealing the potential and pitfalls of these systems under varying scenarios. |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:p98zv |
| By: | Sung Hoon Choi; Donggyu Kim (Department of Economics, University of California Riverside) |
| Abstract: | In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Itˆo semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank conditional expected instantaneous volatility and noise matrices. To predict the low-rank conditional expected instantaneous volatility matrix, we propose the Two-sIde Projected-PCA (TIP-PCA) procedure. We establish asymptotic properties of the proposed estimators and conduct a simulation study to assess the finite sample performance of the proposed prediction method. Finally, we apply the TIP-PCA method to an out-of-sample instantaneous volatility vector prediction study using high-frequency data from the S&P 500 index and 11 sector index funds. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202423 |
| By: | Donggyu Kim (Department of Economics, University of California Riverside); Minseog Oh; Yazhen Wang |
| Abstract: | In this paper, we develop a robust non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noise, which is robust to the stylized features, such as the time-varying beta and the price-dependent and autocorrelated microstructure noise. With this robust realized integrated beta estimator, we investigate dynamic structures of integrated betas and find a persistent autoregressive structure. To model this dynamic structure, we utilize the autoregressivemoving-average (ARMA) model for daily integrated market betas. We call this the dynamic realized beta (DR Beta). Then, we propose a quasi-likelihood procedure for estimating the parameters of the ARMA model with the robust realized integrated beta estimator as the proxy. We establish asymptotic theorems for the proposed estimator and conduct a simulation study to check the performance of finite samples of the estimator. The proposed DR Beta model with the robust realized beta estimator is also illustrated by using data from the E-mini S&P 500 index futures and the top 50 large trading volume stocks from the S&P 500 and an application to constructing market-neutral portfolios. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202422 |
| By: | Carlo Zarattini (CONCRETUM RESEARCH); Andrew Aziz (Peak Capital Trading; Bear Bull Traders); Andrea Barbon (University of St. Gallen; University of St.Gallen) |
| Abstract: | This paper investigates the profitability of a simple, yet effective intraday momentum strategy applied to SPY, one of the most liquid ETFs that tracks the S&P 500. Unlike the academic literature that typically limits trading to the last 30 minutes of the trading session, our model initiates trend-following positions as soon as there is an indication of abnormal demand/supply imbalance in the intraday price action. Building on trading techniques commonly used by active day traders, which have been discussed in our previous papers, we introduce the use of dynamic trailing stops to mitigate downside risks while allowing for unlimited upside potential. From 2007 to early 2024, the resulting intraday momentum portfolio achieved a total return of 1, 985% (net of costs), an annualized return of 19.6%, and a Sharpe Ratio of 1.33. We conduct extensive statistical tests to examine whether the profitability of the strategy is affected by different market volatility regimes and whether the estimated gamma imbalance of dealers could predict changes in strategy profitability. We analyze the daily profitability of the intraday momentum strategy with respect to day-of-the-week effects. Additionally, we evaluate its performance against well-known technical daily patterns to understand its behavior under various market conditions. Given the short-term nature of the model, we also assess the impact of commissions and slippage on the overall profitability of the strategy. |
| Keywords: | Day Trading, Day Trading Systems, Algo Trading, Momentum, Trend-Following, Intraday Momentum, Delta-Hedging |
| JEL: | C00 C10 C50 G00 G11 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2497 |
| By: | Briac Turquet (ETH Zurich); Pierre Bajgrowicz (Axpo Solutions AG); O. Scaillet (Swiss Finance Institute - University of Geneva) |
| Abstract: | We investigate the mean reversion of the naphtha crack after large price moves on daily data over 2014-2024. Our non-parametric estimation of the dynamics of daily changes assuming a univariate diffusion process shows that the reversion strength increases non-linearly after daily moves exceeding a certain threshold. We perform Monte Carlo simulations to study the duration for which the reversion is likely to remain active. We then backtest corresponding trading strategies. We calibrate parameters of the strategy using grid search while controlling for multiple testing. On average the tested strategies deliver positive returns after transaction costs. We are able to select a subset of outperforming strategies generating robust positive net returns. The existence of positive returns can be explained by differences in liquidity, execution speed, and categories of participants in the naphtha and Brent markets constituting the two legs of the naphtha crack. |
| Keywords: | oil derivatives, naptha crack, statistical arbitrage, mean reversion |
| JEL: | G13 G14 G15 G17 G18 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp24101 |
| By: | Jianqing Fan; Donggyu Kim (Department of Economics, University of California Riverside); Minseok Shin; Yazhen Wang |
| Abstract: | This paper introduces a novel Ito diffusion process for both factor and idiosyncratic volatilities whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor and idiosyncratic VAR-Ito (FIVAR-Ito) model. The FIVAR-Ito model considers dynamics of the factor and idiosyncratic volatilities and involve many parameters. In addition, the empirical studies have shown that the financial returns often exhibit heavy tails. To address these two issues simultaneously, we propose a penalized optimization procedure with a truncation scheme for a parameter estimation. We apply the proposed parameter estimation procedure to predicting large volatility matrices and investigate its asymptotic properties. Using high-frequency trading data, the proposed method is applied to large volatility matrix prediction and minimum variance portfolio allocation. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202415 |
| By: | Christopher J. Neely |
| Abstract: | Presidential elections create uncertainty about future economic policy that translates into volatility in asset prices. How has the VIX performed around U.S. elections since 1988? |
| Keywords: | asset price volatility; stock market; stock market volatility; presidential elections |
| Date: | 2024–12–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:99209 |