**4. Determinants of currency and banking crises**

Following the models to identify currency and banking crises that have been described in the previous section, many empirical studies have been made to find the determinants of the currency and banking crises.

There are two popular methodologies to investigate crises. First, the multivariate probit/logit model is arguably the most popular methodology to analyse currency and banking crises [41]. This model uses the event of crisis as a dummy dependent variable with a value of one if there is a crisis and a value of zero if there is no crisis. As *Currency and Banking Crises: The Origins and How to Identify Them DOI: http://dx.doi.org/10.5772/intechopen.107245*

independent variables, a set of macroeconomic indicators is used. These binary models are occasionally also combined with the panel method when investigating a large sample of countries. One advantage of the method is that asymmetries and other non-linearities can be straightforwardly tested [42]. However, despite their popularity, binary models fail to provide useful forecasts [43].

The second strand of literature uses a non-parametric methodology to predict the currency and banking crises. One commonly used non-parametric methodology to examine currency crises is the signalling method [33]. After the crises are identified, the threshold of each variable is determined. A signal is flared when a variable exceeds a given threshold level. The variables are then investigated to calculate the correct signal, missing signal, wrong signal, or correctly do not produce a signal. Noise-to-Signal Ratio is then used to understand the ability of variables to predict systemic banking crises. As a lower Noise-to-Signal Ratio represents a low frequency of false signals, thus, the threshold level can be adjusted to find the lowest Noise-to-Signal Ratio (**Table 1**).

Noise-to-Signal Ratio can be obtained by the following formula:

$$\text{Noise} - \text{to} - \text{SignalRatio} = \frac{\frac{B}{(B+D)}}{\frac{A}{(A+C)}} \tag{17}$$

The signalling method is considered the most successful method to forecast financial crises [43]. However, the signalling method has one main drawback. It evaluates the variables individually. Thus, we need to create a composite index to measure the result. However, it is difficult to interpret the index as it is highly variable [44].

Furthermore, the most recent study employs innovative techniques such as Markov switching models [45], artificial neural networks and genetic algorithms [46], and binary recursive trees [47].

In general, the above methodologies find that the currency and banking crises are typically preceded by a real appreciation and a lending boom [35, 48]. Those two variables are signs of a boom period in the business cycle.

In a boom period, the economy typically enjoys high growth, high export and large capital inflow. High capital flow is usually dominated by hot money which is invested in portfolio instruments such as stocks and bonds. Thus, stock and bond prices start to increase [49]. On the other hand, these also lead to a real appreciation of currency [50, 51].

If real appreciation continues, exporters start to lose competitiveness which leads to decreasing export, increasing imports and a current account deficit. On the other hand, overvalued currency also provides an incentive for investors to attack the currency. Thus, the economy fundamentally becomes fragile.

Funded by capital flows, banks start pushing their lending, leading to a significant increase in speculative financing. On the other hand, to avoid the adverse effect of real appreciation, the central bank starts to intervene by buying foreign currency and


**Table 1.** *The classification of signals.* selling domestic currency. Both foreign reserves [52] and domestic money supply increase. Abundant liquidity encourages banks to push their lending and creates a lending boom. The bank's liquidity ratio starts to decrease, and the banking system becomes weaker.

Current account deficit pressures currency to depreciate. If foreign investors start pulling out their money, the currency depreciates faster, along with the fall of asset prices [53]. Furthermore, liquidity becomes tight, and interest rates are increasing.

Soon, firms and households will have difficulty paying the loan. Current account deficit and high non-performing loans will lead to banking crises and massive capital outflow. As a result, the currency will crash.

As fast currency depreciation is devastating, thus, the central bank tries to intervene to smooth the volatility (in the free float rate regime) or to defend the currency (in the fixed-rate regime). The success of the central bank's intervention depends on two things: the amount of foreign reserve and the amount of domestic liquidity. Even though the central bank collects sufficient foreign reserves during a boom period, the intervention may fail if there is not enough domestic currency in the market to be bought (**Figure 3**).

The above relationship also shows that there is a physical link between the currency crisis and the banking crisis, as there is a vicious cycle in economic activities. The growth may invite capital flows. However, the capital flow also stimulates growth. On the other hand, the currency attack encourages investors to withdraw their money to fund the attack. Thus, the bank run is inevitable. However, in the event of a bank run, many investors reinvest their fresh cash speculatively in foreign currency. Thus, the banking and currency crises reinforce each other in a vicious cycle. The vicious cycle suggests that the initial crisis in the twin currency and banking crisis is hard to determine.

In an empirical study, the lending boom is often represented as financial sector indicators (e.g. M2 multiplier, domestic credit/GDP, real interest rate), and real appreciation is often represented as external sector indicators (e.g. export, term of trade, real exchange rate, import, international reserve).

**Figure 3.** *The cycle of currency and banking crises.*
