Fractals in trade duration: capturing long-range dependence and heavy tailedness in modeling trade duration

Several studies that have investigated a few stocks have found that the spacing between consecutive financial transactions (referred to as trade duration) tend to exhibit long-range dependence, heavy tailedness, and clustering. In this study, we empirically investigate whether a larger sample of stocks exhibit those characteristics. We do so by comparing goodness of fit in modeling trade duration data for stable distribution and fractional stable noise based on a procedure applying bootstrap methods developed by the authors with several alternative distributional assumptions in modeling trade duration data. The empirical results suggest that the autoregressive conditional duration model with stable distribution fits better than other combinations, while fractional stable noise itself fits better for the time series of trade duration. Our result is consistent with the general findings in the literature that trade duration is informative and that short trade durations move prices more than long trade duration. In addition, our result confirms the advantage of fractal models in the study of roughness in trade duration and provides some evidence for duration dependence. S. Rachev’s research was supported by grants from the Division of Mathematical, Life and Physical Science, College of Letters and Science, University of California, Santa Barbara, and the Deutschen Forschungsgemeinschaft. W. Sun’s research was supported by grants from the Deutschen Forschungsgemeinschaft. P.S. Kalev’s research was supported with a NCG grant from the Faculty of Business and Economics, Monash University. Data are supplied by Securities Industry Research Center of Asia-Pacific (SIRCA) on behalf of Reuters. The first draft of this paper was presented at the International Conference on High Frequency Finance 2006; the authors would like to thank the conference participants for their valuable comments.     

Managing the risk of loan prepayments and the optimal structure of short term lending rates

Loan pricing is an extremely important aspect of bank operations because loans are typically over two-thirds of bank assets. Many researchers have analyzed the theoretical and empirical impact of how different factors should and do affect fixed rate loan rates and loan prepayments. However, a theoretical decision making model for maximizing expected profit in a declining rate environment has not been developed. After describing the conditions for the optimal loan rate, we develop numerical solutions for it under varying conditions. The varying conditions include the trend in interest rates, volatility of interest rates, and loan maturity. We thank Yen Low and Hamed Bagherpour for their assistance.     

Existence of Lévy term structure models

Lévy driven term structure models have become an important subject in the mathematical finance literature. This paper provides a comprehensive analysis of the Lévy driven Heath–Jarrow–Morton type term structure equation. This includes a full proof of existence and uniqueness in particular, which seems to have been lacking in the finance literature so far.      

Liquidity and market incompleteness

This note shows that according to Lippman and McCall’s (Am Econ Rev 76, 43–55, 1986) operational definition of liquidity, incomplete markets are a necessary condition for illiquidity. This note is a revised subset of a larger paper that circulated under the name of “Liquidity as an Insurance Problem” (Zurita 2001). I am grateful to Luis Ahumada, David K. Levine, Raimundo Soto, Gert Wagner, Federico Weinschelbaum and seminar participants at UCLA, Summer Meeting of the Econometric Society, Banco Central de Chile, LACEA, Jornadas de Economía del Banco Central del Uruguay, and ILADES, for their helpful comments, as well as the feedback of an anonymous referee. Financial support from Vicerrectoría Académica de la Pontificia Universidad Católica de Chile is gratefully acknowledged.     

Relationship disconnect in retail banking

It has been established that increasing the role of technology in a service organisation can serve to reduce costs and improve service reliability. It is argued, however, that there remains an important role for personalised relationships in the delivery of any service proposition. Throughout this paper both of these perspectives will be discussed and an attempt is made to reconcile these apparently opposing views in the emerging age of technology-enabled remote relationships. Findings from a longitudinal research study with a large UK case bank that focused on the area of remote relationships are revisited and reinterpreted with management from the case bank in question. Through this reflective lens the bank's recent strategic decision to no longer proactively relationship manage its personal customer base is examined. In light of this deliberate relationship disconnection strategy key marketing implications are discussed.     

Investor reactions to derivative use and outcomes

How do investors evaluate managers who choose whether or not to use derivatives once the outcomes of those decisions become known? Different theories offer different predictions, and we test these in three experiments. Results show that investors are more satisfied with firm managers and assign a higher value to firms when managers use derivatives (that address firm risks) than when they do not. This result occurs even though we hold constant the economic differences typically present when comparing derivative use versus non-use (that is, ex ante risk and ex post outcome), suggesting that investors reward firms that use derivatives. Additional tests reveal that investors believe that managers who use derivatives in these situations exhibit a higher level of decision-making care than those who do not use derivatives. We also document that these inferences about greater decision-making care do not apply to the speculative use of derivatives. Overall, our study adds to our understanding of how investors judge companies that use derivatives, given the resulting outcomes of such use.     

Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.     

I. Neuerscheinungen versicherungswissenschaftlicher Bücher

Ohne Zusammenfassung Zusammengestellt von Inga Krebs     

Banking competition and welfare

We develop a simple general equilibrium model in which investment in a risky technology is subject to moral hazard and banks can extract market power rents. We show that more bank competition results in lower economy-wide risk, higher social welfare, lower bank capital ratios, more efficient production plans and Pareto-ranked real allocations. Perfect competition supports a second best allocation and optimal levels of bank risk and capitalization. These results are at variance with those obtained by a large literature that has studied a similar environment in partial equilibrium, they are empirically relevant, and carry significant implications for policy guidance.     

Risk-based forecasting and planning and management earnings forecasts

This study examines the association between a firm’s internal information environment and the accuracy of its externally disclosed management earnings forecasts. Internally, firms use forecasts to plan for uncertain futures. The risk management literature argues that integrating risk-related information into forecasts and plans can improve a firm’s ability to forecast financial outcomes. We investigate whether this internal information manifests itself in the accuracy of external earnings guidance. Using detailed survey data and publicly disclosed management earnings forecasts from a sample of publicly traded U.S. companies, we find that more sophisticated risk-based forecasting and planning processes are associated with smaller earnings forecast errors and narrower forecast widths. These associations hold across a variety of different planning horizons (ranging from annual budgeting to long-term strategic planning), providing empirical support for the theoretical link between internal information quality and the quality of external disclosures.