Liquidity and market makers: a pseudo-experimental analysis with ultrahigh frequency data

An analysis is given of the effect of market makers on liquidity using a transaction-level database. For this purpose, the focus is on a financial market where a change in regulations created explicitly the category of market maker in 1997 and that date is used to construct a pseudo-experiment. In contrast with other studies that use ultrahigh frequency data, the days to be analysed are selected using a statistical procedure to match observations before and after the change in regulation. The propensity score is used to perform the matching. After choosing the days, an estimate of an ordered probit model is made to explain the intraday behaviour of price changes. The coefficient estimates from the ordered probit model are used to calculate a measure of liquidity based on the steepness of the response function of price changes to volume. The results show that liquidity, measured in this way, has not been affected by the introduction of the market makers.     

Predicting issuer credit ratings using a semiparametric method

This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model.     

Intraday price reversals in the US stock index futures market: A 15-year study

This paper gives a long-term assessment of intraday price reversals in the US stock index futures market following large price changes at the market open. We find highly significant intraday price reversals over a 15-year period (November 1987–September 2002) as well as significant intraday reversals in our yearly and day-of-the-week investigations. Moreover, the strength of the intraday overreaction phenomenon seems more pronounced following large positive price changes at the market open. That being said, the question of whether a trader can consistently profit from this information remains open as the significance of intraday price reversals is sharply reduced when gross trading results are adjusted by a bid–ask proxy for transactions costs.     

A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio.     

Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD–ARMA–EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.     

A random effects ordered probit model for rating migrations

Employing a random effects ordered probit model, this paper examines the sources of heterogeneity in sovereign credit ratings in emerging economies. The analysis uses data from six rating agencies for 90 countries. The model highlights the importance of considering the cross-section error, which captures country-specific heterogeneity, in modelling rating upgrades. Watchlist status is a powerful tool in predicting future rating upgrades/downgrades, and dominates rating momentum in some cases. Rating duration and existing rating are important determinants of rating migrations. Evidence of inter-agency differences and dissimilar behaviour of upgrades and downgrades is presented.     

Dynamics of analysts' coverage and the firms' information environment

The main goal of this paper is to study analysts' coverage of stocks. Through a series of ordered probit regressions the paper studies the relationship between changes in coverage and the information environment of a firm.Coverage decreases on average with higher errors in estimation. The data also shows that coverage is less likely to decrease for physically large firms, but more likely to decrease for firms with high lagged market value. Higher past revisions to the predictions also decrease coverage, showing a real cost of uncertainty.     

Index arbitrage with heterogeneous investors: A smooth transition error correction analysis

The traditional index arbitrage model assumes a constant threshold mispricing between the futures and cash prices for all investors. Allowing for heterogeneity in investors' transaction costs, objectives, and capital constraints, we model the intraday mispricing of DJIA futures as a smooth transition autoregressive (STAR) process with the speed of adjustment toward equilibrium varying directly with the mispricing. We show that the observed mean reversion in mispricing changes is induced by heterogeneous arbitrageurs, instead of a statistical illusion-infrequent trading of index portfolio stocks. We further use a STAR error correction model to describe the nonlinear dynamics between the DJIA futures and index. This model describes not only which market is more informationally efficient than the other, but also the legging process – the nonsimultaneous establishing of cash and futures positions.     

Credit rating model development: An ordered analysis based on accounting data

In this paper we propose and test a methodology for constructing a credit rating model. We follow a polytomous ordered probit analysis leading to the specification of statistically significant credit rating intervals. We test our model with accounting data of Greek listed firms over the years 2004–2013, a period which includes both the pre-crisis growth and the crisis phase of the Greek economy and the stock market. Using the empirically—based rating categories that the model generates endogenously, we observe not only a clear and timely response of ratings to the changing economic environment, but we also obtain significant predictive ability over a period of one, two and three years.     

Increased presence of foreign investors and dividend policy of Japanese firms

This paper investigates the impact of the increased presence of foreign investors on the dividend policy of Japanese firms. A choice-to-pay model, estimated with a random-effects binary probit method, shows that a higher level of foreign ownership is associated with a significantly higher probability of dividend payouts. A choice-to-change model, estimated with a random-effects generalized ordered probit method, shows that a higher level of foreign ownership is associated with a significantly higher (lower) probability of an increase (no change) in dividends, while a larger 1-year increase is associated with a significantly higher (lower) probability of an increase (decrease).     

Bank-firm relationship and credit risk: An analysis on Tunisian firms

This paper analyses the impact of the intensity and length of bank-firm lending relationship on Tunisian banks’ credit risk over the period 2001–2012. The sample includes 494 bank-firm relationships for 383 firms. By applying probit and ordered probit models, our results indicate that firms which engage in intense relationships with banks are less likely to encounter a credit default. In addition, these firms exhibit a higher loan quality. However, no evidence has been found for the impact of the relationship length on credit risk. Further, the findings show that private banks, unlike public financial institutions, take advantage of their close lending relationships with borrowers to mitigate information asymmetry and therefore improve their loans portfolio quality.     

Intraday Return Volatility Process: Evidence from NASDAQ Stocks

This paper presents a comprehensive analysis of the distributional and time-series properties of intraday returns. The purpose is to determine whether a GARCH model that allows for time varying variance in a process can adequately represent intraday return volatility. Our primary data set consists of 5-minute returns, trading volumes, and bid-ask spreads during the period January 1, 1999 through March 31, 1999, for a subset of thirty stocks from the NASDAQ 100 Index. Our results indicate that the GARCH(1,1) model best describes the volatility of intraday returns. Current volatility can be explained by past volatility that tends to persist over time. These results are consistent with those of Akgiray (1989) who estimates volatility using the various ARCH and GARCH specifications and finds the GARCH(1,1) model performs the best. We add volume as an additional explanatory variable in the GARCH model to examine if volume can capture the GARCH effects. Consistent with results of Najand and Yung (1991) and Foster (1995) and contrary to those of Lamoureux and Lastrapes (1990), our results show that the persistence in volatility remains in intraday return series even after volume is included in the model as an explanatory variable. We then substitute bid-ask spread for volume in the conditional volatility equation to examine if the latter can capture the GARCH effects. The results show that the GARCH effects remain strongly significant for many of the securities after the introduction of bid-ask spread. Consistent with results of Antoniou, Homes and Priestley (1998), intraday returns also exhibit significant asymmetric responses of volatility to flow of information into the market.     

A duscrete-time model of high-frequency stock returns

A model of intraday financial time series is developed. The model is a dynamic factor model consisting of two equations. First, a rate of return of a ‘stock’ in a single day is assumed to be generated by serveral common factors plus some additive erros (‘intraday equation’). Secondly, the joint distribution of those common factors is assumed to depend on the hidden state of the day, which fluctuates according to a Markov chain (‘day-by-day equation’). Together the equations compose a hidden Markov model.

We investigate properties of the model. Among them is a central limit theorem for cumulative returns, which agrees with the well-known empirical phenomenon in the stock markets that the distributions of longer-horizon returns are closer to the normal. We propose a two-step procedure consisting of the method of principal components and the EM algorithm to estimate the model parameters as well as the unboservable states. In addition, we propose a procedure for predicting intraday returns. Finally, the model is fitted to empirical data, the Standard&Poors 500 Index 5 min return data, to see if the model is capable of describing intraday movements of the index.     

Why Does the Interest Rate Decline Over the Day? Evidence from the Liquidity Crisis

We provide a simple model, able to explain why the overnight (ON) rate follows a downward intraday pattern, implicitly creating a positive intraday interest rate. While this normally reflects only some frictions, a liquidity crisis introduces a new component: the chance of an upward jump of the ON rate, which must be compensated by an intraday decline of the ON rate. By analyzing real time data for the e-MID interbank market, we show that the intraday rate has increased from a negligible level to a significant one after the start of the liquidity crisis in August 2007, and even more so since September 2008. The intraday rate is affected by the likelihood of a dry-up of the ON market, proxied by the 3M Euribor—Eonia swap spread. This evidence supports our model and it shows that a liquidity crisis impairs the ability of central banks to curb the market price of intraday liquidity, even by providing free daylight overdrafts. Such results have implications for the efficiency of the money market and of payment systems, as well as for the operational framework of central banks.     

A noise-robust estimator of volatility based on interquantile ranges

This paper proposes a new class of estimators based on the interquantile range of intraday returns, referred to as interquantile range based volatility (IQRBV), to estimate the integrated daily volatility. More importantly and intuitively, it is shown that a properly chosen IQRBV is jump-free for its trimming of the intraday extreme two tails that utilize the range between symmetric quantiles. We exploit its approximation optimality by examining a general class of distributions from the Pearson type IV family and recommend using IQRBV^.04 as the integrated variance estimate. Both our simulation and the empirical results highlight interesting features of the easy-to-implement and model-free IQRBV over the other competing estimators that are seen in the literature.     

Implicit intraday interest rate in the UK unsecured overnight money market

This paper estimates the intraday value of money implicit in the UK unsecured overnight money market. Using transactions data on overnight loans advanced through the UK large value payments system CHAPS in 2003–2009, we find a positive and economically significant intraday interest rate. While the implicit intraday interest rate is quite small pre-crisis, it increases more than tenfold during the financial crisis of 2007–2009. The key interpretation is that an increase in implicit intraday interest rate reflects the increased opportunity cost of pledging collateral intraday and can be used as an indicator to gauge the stress of the payment system. We obtain qualitatively similar estimates of the intraday interest rate by using quoted intraday bid and offer rates and confirm that our results are not driven by the intraday variation in the bid-ask spread.     

Intraday risk management in International stock markets: A conditional EVT approach

The study compares the predictive ability of various models in estimating intraday Value-at-Risk (VaR) and Expected Shortfall (ES) using high frequency share price index data from sixteen different countries across the world for a period of seven and half months from September 20, 2013 to May 07, 2014. The main emphasis of the study has been given to Extreme Value Theory (EVT) and to evaluate how well Conditional EVT model performs in modeling tails of distributions and in estimating and forecasting intraday VaR and ES measures. We have followed McNeil and Frey's (2000) two stage approach called Conditional EVT to estimate dynamic intraday VaR and ES. We have compared the accuracy of Conditional EVT approach to intraday VaR and ES estimation with other competing models. The best performing model is found to be the Conditional EVT in estimating both the quantiles for the entire sample. The study is useful for market participants (such as intraday traders and market makers) involved in frequent intraday trading in such equity markets.     

Does retirement planning affect the level of retirement satisfaction?

This paper analyzes the relationship between retirement planning and retirement satisfaction. Do individuals think about and plan for retirement? If they do, do they utilize financial planning services? If they plan, are they more satisfied with retirement than those who did not? Data for 1,781 retired individuals from the first wave of the Health and Retirement Study (HRS) are analyzed using an ordered probit model. The results indicate that thinking about retirement and attending planning meetings have a significant positive impact on satisfaction even when income, wealth, marital status and health are included as explanatory variables.     

Modelling the monetary policy reaction function of the Colombian Central Bank

This paper proposes a simple ordered probit model to analyse the monetary policy reaction function of the Colombian Central Bank. There is evidence that the reaction function is asymmetric, in the sense that the Bank increases the Bank rate when the gap between observed inflation and the inflation target (lagged once) is positive, but it does not reduce the Bank rate when the gap is negative. This behaviour suggests that the Bank is more interested in fulfilling the announced inflation target rather than in reducing inflation excessively. The forecasting performance of the model, both within and beyond the estimation period, appears to be particularly good.     

A cyclical model of exchange rate volatility

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to 1 year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the range-based EGARCH and FIEGARCH models of Brandt and Jones (2006). Not only does the cyclical volatility model provide a very substantial computational advantage over the EGARCH and FIEGARCH models, but it also offers an improvement in out-of-sample forecast performance.