Identifying financial crises in real time

Following the thermodynamic formulation of a multifractal measure that was shown to enable the detection of large fluctuations at an early stage, here we propose a new index which permits us to distinguish events like financial crises in real time. We calculate the partition function from which we can obtain thermodynamic quantities analogous to the free energy and specific heat. The index is defined as the normalized energy variation and it can be used to study the behavior of stochastic time series, such as financial market daily data. Famous financial market crashes-Black Thursday (1929), Black Monday (1987) and the subprime crisis (2008)-are identified with clear and robust results. The method is also applied to the market fluctuations of 2011. From these results it appears as if the apparent crisis of 2011 is of a different nature to the other three. We also show that the analysis has forecasting capabilities. © 2012 Elsevier B.V. All rights reserved.

Brazilian exchange rate complexity: Financial crisis effects

With the financial market globalization, foreign investments became vital for the economies, mainly in emerging countries. In the last decades, Brazilian exchange rates appeared as a good indicator to measure either investors' confidence or risk aversion. Here, some events of global or national financial crisis are analyzed, trying to understand how they influenced the "dollar-real" rate evolution. The theoretical tool to be used is the Lopez-Mancini-Calbet (LMC) complexity measure that, applied to real exchange rate data, has shown good fitness between critical events and measured patterns. (C) 2011 Elsevier B.V. All rights reserved.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Determinants and Effects of Maturity Mismatches in Emerging Markets: Evidence from Bank Level Data

Despite the extensive work on currency mismatches, research on the determinants and effects of maturity mismatches is scarce. In this paper I show that emerging market maturity mismatches are negatively affected by capital inflows and price volatilities. Furthermore, I find that banks with low maturity mismatches are more profitable during crisis periods but less profitable otherwise. The later result implies that banks face a tradeoff between higher returns and risk, hence channeling short term capital into long term loans is caused by cronyism and implicit guarantees rather than the depth of the financial market. The positive relationship between maturity mismatches and price volatility, on the other hand, shows that the banks of countries with high exchange rate and interest rate volatilities can not, or choose not to hedge themselves. These results follow from a panel regression on a data set I constructed by merging bank level data with aggregate data. This is advantageous over traditional studies which focus only on aggregate data.

The impact of financial development and market conditions on investment-cash flow sensitivity: global evidence

In this study, we analyze the impact of financial development and market conditions on investment-cash flow sensitivity during the 2006-2014 for 76 countries. First, the results show a relationship between investment-cash flow sensitivity and an index of financial development and its components. Second, 68 countries are affected by the 2008-2009 financial crisis, but only 16 countries exhibit a higher investment-cash flow sensitivity during the crisis. Third, investment-cash flow sensitivity is lower in countries with a larger primary debt market, while the size of the primary equity market has no impact. Finally, analyzing investment-cash flow sensitivity over time, we find lower sensitivity during years associated with higher primary debt market activity.

Macroeconomic Dynamics of Market Risk Capital Requirements and Credit Supply Interdependence

Thesis (Ph.D.)--University of Washington, 2016-06

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Testing for financial crashes using the Log Periodic Power Law model

Many papers claim that a Log Periodic Power Law (LPPL) model fitted to financial market bubbles that precede large market falls or 'crashes', contains parameters that are confined within certain ranges. Further, it is claimed that the underlying model is based on influence percolation and a martingale condition. This paper examines these claims and their validity for capturing large price falls in the Hang Seng stock market index over the period 1970 to 2008. The fitted LPPLs have parameter values within the ranges specified post hoc by Johansen and Sornette (2001) for only seven of these 11 crashes. Interestingly, the LPPL fit could have predicted the substantial fall in the Hang Seng index during the recent global downturn. Overall, the mechanism posited as underlying the LPPL model does not do so, and the data used to support the fit of the LPPL model to bubbles does so only partially. © 2013.

Three essays on market depth in futures markets

Liquidity is an important market characteristic for participants in every financial market. One of the three components of liquidity is market depth. Prior literature lacks a comprehensive analysis of depth in U.S. futures markets due to past limitations on the availability of data. However, recent innovations in data collection and dissemination provide new opportunities to investigate the depth dimension of liquidity. In this dissertation, the Chicago Mercantile Exchange (CME) Group proprietary database on depth is employed to study the dynamics of depth in the U.S. futures markets. This database allows for the analysis of depth along the entire limit order book rather than just at the first level. The first essay examines the characteristics of depth within the context of the five-deep limit order book. Results show that a large amount of depth is present in the book beyond the best level. Furthermore, the findings show that the characteristics of five-deep depth between day and night trading vary and that depth is unequal across levels within the limit order book. The second essay examines the link between the five-deep market depth and the bid-ask spread. The results suggest an inverse relation between the spread and the depth after adjusting for control factors. The third essay explores transitory volatility in relation to depth in the limit order book. Evidence supports the relation between an increase in volatility and a subsequent decrease in market depth. Overall, the results of this dissertation are consistent with limit order traders actively managing depth along the limit order book in electronic U.S. futures markets.

Analysis of the Bovespa Futures and Spot Indexes With High Frequency Data. Chinese Business Review

Data from the World Federation of Exchanges show that Brazil’s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built: ARFIMA, vector autoregressive (VAR), and vector error correction (VEC). Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.

Does financial news predict stock returns? New evidence from Islamic and Non-Islamic stocks

The paper extends the time-series financial news data set constructed by Garcia (2013) and uses it to examine whether financial news predicts returns of Islamic stocks differently compared to non-Islamic (conventional) stocks. We find that they do. First, while both positive and negative worded news predict most Islamic and conventional stock returns, positive words have a larger impact on both types of stock returns. Second, shock to returns from financial news reverses only in part for some stocks. Third, for a mean-variance investor, investing in Islamic stocks is relatively more profitable than investing in the corresponding conventional stocks. Fourth, we show that profits are robust to a range of time-series risk factors, namely, market risk, size-based risk, and momentum-induced risk.

Three Essays on Market Depth in Futures Markets

Liquidity is an important market characteristic for participants in every financial market. One of the three components of liquidity is market depth. Prior literature lacks a comprehensive analysis of depth in U.S. futures markets due to past limitations on the availability of data. However, recent innovations in data collection and dissemination provide new opportunities to investigate the depth dimension of liquidity. In this dissertation, the Chicago Mercantile Exchange (CME) Group proprietary database on depth is employed to study the dynamics of depth in the U.S. futures markets. This database allows for the analysis of depth along the entire limit order book rather than just at the first level. The first essay examines the characteristics of depth within the context of the five-deep limit order book. Results show that a large amount of depth is present in the book beyond the best level. Furthermore, the findings show that the characteristics of five-deep depth between day and night trading vary and that depth is unequal across levels within the limit order book. The second essay examines the link between the five-deep market depth and the bid-ask spread. The results suggest an inverse relation between the spread and the depth after adjusting for control factors. The third essay explores transitory volatility in relation to depth in the limit order book. Evidence supports the relation between an increase in volatility and a subsequent decrease in market depth. Overall, the results of this dissertation are consistent with limit order traders actively managing depth along the limit order book in electronic U.S. futures markets.

A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an upto- date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.

A hybrid approach to combining CART and logistic regression for stock ranking

The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])...

Comparing and complementing methods : traditional market research vs. deep customer insights

Quantitative market data has traditionally been used throughout marketing and business as a tool to inform and direct design decisions. However, in our changing economic climate, businesses need to innovate and create products their customers will love. Deep customer insight methods move beyond just questioning customers and aims to provoke true emotional responses in order to reveal new opportunities that go beyond functional product requirements. This paper explores traditional market research methods and compares them to methods used to gain deep customer insights. This study reports on a collaborative research project with seven small to medium enterprises and four multi-national organisations. Firms were introduced to a design led innovation approach, and were taught the different methods to gain deep customer insights. Interviews were conducted to understand the experience and outcomes of pre-existing research methods and deep customer insight approaches. Findings concluded that deep customer insights were unlikely to be revealed through traditional market research techniques. The theoretical outcome of this study is a complementary methods matrix, providing guidance on appropriate research methods in accordance to a project’s timeline.